ICPR2020 Paper Browser

Paper download is intended for registered attendees only, and is subjected to the IEEE Copyright Policy. Any other use is strongly forbidden.

Seasonal Inhomogeneous Nonconsecutive Arrival Process Search and Evaluation

Kimberly Holmgren, Paul Gibby, Joseph Zipkin

Auto-TLDR; SINAPSE: Fitting a Sparse Time Series Model to Seasonal Data

Abstract Slides Poster

Time series often exhibit seasonal patterns, and identification of these patterns is essential to understanding the data and predicting future behavior. Most methods train on large datasets and can fail to predict far past the training data. This limitation becomes more pronounced when data is sparse. This paper presents a method to fit a model to seasonal time series data that maintains predictive power when data is limited. This method, called \textit{SINAPSE}, combines statistical model fitting with an information criteria to search for disjoint, and possibly nonconsecutive, regimes underlying the data, allowing for a sparse representation resistant to overfitting.

Similar papers

3CS Algorithm for Efficient Gaussian Process Model Retrieval

Fabian Berns, Kjeld Schmidt, Ingolf Bracht, Christian Beecks

Auto-TLDR; Efficient retrieval of Gaussian Process Models for large-scale data using divide-&-conquer-based approach

Abstract Slides Poster Similar

Gaussian Process Models (GPMs) have been applied for various pattern recognition tasks due to their analytical tractability, ability to quantify uncertainty for their own results as well as to subsume prominent other regression techniques. Despite these promising prospects their super-quadratic computation time complexity for model selection and evaluation impedes its broader application for more than a few thousand data points. Although there have been many proposals towards Gaussian Processes for large-scale data, those only offer a linearly scaling improvement to a cubical scaling problem. In particular, solutions like the Nystrom approximation or sparse matrices are only taking fractions of the given data into account and subsequently lead to inaccurate models. In this paper, we thus propose a divide-&-conquer-based approach, that allows to efficiently retrieve GPMs for large-scale data. The resulting model is composed of independent pattern representations for non-overlapping segments of the given data and consequently reduces computation time significantly. Our performance analysis indicates that our proposal is able to outperform state-of-the-art algorithms for GPM retrieval with respect to the qualities of efficiency and accuracy.

Temporal Pattern Detection in Time-Varying Graphical Models

Federico Tomasi, Veronica Tozzo, Annalisa Barla

Auto-TLDR; A dynamical network inference model that leverages on kernels to consider general temporal patterns

Abstract Slides Poster Similar

Graphical models allow to describe the interplay among variables of a system through a compact representation, suitable when relations evolve over time. For example, in a biological setting, genes interact differently depending on external environmental or metabolic factors. To incorporate this dynamics a viable strategy is to estimate a sequence of temporally related graphs assuming similarity among samples in different time points. While adjacent time points may direct the analysis towards a robust estimate of the underlying graph, the resulting model will not incorporate long-term or recurrent temporal relationships. In this work we propose a dynamical network inference model that leverages on kernels to consider general temporal patterns (such as circadian rhythms or seasonality). We show how our approach may also be exploited when the recurrent patterns are unknown, by coupling the network inference with a clustering procedure that detects possibly non-consecutive similar networks. Such clusters are then used to build similarity kernels. The convexity of the functional is determined by whether we impose or infer the kernel. In the first case, the optimisation algorithm exploits efficiently proximity operators with closed-form solutions. In the other case, we resort to an alternating minimisation procedure which jointly learns the temporal kernel and the underlying network. Extensive analysis on synthetic data shows the efficacy of our models compared to state-of-the-art methods. Finally, we applied our approach on two real-world applications to show how considering long-term patterns is fundamental to have insights on the behaviour of a complex system.

PIF: Anomaly detection via preference embedding

Filippo Leveni, Luca Magri, Giacomo Boracchi, Cesare Alippi

Auto-TLDR; PIF: Anomaly Detection with Preference Embedding for Structured Patterns

Abstract Slides Poster Similar

We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantages of adaptive isolation methods with the flexibility of preference embedding. Specifically, we propose to embed the data in a high dimensional space where an efficient tree-based method, PI-FOREST, is employed to compute an anomaly score. Experiments on synthetic and real datasets demonstrate that PIF favorably compares with state-of-the-art anomaly detection techniques, and confirm that PI-FOREST is better at measuring arbitrary distances and isolate points in the preference space.

Video Analytics Gait Trend Measurement for Fall Prevention and Health Monitoring

Lawrence O'Gorman, Xinyi Liu, Md Imran Sarker, Mariofanna Milanova

Auto-TLDR; Towards Health Monitoring of Gait with Deep Learning

Abstract Slides Poster Similar

We design a video analytics system to measure gait over time and detect trend and outliers in the data. The purpose is for health monitoring, the thesis being that trend especially can lead to early detection of declining health and be used to prevent accidents such as falls in the elderly. We use the OpenPose deep learning tool for recognizing the back and neck angle features of walking people, and measure speed as well. Trend and outlier statistics are calculated upon time series of these features. A challenge in this work is lack of testing data of decaying gait. We first designed experiments to measure consistency of the system on a healthy population, then analytically altered this real data to simulate gait decay. Results on about 4000 gait samples of 50 people over 3 months showed good separation of healthy gait subjects from those with trend or outliers, and furthermore the trend measurement was able to detect subtle decay in gait not easily discerned by the human eye.

Location Prediction in Real Homes of Older Adults based on K-Means in Low-Resolution Depth Videos

Simon Simonsson, Flávia Dias Casagrande, Evi Zouganeli

Auto-TLDR; Semi-supervised Learning for Location Recognition and Prediction in Smart Homes using Depth Video Cameras

Abstract Slides Poster Similar

In this paper we propose a novel method for location recognition and prediction in smart homes based on semi-supervised learning. We use data collected from low-resolution depth video cameras installed in four apartments with older adults over 70 years of age, and collected during a period of one to seven weeks. The location of the person in the depth images is detected by a person detection algorithm adapted from YOLO (You Only Look Once). The locations extracted from the videos are then clustered using K-means clustering. Sequence prediction algorithms are used to predict the next cluster (location) based on the previous clusters (locations). The accuracy of predicting the next location is up to 91%, a significant improvement compared to the case where binary sensors are placed in the apartment based on human intuition. The paper presents an analysis on the effect of the memory length (i.e. the number of previous clusters used to predict the next one), and on the amount of recorded data required to converge.

Partial Monotone Dependence

Denis Khryashchev, Huy Vo, Robert Haralick

Auto-TLDR; Partially Monotone Autoregressive Correlation for Time Series Forecasting

Abstract Slides Similar

We present a new measure of dependence suitable for time series forecasting: Partially Monotone Correlation (PMC) that generalizes Monotone Correlation. Unlike Monotone Correlation, the new measure of dependence uses piece-wise strictly monotone transformations that increase the value of the correlation coefficient. We explore its properties, its relationship with Monotone and Maximal Correlation, and present an algorithm that calculates it based on the Simultaneous Perturbation Stochastic Approximation method. We also demonstrate how to apply Partially Monotone Correlation for time series analysis and forecasting introducing Partially Monotone Autoregressive model (PMAR) of order 1. Its performance is then evaluated against the baseline of linear and non-linear autoregressive models (AR, LSTAR) on 150 time series produced from 3 datasets: Yellow Taxi pickups, Citi Bike pickups, and Cellular Network hits. Overall, PMAR model outperforms the baseline with the average sMAPE of about 1.7-4% lower.

GPSRL: Learning Semi-Parametric Bayesian Survival Rule Lists from Heterogeneous Patient Data

Ameer Hamza Shakur, Xiaoning Qian, Zhangyang Wang, Bobak Mortazavi, Shuai Huang

Auto-TLDR; Semi-parametric Bayesian Survival Rule List Model for Heterogeneous Survival Data

Abstract Slides Similar

Survival data is often collected in medical applications from a heterogeneous population of patients. While in the past, popular survival models focused on modeling the average effect of the co-variates on survival outcomes, rapidly advancing sensing and information technologies have provided opportunities to further model the heterogeneity of the population as well as the non-linearity of the survival risk. With this motivation, we propose a new semi-parametric Bayesian Survival Rule List model in this paper. Our model derives a rule-based decision-making approach, while within the regime defined by each rule, survival risk is modelled via a Gaussian process latent variable model. Markov Chain Monte Carlo with a nested Laplace approximation for the latent variable model is used to search over the posterior of the rule lists efficiently. The use of ordered rule lists enables us to model heterogeneity while keeping the model complexity in check. Performance evaluations on a synthetic heterogeneous survival dataset and a real world sepsis survival dataset demonstrate the effectiveness of our model.

The eXPose Approach to Crosslier Detection

Antonio Barata, Frank Takes, Hendrik Van Den Herik, Cor Veenman

Auto-TLDR; EXPose: Crosslier Detection Based on Supervised Category Modeling

Abstract Slides Poster Similar

Transit of wasteful materials within the European Union is highly regulated through a system of permits. Waste processing costs vary greatly depending on the waste category of a permit. Therefore, companies may have a financial incentive to allege transporting waste with erroneous categorisation. Our goal is to assist inspectors in selecting potentially manipulated permits for further investigation, making their task more effective and efficient. Due to data limitations, a supervised learning approach based on historical cases is not possible. Standard unsupervised approaches, such as outlier detection and data quality-assurance techniques, are not suited since we are interested in targeting non-random modifications in both category and category-correlated features. For this purpose we (1) introduce the concept of crosslier: an anomalous instance of a category which lies across other categories; (2) propose eXPose: a novel approach to crosslier detection based on supervised category modelling; and (3) present the crosslier diagram: a visualisation tool specifically designed for domain experts to easily assess crossliers. We compare eXPose against traditional outlier detection methods in various benchmark datasets with synthetic crossliers and show the superior performance of our method in targeting these instances.

Emerging Relation Network and Task Embedding for Multi-Task Regression Problems

Schreiber Jens, Bernhard Sick

Auto-TLDR; A Comparative Study of Multi-Task Learning for Non-linear Time Series Problems

Abstract Slides Poster Similar

Multi-Task learning (MTL) provides state-of-the-art results in many applications of computer vision and natural language processing. In contrast to single-task learning (STL), MTL allows for leveraging knowledge between related tasks improving prediction results on all tasks. However, there is a limited number of comparative studies applied to MTL architectures for regression and time series problems taking recent advances of MTL into account. An intriguing, non-linear time-series problem are day ahead forecasts of the expected power generation for renewable power plants. Therefore, the main contribution of this article is a comparative study of the following recent and relevant MTL architectures: Hard-parameter sharing, cross-stitch network, and sluice network (SN). They are compared to a multi-layer peceptron (MLP) model of similar size in an STL setting. As a additional contribution, we provide a simple, yet practical approach to model task specific information through an embedding layer in an MLP, referred to as task embedding. Further, we contribute a new MTL architecture named emerging relation network (ERN), which can be considered as an extension of the SN. For a solar power dataset, the task embedding achieves the best mean improvement with 8.2%. For two wind and one additional solar dataset, the ERN is the best MTL architecture with improvements up to 11.3%.

A Framework for Local Outlier Detection from Spatio-Temporal Trajectory Datasets

Xumin Cai, Berkay Aydin, Anli Ji, Rafal Angryk

Auto-TLDR; Local Outlier Detection Using Spatial and Temporal Features of Trajectory Segment Using Time-Based partition strategy

Abstract Slides Similar

As one of the primary tasks in data mining, outlierdetection serves a significant role in data quality enhancementfor the scientific model prediction and revealing the abnormalhidden patterns from large scale trajectory datasets. In this paper,we introduce a versatile framework for detecting local trajectoryoutliers using spatial and temporal features of moving objects.Our local outlier detection consists of three phases. In the firstphase, we divide the raw trajectory into trajectory segments byusing a time-based partition strategy and extracting trajectoryfeatures from spatial attributes for each trajectory segment. Inthe second phase, we create template trajectory segments basedon a clustering schema. In the final phase, we compute theabnormal score, which measures the dissimilarity among thequery and template trajectory segments, and thus determine theoutlying trajectory segments according to the overall distributionof abnormal score. To show the effectiveness of our approach, weconduct two case studies on the real-life solar active region andCoronal Mass Ejection (CME) trajectory datasets. Our resultsshow that our local outlier detection method can successfullydetect the reporting errors and anomalous phenomenon in bothof our case studies.

Evaluation of Anomaly Detection Algorithms for the Real-World Applications

Marija Ivanovska, Domen Tabernik, Danijel Skocaj, Janez Pers

Auto-TLDR; Evaluating Anomaly Detection Algorithms for Practical Applications

Abstract Slides Poster Similar

Anomaly detection in complex data structures is oneof the most challenging problems in computer vision. In manyreal-world problems, for example in the quality control in modernmanufacturing, the anomalous samples are usually rare, resultingin (highly) imbalanced datasets. However, in current researchpractice, these scenarios are rarely modeled, and as a conse-quence, evaluation of anomaly detection algorithms often do notreproduce results that are useful for practical applications. First,even in case of highly unbalanced input data, anomaly detectionalgorithms are expected to significantly reduce the proportionof anomalous samples, detecting ”almost all” anomalous samples(with exact specifications depending on the target customer). Thisplaces high importance on only the small part of the ROC curve,possibly rendering the standard metrics such as AUC (AreaUnder Curve) and AP (Average Precision) useless. Second, thetarget of automatic anomaly detection in practical applicationsis significant reduction in manual work required, and standardmetrics are poor predictor of this feature. Finally, the evaluationmay produce erratic results for different randomly initializedtraining runs of the neural network, producing evaluation resultsthat may not reproduce well in practice. In this paper, we presentan evaluation methodology that avoids these pitfalls.

Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting

Tanwi Mallick, Prasanna Balaprakash, Eric Rask, Jane Macfarlane

Auto-TLDR; Transfer Learning for Highway Traffic Forecasting on Unseen Traffic Networks

Abstract Slides Poster Similar

Large-scale highway traffic forecasting approaches are critical for intelligent transportation systems. Recently, deep-learning-based traffic forecasting methods have emerged as promising approaches for a wide range of traffic forecasting tasks. However, these methods are specific to a given traffic network and consequently, they cannot be used for forecasting traffic on an unseen traffic network. Previous work has identified diffusion convolutional recurrent neural network (DCRNN), as a state-of-the-art method for highway traffic forecasting. It models the complex spatial and temporal dynamics of a highway network using a graph-based diffusion convolution operation within a recurrent neural network. Currently, DCRNN cannot perform transfer learning because it learns location-specific traffic patterns, which cannot be used for unseen regions of a network or new geographic locations. To that end, we develop TL-DCRNN, a new transfer learning approach for DCRNN, where a single model trained on a highway network can be used to forecast traffic on unseen highway networks. Given a traffic network with a large amount of traffic data, our approach consists of partitioning the traffic network into a number of subgraphs and using a new training scheme that utilizes subgraphs for the DCRNN to marginalize the location-specific information, thus learning the traffic as a function of network connectivity and temporal patterns alone. The resulting trained model can be used to forecast traffic on unseen networks. We demonstrate that TL-DCRNN can learn from San Francisco regional traffic data and forecast traffic on the Los Angeles region and vice versa.

Aggregating Dependent Gaussian Experts in Local Approximation

Hamed Jalali, Gjergji Kasneci

Auto-TLDR; A novel approach for aggregating the Gaussian experts by detecting strong violations of conditional independence

Abstract Slides Poster Similar

Distributed Gaussian processes (DGPs) are prominent local approximation methods to scale Gaussian processes (GPs) to large datasets. Instead of a global estimation, they train local experts by dividing the training set into subsets, thus reducing the time complexity. This strategy is based on the conditional independence assumption, which basically means that there is a perfect diversity between the local experts. In practice, however, this assumption is often violated, and the aggregation of experts leads to sub-optimal and inconsistent solutions. In this paper, we propose a novel approach for aggregating the Gaussian experts by detecting strong violations of conditional independence. The dependency between experts is determined by using a Gaussian graphical model, which yields the precision matrix. The precision matrix encodes conditional dependencies between experts and is used to detect strongly dependent experts and construct an improved aggregation. Using both synthetic and real datasets, our experimental evaluations illustrate that our new method outperforms other state-of-the-art (SOTA) DGP approaches while being substantially more time-efficient than SOTA approaches, which build on independent experts.

Detecting Anomalies from Video-Sequences: A Novel Descriptor

Giulia Orrù, Davide Ghiani, Maura Pintor, Gian Luca Marcialis, Fabio Roli

Auto-TLDR; Trit-based Measurement of Group Dynamics for Crowd Behavior Analysis and Anomaly Detection

Abstract Slides Poster Similar

We present a novel descriptor for crowd behavior analysis and anomaly detection. The goal is to measure by appropriate patterns the speed of formation and disintegration of groups in the crowd. This descriptor is inspired by the concept of one-dimensional local binary patterns: in our case, such patterns depend on the number of group observed in a time window. An appropriate measurement unit, named "trit" (trinary digit), represents three possible dynamic states of groups on a certain frame. Our hypothesis is that abrupt variations of the groups' number may be due to an anomalous event that can be accordingly detected, by translating these variations on temporal trit-based sequence of strings which are significantly different from the one describing the "no-anomaly" one. Due to the peculiarity of the rationale behind this work, relying on the number of groups, three different methods of people group's extraction are compared. Experiments are carried out on the Motion-Emotion benchmark data set. Reported results point out in which cases the trit-based measurement of group dynamics allows us to detect the anomaly. Besides the promising performance of our approach, we show how it is correlated with the anomaly typology and the camera's perspective to the crowd's flow (frontal, lateral).

Automatic Detection of Stationary Waves in the Venus’ Atmosphere Using Deep Generative Models

Minori Narita, Daiki Kimura, Takeshi Imamura

Auto-TLDR; Anomaly Detection of Large Bow-shaped Structures on the Venus Clouds using Variational Auto-encoder and Attention Maps

Abstract Slides Poster Similar

Various anomaly detection methods utilizing different types of images have recently been proposed. However, anomaly detection in the field of planetary science is still done predominantly by the human eye because explainability is crucial in the physical sciences and most of today's anomaly detection methods based on deep learning cannot offer enough. Moreover, preparing a large number of images required for fully utilizing anomaly detection is not always feasible. In this work, we propose a new framework that automatically detects large bow-shaped structures~(stationary waves) appearing on the surface of the Venus clouds by applying a variational auto-encoder~(VAE) and attention maps to anomaly detection. We also discuss the advantages of using image augmentation. Experiments show that our approach can achieve higher accuracy than the state-of-the-art methods even when the anomaly images are scarce. On the basis of this finding, we discuss anomaly detection frameworks particularly suited to physical science domains.

An Invariance-Guided Stability Criterion for Time Series Clustering Validation

Florent Forest, Alex Mourer, Mustapha Lebbah, Hanane Azzag, Jérôme Lacaille

Auto-TLDR; An invariance-guided method for clustering model selection in time series data

Abstract Slides Poster Similar

Time series clustering is a challenging task due to the specificities of this type of data. Temporal correlation and invariance to transformations such as shifting, warping or noise prevent the use of standard data mining methods. Time series clustering has been mostly studied under the angle of finding efficient algorithms and distance metrics adapted to the specific nature of time series data. Much less attention has been devoted to the general problem of model selection. Clustering stability has emerged as a universal and model-agnostic principle for clustering model selection. This principle can be stated as follows: an algorithm should find a structure in the data that is resilient to perturbation by sampling or noise. We propose to apply stability analysis to time series by leveraging prior knowledge on the nature and invariances of the data. These invariances determine the perturbation process used to assess stability. Based on a recently introduced criterion combining between-cluster and within-cluster stability, we propose an invariance-guided method for model selection, applicable to a wide range of clustering algorithms. Experiments conducted on artificial and benchmark data sets demonstrate the ability of our criterion to discover structure and select the correct number of clusters, whenever data invariances are known beforehand.

Algorithm Recommendation for Data Streams

Jáder Martins Camboim De Sá, Andre Luis Debiaso Rossi, Gustavo Enrique De Almeida Prado Alves Batista, Luís Paulo Faina Garcia

Auto-TLDR; Meta-Learning for Algorithm Selection in Time-Changing Data Streams

Abstract Slides Poster Similar

In the last decades, many companies are taking advantage of massive data generation at high frequencies through knowledge discovery to identify valuable information. Machine learning techniques can be employed for knowledge discovery, since they are able to extract patterns from data and induce models to predict future events. However, dynamic and evolving environments generate streams of data that usually are non-stationary. Models induced in these scenarios may perish over time due to seasonality or concept drift. The periodic retraining could help but the fixed algorithm's hypothesis space could no longer be appropriate. An alternative solution is to use meta-learning for periodic algorithm selection in time-changing environments, choosing the bias that best suits the current data. In this paper, we present an enhanced framework for data streams algorithm selection based on MetaStream. Our approach uses meta-learning and incremental learning to actively select the best algorithm for the current concept in a time-changing. Different from previous works, a set of cutting edge meta-features and an incremental learning approach in the meta-level based on LightGBM are used. The results show that this new strategy can improve the recommendation of the best algorithm more accurately in time-changing data.

SDMA: Saliency Driven Mutual Cross Attention for Multi-Variate Time Series

Yash Garg, K. Selcuk Candan

Auto-TLDR; Salient-Driven Mutual Cross Attention for Intelligent Time Series Analytics

Abstract Slides Poster Similar

Integration of rich sensory technologies into critical applications, such as gesture recognition and building energy optimization, has highlighted the importance of intelligent time series analytics. To accommodate this demand, uni-variate approaches have been extended for multi-variate scenarios, but naive extensions have lead to deterioration in model performances due to their limited ability to capture the information recorded in different variates and complex multi-variate time series patterns’ evolution over time. Furthermore, real-world time series are often contaminated with noisy information. In this paper, we note that a time series often carry robust localized temporal events that could help improve model performance by highlighting the relevant information; however, the lack of sufficient data to train for these events make it impossible for neural architectures to identify and make use of these temporal events. We, therefore, argue that a companion process helping identify salient events in the input time series and driving model’s attention to the associated salient sub-sequences can help with learning a high-performing network. Relying on this observation, we propose a novel Saliency-Driven Mutual Cross Attention (SDMA) framework that extracts localized temporal events and generate a saliency series to complement the input time series. We further propose an architecture which accounts for the mutual cross-talk between the input and saliency series branches where input and saliency series attend each other. Experiments show that the proposed mutually-cross attention framework can offer significant boosts in model performance when compared against non-attentioned, conventionally attentioned, and conventionally cross-attentioned models.

Deep Transformation Models: Tackling Complex Regression Problems with Neural Network Based Transformation Models

Beate Sick, Torsten Hothorn, Oliver Dürr

Auto-TLDR; A Deep Transformation Model for Probabilistic Regression

Abstract Slides Poster Similar

We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks it is predominantly used to just predict a single number. This ignores the non-deterministic character of most tasks. Especially if crucial decisions are based on the predictions, like in medical applications, it is essential to quantify the prediction uncertainty. The presented deep learning transformation model estimates the whole conditional probability distribution, which is the most thorough way to capture uncertainty about the outcome. We combine ideas from a statistical transformation model (most likely transformation) with recent transformation models from deep learning (normalizing flows) to predict complex outcome distributions. The core of the method is a parameterized transformation function which can be trained with the usual maximum likelihood framework using gradient descent. The method can be combined with existing deep learning architectures. For small machine learning benchmark datasets, we report state of the art performance for most dataset and partly even outperform it. Our method works for complex input data, which we demonstrate by employing a CNN architecture on image data.

Detecting and Adapting to Crisis Pattern with Context Based Deep Reinforcement Learning

Eric Benhamou, David Saltiel Saltiel, Jean-Jacques Ohana Ohana, Jamal Atif Atif

Auto-TLDR; Deep Reinforcement Learning for Financial Crisis Detection and Dis-Investment

Abstract Slides Poster Similar

Deep reinforcement learning (DRL) has reached super human levels in complexes tasks like game solving (Go, StarCraft II), and autonomous driving. However, it remains an open question whether DRL can reach human level in applications to financial problems and in particular in detecting pattern crisis and consequently dis-investing. In this paper, we present an innovative DRL framework consisting in two sub-networks fed respectively with portfolio strategies past performances and standard deviation as well as additional contextual features. The second sub network plays an important role as it captures dependencies with common financial indicators features like risk aversion, economic surprise index and correlations between assets that allows taking into account context based information. We compare different network architectures either using layers of convolutions to reduce network's complexity or LSTM block to capture time dependency and whether previous allocations is important in the modeling. We also use adversarial training to make the final model more robust. Results on test set show this approach substantially over-performs traditional portfolio optimization methods like Markovitz and is able to detect and anticipate crisis like the current Covid one.

Learning Parameter Distributions to Detect Concept Drift in Data Streams

Johannes Haug, Gjergji Kasneci

Auto-TLDR; A novel framework for the detection of concept drift in streaming environments

Abstract Slides Poster Similar

Data distributions in streaming environments are usually not stationary. In order to maintain a high predictive quality at all times, online learning models need to adapt to distributional changes, which are known as concept drift. The timely and robust identification of concept drift can be difficult, as we never have access to the true distribution of streaming data. In this work, we propose a novel framework for the detection of real concept drift, called ERICS. By treating the parameters of a predictive model as random variables, we show that concept drift corresponds to a change in the distribution of optimal parameters. To this end, we adopt common measures from information theory. The proposed framework is completely model-agnostic. By choosing an appropriate base model, ERICS is also capable to detect concept drift at the input level, which is a significant advantage over existing approaches. An evaluation on several synthetic and real-world data sets suggests that the proposed framework identifies concept drift more effectively and precisely than various existing works.

Switching Dynamical Systems with Deep Neural Networks

Cesar Ali Ojeda Marin, Kostadin Cvejoski, Bogdan Georgiev, Ramses J. Sanchez

Auto-TLDR; Variational RNN for Switching Dynamics

Abstract Slides Poster Similar

The problem of uncovering different dynamicalregimes is of pivotal importance in time series analysis. Switchingdynamical systems provide a solution for modeling physical phe-nomena whose time series data exhibit different dynamical modes.In this work we propose a novel variational RNN model forswitching dynamics allowing for both non-Markovian and non-linear dynamical behavior between and within dynamic modes.Attention mechanisms are provided to inform the switchingdistribution. We evaluate our model on synthetic and empiricaldatasets of diverse nature and successfully uncover differentdynamical regimes and predict the switching dynamics.

Fingerprints, Forever Young?

Roman Kessler, Olaf Henniger, Christoph Busch

Auto-TLDR; Mated Similarity Scores for Fingerprint Recognition: A Hierarchical Linear Model

Abstract Slides Poster Similar

In the present study we analyzed longitudinal fingerprint data of 20 data subjects, acquired over a time span of up to 12 years. Using hierarchical linear modeling, we aimed to delineate mated similarity scores as a function of fingerprint quality and of the time interval between reference and probe images. Our results did not reveal effects on mated similarity scores caused by an increasing time interval across subjects, but rather individual effects on mated similarity scores. The results are in line with the general assumption that the fingerprint as a biometric characteristic and the features extracted from it do not change over the adult life span. However, it contradicts several related studies that reported noticeable template ageing effects. We discuss why different findings regarding ageing of references in fingerprint recognition systems were made.

Attack-Agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning

Matthew Watson, Noura Al Moubayed

Auto-TLDR; Explainability-based Detection of Adversarial Samples on EHR and Chest X-Ray Data

Abstract Slides Poster Similar

Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks has shown the ease of designing samples to mislead a model into making incorrect predictions. In this work, we propose an explainability-based method for the accurate detection of adversarial samples on two datasets with different complexity and properties: Electronic Health Record (EHR) and chest X-ray (CXR) data. On the MIMIC-III and Henan-Renmin EHR datasets, we report a detection accuracy of 77% against the Longitudinal Adversarial Attack. On the MIMIC-CXR dataset, we achieve an accuracy of 88%; significantly improving on the state of the art of adversarial detection in both datasets by over 10% in all settings. We propose an anomaly detection based method using explainability techniques to detect adversarial samples which is able to generalise to different attack methods without a need for retraining.

Deep Learning Based Sepsis Intervention: The Modelling and Prediction of Severe Sepsis Onset

Gavin Tsang, Xianghua Xie

Auto-TLDR; Predicting Sepsis onset by up to six hours prior using a boosted cascading training methodology and adjustable margin hinge loss function

Abstract Slides Poster Similar

Sepsis presents a significant challenge to healthcare providers during critical care scenarios such as within an intensive care unit. The prognosis of the onset of severe septic shock results in significant increases in mortality rate, length of stay and readmission rates. Continual advancements in health informatics data allows for applications within the machine learning field to predict sepsis onset in a timely manner, allowing for effective preventative intervention of severe septic shock. A novel deep learning application is proposed to provide effective prediction of sepsis onset by up to six hours prior, involving the use of novel concepts such as a boosted cascading training methodology and adjustable margin hinge loss function. The proposed methodology provides statistically significant improvements to that of current machine learning based modelling applications based off the Physionet Computing in Cardiology 2019 challenge. Results show test F1 scores of 0.420, a significant improvement of 0.281 as compared to the next best challenger results.

Can Reinforcement Learning Lead to Healthy Life?: Simulation Study Based on User Activity Logs

Masami Takahashi, Masahiro Kohjima, Takeshi Kurashima, Hiroyuki Toda

Auto-TLDR; Reinforcement Learning for Healthy Daily Life

Abstract Slides Poster Similar

The importance of developing an application based on intervention technology that leads to a healthier life is widely recognized. A challenging part of realizing the application is the need for planning, i.e., considering a user's health goal (e.g., sleep at 10:00 p.m. to get enough sleep), providing intervention at the appropriate timing to help the user achieve the goal. The reinforcement learning (RL) approach is well suited to this type of problem since it is a methodology for planning; RL finds the optimal strategy as that which maximizes future expected profit. The purpose of this study is to clarify the effects of intervention based on RL to support healthy daily life. Therefore, we (i) collect real daily activity data from participants, (ii) generate a user model that imitates the user's response to system interventions, (iii) examine valuable goals and design them as rewards in RL and (iv) obtain optimal intervention strategies by RL via simulations given a user model and goals. We evaluate a generated user model and verify by simulations whether our method could successfully achieve the goal. In addition, we analyze the cases that demonstrated higher probability of achieving the goal and report the features.

Thermal Characterisation of Unweighted and Weighted Networks

Jianjia Wang, Hui Wu, Edwin Hancock

Auto-TLDR; Thermodynamic Characterisation of Networks as Particles of the Thermal System

Abstract Slides Poster Similar

Thermodynamic characterisations or analogies have proved to provide powerful tools for the statistical analysis of network populations or time series, together with the identification of structural anomalies that occur within them. For instance, classical Boltzmann statistics together with the corresponding partition function have been used to apply the tools of statistical physics to the analysis of variations in network structure. However, the physical analogy adopted in this analysis, together with the interpretation of the resulting system of particles is sometimes vague and remains an open question. This, in turn, has implications concerning the definition of quantities such as temperature and energy. In this paper, we take a novel view of the thermal characterisation where we regard the edges in a network as the particles of the thermal system. By considering networks with a fixed number of nodes we obtain a conservation law which applies to the particle occupation configuration. Using this interpretation, we provide a physical meaning for the temperature which is related to the number of network nodes and edges. This provides a fundamental description of a network as a thermal system. If we further interpret the elements of the adjacency matrix as the binary microstates associated with edges, this allows us to further extend the analysis to systems with edge-weights. We thus introduce the concept of the canonical ensemble into the thermal network description and the corresponding partition function and then use this to compute the thermodynamic quantities. Finally, we provide numerical experiments on synthetic and real-world data-sets to evaluate the thermal characterisations for both unweighted and weighted networks.

Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

Jiansheng Fang, Xiaoqing Zhang, Yan Hu, Yanwu Xu, Ming Yang, Jiang Liu

Auto-TLDR; Bayesian Latent Factor Model for Collaborative Filtering

Abstract Slides Similar

Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix factorization applied usually in pattern recognition, LFM models user-item interactions as inner products of factor vectors of user and item in that space and can be efficiently solved by least square methods with optimal estimation. However, such optimal estimation methods are prone to overfitting due to the extreme sparsity of user-item interactions. In this paper, we propose a Bayesian treatment for LFM, named Bayesian Latent Factor Model (BLFM). Based on observed user-item interactions, we build a probabilistic factor model in which the regularization is introduced via placing prior constraint on latent factors, and the likelihood function is established over observations and parameters. Then we draw samples of latent factors from the posterior distribution with Variational Inference (VI) to predict expected value. We further make an extension to BLFM, called BLFMBias, incorporating user-dependent and item-dependent biases into the model for enhancing performance. Extensive experiments on the movie rating dataset show the effectiveness of our proposed models by compared with several strong baselines.

Better Prior Knowledge Improves Human-Pose-Based Extrinsic Camera Calibration

Olivier Moliner, Sangxia Huang, Kalle Åström

Auto-TLDR; Improving Human-pose-based Extrinsic Calibration for Multi-Camera Systems

Abstract Slides Poster Similar

Accurate extrinsic calibration of wide baseline multi-camera systems enables better understanding of 3D scenes for many applications and is of great practical importance. Classical Structure-from-Motion calibration methods require special calibration equipment so that accurate point correspondences can be detected between different views. In addition, an operator with some training is usually needed to ensure that data is collected in a way that leads to good calibration accuracy. This limits the ease of adoption of such technologies. Recently, methods have been proposed to use human pose estimation models to establish point correspondences, thus removing the need for any special equipment. The challenge with this approach is that human pose estimation algorithms typically produce much less accurate feature points compared to classical patch-based methods. Another problem is that ambient human motion might not be optimal for calibration. We build upon prior works and introduce several novel ideas to improve the accuracy of human-pose-based extrinsic calibration. Our first contribution is a robust reprojection loss based on a better understanding of the sources of pose estimation error. Our second contribution is a 3D human pose likelihood model learned from motion capture data. We demonstrate significant improvements in calibration accuracy by evaluating our method on four publicly available datasets.

Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction

Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Cheng-Zhong Xu

Auto-TLDR; Multi-GCGRU: A Deep Learning Framework for Stock Price Prediction with Cross Effect

Abstract Slides Poster Similar

Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration, we propose a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent units (GRU) to predict stock movement. Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on the pre-defined graphs. The cross-correlation features produced by GCN are concatenated with historical records and fed into GRU to model the temporal pattern in stock price. To further get rid of prior knowledge, we explore an adaptive stock graph learned by data automatically. Experiments on two stock indexes in China market show that our model outperforms other baselines. Note that our model is rather feasible to incorporate more effective pre-defined stock relationships. What's more, it can also learn a data-driven relationship without any domain knowledge.

Sketch-Based Community Detection Via Representative Node Sampling

Mahlagha Sedghi, Andre Beckus, George Atia

Auto-TLDR; Sketch-based Clustering of Community Detection Using a Small Sketch

Abstract Slides Poster Similar

This paper proposes a sketch-based approach to the community detection problem which clusters the full graph through the use of an informative and concise sketch. The reduced sketch is built through an effective sampling approach which selects few nodes that best represent the complete graph and operates on a pairwise node similarity measure based on the average commute time. After sampling, the proposed algorithm clusters the nodes in the sketch, and then infers the cluster membership of the remaining nodes in the full graph based on their aggregate similarity to nodes in the partitioned sketch. By sampling nodes with strong representation power, our approach can improve the success rates over full graph clustering. In challenging cases with large node degree variation, our approach not only maintains competitive accuracy with full graph clustering despite using a small sketch, but also outperforms existing sampling methods. The use of a small sketch allows considerable storage savings, and computational and timing improvements for further analysis such as clustering and visualization. We provide numerical results on synthetic data based on the homogeneous, heterogeneous and degree corrected versions of the stochastic block model, as well as experimental results on real-world data.

Inferring Functional Properties from Fluid Dynamics Features

Andrea Schillaci, Maurizio Quadrio, Carlotta Pipolo, Marcello Restelli, Giacomo Boracchi

Auto-TLDR; Exploiting Convective Properties of Computational Fluid Dynamics for Medical Diagnosis

Abstract Slides Poster Similar

In a wide range of applied problems involving fluid flows, Computational Fluid Dynamics (CFD) provides detailed quantitative information on the flow field, at various levels of fidelity and computational cost. However, CFD alone cannot predict high-level functional properties of the system that are not easily obtained from the equations of fluid motion. In this work, we present a data-driven framework to extract additional information, such as medical diagnostic output, from CFD solutions. The task is made difficult by the huge data dimensionality of CFD, together with the limited amount of training data implied by its high computational cost. By pursuing a traditional ML pipeline of pre-processing, feature extraction, and model training, we demonstrate that informative features can be extracted from CFD data. Two experiments, pertaining to different application domains, support the claim that the convective properties implicit into a CFD solution can be leveraged to retrieve functional information for which an analytical definition is missing. Despite the preliminary nature of our study and the relative simplicity of both the geometrical and CFD models, for the first time we demonstrate that the combination of ML and CFD can diagnose a complex system in terms of high-level functional information.

Wireless Localisation in WiFi Using Novel Deep Architectures

Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela Doufexi, Tim Farnham

Auto-TLDR; Deep Neural Network for Indoor Localisation of WiFi Devices in Indoor Environments

Abstract Slides Poster Similar

This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding. First, we present a novel shallow neural network (SNN) in which features are extracted from the channel state information (CSI) corresponding to WiFi subcarriers received on different antennas and used to train the model. The single layer architecture of this localisation neural network makes it lightweight and easy-to-deploy on devices with stringent constraints on computational resources. We further investigate for localisation the use of deep learning models and design novel architectures for convolutional neural network (CNN) and long-short term memory (LSTM). We extensively evaluate these localisation algorithms for continuous tracking in indoor environments. Experimental results prove that even an SNN model, after a careful handcrafted feature extraction, can achieve accurate localisation. Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates. We also found that the performance of neural network-based methods are directly affected by the number of anchor access points (APs) regardless of their structure. With three APs, all neural network models proposed in this paper can obtain localisation accuracy of around 0.5 metres. In addition the proposed deep NN architecture reduces the data pre-processing time by 6.5 hours compared with a shallow NN using the data collected in our testbed. In the deployment phase, the inference time is also significantly reduced to 0.1 ms per sample. We also demonstrate the generalisation capability of the proposed method by evaluating models using different target movement characteristics to the ones in which they were trained.

End-To-End Multi-Task Learning of Missing Value Imputation and Forecasting in Time-Series Data

Jinhee Kim, Taesung Kim, Jang-Ho Choi, Jaegul Choo

Auto-TLDR; Time-Series Prediction with Denoising and Imputation of Missing Data

Abstract Slides Poster Similar

Multivariate time-series prediction is a common task, but it often becomes challenging due to missing values involved in data caused by unreliable sensors and other issues. In fact, inaccurate imputation of missing values can degrade the downstream prediction performance, so it may be better not to rely on the estimated values of missing data. Furthermore, observed data may contain noise, so denoising them can be helpful for the main task at hand. In response, we propose a novel approach that can automatically utilize the optimal combination of the observed and the estimated values to generate not only complete, but also noise-reduced data by our own gating mechanism. We evaluate our model on real-world time-series datasets and achieved state-of-the-art performance, demonstrating that our method successfully handle the incomplete datasets. Moreover, we present in-depth studies using a carefully designed, synthetic multivariate time-series dataset to verify the effectiveness of the proposed model. The ablation studies and the experimental analysis of the proposed gating mechanism show that the proposed method works as an effective denoising as well as imputation method for time-series classification tasks.

Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study

Veysel Kocaman, Ofer M. Shir, Thomas Baeck

Auto-TLDR; Exploiting Batch Normalization before the Output Layer in Deep Learning for Minority Class Detection in Imbalanced Data Sets

Abstract Slides Poster Similar

Some real-world domains, such as Agriculture and Healthcare, comprise early-stage disease indications whose recording constitutes a rare event, and yet, whose precise detection at that stage is critical. In this type of highly imbalanced classification problems, which encompass complex features, deep learning (DL) is much needed because of its strong detection capabilities. At the same time, DL is observed in practice to favor majority over minority classes and consequently suffer from inaccurate detection of the targeted early-stage indications. To simulate such scenarios, we artificially generate skewness (99% vs. 1%) for certain plant types out of the PlantVillage dataset as a basis for classification of scarce visual cues through transfer learning. By randomly and unevenly picking healthy and unhealthy samples from certain plant types to form a training set, we consider a base experiment as fine-tuning ResNet34 and VGG19 architectures and then testing the model performance on a balanced dataset of healthy and unhealthy images. We empirically observe that the initial F1 test score jumps from 0.29 to 0.95 for the minority class upon adding a final Batch Normalization (BN) layer just before the output layer in VGG19. We demonstrate that utilizing an additional BN layer before the output layer in modern CNN architectures has a considerable impact in terms of minimizing the training time and testing error for minority classes in highly imbalanced data sets. Moreover, when the final BN is employed, trying to minimize validation and training losses may not be an optimal way for getting a high F1 test score for minority classes in anomaly detection problems. That is, the network might perform better even if it is not ‘confident’ enough while making a prediction; leading to another discussion about why softmax output is not a good uncertainty measure for DL models.

Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization

Aliaksei Mikhailiuk, Clifford Wilmot, Maria Perez-Ortiz, Dingcheng Yue, Rafal Mantiuk

Auto-TLDR; ASAP: An Active Sampling Algorithm for Pairwise Comparison Data

Abstract Slides Similar

Pairwise comparison data arise in many domains with subjective assessment experiments, for example in image and video quality assessment. In these experiments observers are asked to express a preference between two conditions. However, many pairwise comparison protocols require a large number of comparisons to infer accurate scores, which may be unfeasible when each comparison is time-consuming (e.g. videos) or expensive (e.g. medical imaging). This motivates the use of an active sampling algorithm that chooses only the most informative pairs for comparison. In this paper we propose ASAP, an active sampling algorithm based on approximate message passing and expected information gain maximization. Unlike most existing methods, which rely on partial updates of the posterior distribution, we are able to perform full updates and therefore much improve the accuracy of the inferred scores. The algorithm relies on three techniques for reducing computational cost: inference based on approximate message passing, selective evaluations of the information gain, and selecting pairs in a batch that forms a minimum spanning tree of the inverse of information gain. We demonstrate, with real and synthetic data, that ASAP offers the highest accuracy of inferred scores compared to the existing methods. We also provide an open-source GPU implementation of ASAP for large-scale experiments.

Using Machine Learning to Refer Patients with Chronic Kidney Disease to Secondary Care

Lee Au-Yeung, Xianghua Xie, Timothy Marcus Scale, James Anthony Chess

Auto-TLDR; A Machine Learning Approach for Chronic Kidney Disease Prediction using Blood Test Data

Abstract Slides Poster Similar

There has been growing interest recently in using machine learning techniques as an aid in clinical medicine. Machine learning offers a range of classification algorithms which can be applied to medical data to aid in making clinical predictions. Recent studies have demonstrated the high predictive accuracy of various classification algorithms applied to clinical data. Several studies have already been conducted in diagnosing or predicting chronic kidney disease at various stages using different sets of variables. In this study we are investigating the use machine learning techniques with blood test data. Such a system could aid renal teams in making recommendations to primary care general practitioners to refer patients to secondary care where patients may benefit from earlier specialist assessment and medical intervention. We are able to achieve an overall accuracy of 88.48\% using logistic regression, 87.12\% using ANN and 85.29\% using SVM. ANNs performed with the highest sensitivity at 89.74\% compared to 86.67\% for logistic regression and 85.51\% for SVM.

Activation Density Driven Efficient Pruning in Training

Timothy Foldy-Porto, Yeshwanth Venkatesha, Priyadarshini Panda

Auto-TLDR; Real-Time Neural Network Pruning with Compressed Networks

Abstract Slides Poster Similar

Neural network pruning with suitable retraining can yield networks with considerably fewer parameters than the original with comparable degrees of accuracy. Typical pruning methods require large, fully trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining procedure to regain the original accuracy. We propose a novel pruning method that prunes a network real-time during training, reducing the overall training time to achieve an efficient compressed network. We introduce an activation density based analysis to identify the optimal relative sizing or compression for each layer of the network. Our method is architecture agnostic, allowing it to be employed on a wide variety of systems. For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to $200 \times$ reduction in parameters and over $60 \times$ reduction in inference compute operations in the best case) with accuracy comparable to the baseline network. By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods. Furthermore, training compressed networks at different epochs with our proposed method yields considerable reduction in training compute complexity ($1.6\times$ to $3.2\times$ lower) at near iso-accuracy as compared to a baseline network trained entirely from scratch.

Verifying the Causes of Adversarial Examples

Honglin Li, Yifei Fan, Frieder Ganz, Tony Yezzi, Payam Barnaghi

Auto-TLDR; Exploring the Causes of Adversarial Examples in Neural Networks

Abstract Slides Poster Similar

The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial examples falls behind studies on attacks and defenses. In this paper, we present a collection of potential causes of adversarial examples and verify (or partially verify) them through carefully-designed controlled experiments. The major causes of adversarial examples include model linearity, one-sum constraint, and geometry of the categories. To control the effect of those causes, multiple techniques are applied such as $L_2$ normalization, replacement of loss functions, construction of reference datasets, and novel models using multi-layer perceptron probabilistic neural networks (MLP-PNN) and density estimation (DE). Our experiment results show that geometric factors tend to be more direct causes and statistical factors magnify the phenomenon, especially for assigning high prediction confidence. We hope this paper will inspire more studies to rigorously investigate the root causes of adversarial examples, which in turn provide useful guidance on designing more robust models.

Video Anomaly Detection by Estimating Likelihood of Representations

Yuqi Ouyang, Victor Sanchez

Auto-TLDR; Video Anomaly Detection in the latent feature space using a deep probabilistic model

Abstract Slides Poster Similar

Video anomaly detection is a challenging task not only because it involves solving many sub-tasks such as motion representation, object localization and action recognition, but also because it is commonly considered as an unsupervised learning problem that involves detecting outliers. Traditionally, solutions to this task have focused on the mapping between video frames and their low-dimensional features, while ignoring the spatial connections of those features. Recent solutions focus on analyzing these spatial connections by using hard clustering techniques, such as K-Means, or applying neural networks to map latent features to a general understanding, such as action attributes. In order to solve video anomaly in the latent feature space, we propose a deep probabilistic model to transfer this task into a density estimation problem where latent manifolds are generated by a deep denoising autoencoder and clustered by expectation maximization. Evaluations on several benchmarks datasets show the strengths of our model, achieving outstanding performance on challenging datasets.

Deep Learning on Active Sonar Data Using Bayesian Optimization for Hyperparameter Tuning

Henrik Berg, Karl Thomas Hjelmervik

Auto-TLDR; Bayesian Optimization for Sonar Operations in Littoral Environments

Abstract Slides Poster Similar

Sonar operations in littoral environments may be challenging due to an increased probability of false alarms. Machine learning can be used to train classifiers that are able to filter out most of the false alarms automatically, however, this is a time consuming process, with many hyperparameters that need to be tuned in order to yield useful results. In this paper, Bayesian optimization is used to search for good values for some of the hyperparameters, like topology and training parameters, resulting in performance superior to earlier trial-and-error based training. Additionally, we analyze some of the parameters involved in the Bayesian optimization, as well as the resulting hyperparameter values.

Hierarchical Routing Mixture of Experts

Wenbo Zhao, Yang Gao, Shahan Ali Memon, Bhiksha Raj, Rita Singh

Auto-TLDR; A Binary Tree-structured Hierarchical Routing Mixture of Experts for Regression

Abstract Slides Poster Similar

In regression tasks the distribution of the data is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data, and strong local models are needed to make good predictions. Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts. The classifier nodes jointly soft-partition the input-output space based on the natural separateness of multimodal data. This enables simple leaf experts to be effective for prediction. Further, we develop a probabilistic framework for the HRME model, and propose a recursive Expectation-Maximization (EM) based algorithm to learn both the tree structure and the expert models. Experiments on a collection of regression tasks validate the effectiveness of our method compared to a variety of other regression models.

Killing Four Birds with One Gaussian Process: The Relation between Different Test-Time Attacks

Kathrin Grosse, Michael Thomas Smith, Michael Backes

Auto-TLDR; Security of Gaussian Process Classifiers against Attack Algorithms

Abstract Slides Poster Similar

In machine learning (ML) security, attacks like evasion, model stealing or membership inference are generally studied in individually. Previous work has also shown a relationship between some attacks and decision function curvature of the targeted model. Consequently, we study an ML model allowing direct control over the decision surface curvature: Gaussian Process classifiers (GPCs). For evasion, we find that changing GPC's curvature to be robust against one attack algorithm boils down to enabling a different norm or attack algorithm to succeed. This is backed up by our formal analysis showing that static security guarantees are opposed to learning. Concerning intellectual property, we show formally that lazy learning does not necessarily leak all information when applied. In practice, often a seemingly secure curvature can be found. For example, we are able to secure GPC against empirical membership inference by proper configuration. In this configuration, however, the GPC's hyper-parameters are leaked, e.g. model reverse engineering succeeds. We conclude that attacks on classification should not be studied in isolation, but in relation to each other.

Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks

Denis Huseljic, Bernhard Sick, Marek Herde, Daniel Kottke

Auto-TLDR; AE-DNN: Modeling Uncertainty in Deep Neural Networks

Abstract Slides Poster Similar

Despite the success of deep neural networks (DNN) in many applications, their ability to model uncertainty is still significantly limited. For example, in safety-critical applications such as autonomous driving, it is crucial to obtain a prediction that reflects different types of uncertainty to address life-threatening situations appropriately. In such cases, it is essential to be aware of the risk (i.e., aleatoric uncertainty) and the reliability (i.e., epistemic uncertainty) that comes with a prediction. We present AE-DNN, a model allowing the separation of aleatoric and epistemic uncertainty while maintaining a proper generalization capability. AE-DNN is based on deterministic DNN, which can determine the respective uncertainty measures in a single forward pass. In analyses with synthetic and image data, we show that our method improves the modeling of epistemic uncertainty while providing an intuitively understandable separation of risk and reliability.

Road Network Metric Learning for Estimated Time of Arrival

Yiwen Sun, Kun Fu, Zheng Wang, Changshui Zhang, Jieping Ye

Auto-TLDR; Road Network Metric Learning for Estimated Time of Arrival (RNML-ETA)

Abstract Slides Poster Similar

Recently, deep learning have achieved promising results in Estimated Time of Arrival (ETA), which is considered as predicting the travel time from the origin to the destination along a given path. One of the key techniques is to use embedding vectors to represent the elements of road network, such as the links (road segments). However, the embedding suffers from the data sparsity problem that many links in the road network are traversed by too few floating cars even in large ride-hailing platforms like Uber and DiDi. Insufficient data makes the embedding vectors in an under-fitting status, which undermines the accuracy of ETA prediction. To address the data sparsity problem, we propose the Road Network Metric Learning framework for ETA (RNML ETA). It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors. We further propose the triangle loss, a novel loss function to improve the efficiency of metric learning. We validated the effectiveness of RNML-ETA on large scale real-world datasets, by showing that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.

Assortative-Constrained Stochastic Block Models

Daniel Gribel, Thibaut Vidal, Michel Gendreau

Auto-TLDR; Constrained Stochastic Block Models for Assortative Communities in Neural Networks

Abstract Slides Poster Similar

Stochastic block models (SBMs) are often used to find assortative community structures in networks, such that the probability of connections within communities is higher than in between communities. However, classic SBMs are not limited to assortative structures. In this study, we discuss the implications of this model-inherent indifference towards assortativity or disassortativity, and show that it can lead to undesirable outcomes in datasets which are known to be assortative but which contain a reduced amount of information. To circumvent these issues, we propose a constrained SBM that imposes strong assortativity constraints, along with efficient algorithmic solutions. These constraints significantly boost community-detection capabilities in regimes which are close to the detectability threshold. They also permit to identify structurally-different communities in networks representing cerebral-cortex activity regions.

Data Normalization for Bilinear Structures in High-Frequency Financial Time-Series

Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis

Auto-TLDR; Bilinear Normalization for Financial Time-Series Analysis and Forecasting

Abstract Slides Poster Similar

Financial time-series analysis and forecasting have been extensively studied over the past decades, yet still remain as a very challenging research topic. Since the financial market is inherently noisy and stochastic, a majority of financial time-series of interests are non-stationary, and often obtained from different modalities. This property presents great challenges and can significantly affect the performance of the subsequent analysis/forecasting steps. Recently, the Temporal Attention augmented Bilinear Layer (TABL) has shown great performances in tackling financial forecasting problems. In this paper, by taking into account the nature of bilinear projections in TABL networks, we propose Bilinear Normalization (BiN), a simple, yet efficient normalization layer to be incorporated into TABL networks to tackle potential problems posed by non-stationarity and multimodalities in the input series. Our experiments using a large scale Limit Order Book (LOB) consisting of more than 4 million order events show that BiN-TABL outperforms TABL networks using other state-of-the-arts normalization schemes by a large margin.

Anomaly Detection, Localization and Classification for Railway Inspection

Riccardo Gasparini, Andrea D'Eusanio, Guido Borghi, Stefano Pini, Giuseppe Scaglione, Simone Calderara, Eugenio Fedeli, Rita Cucchiara

Auto-TLDR; Anomaly Detection and Localization using thermal images in the lowlight environment

Abstract Slides Similar

The ability to detect, localize and classify objects that are anomalies is a challenging task in the computer vision community. In this paper, we tackle these tasks developing a framework to automatically inspect the railway during the night. Specifically, it is able to predict the presence, the image coordinates and the class of obstacles. To deal with the lowlight environment, the framework is based on thermal images and consists of three different modules that address the problem of detecting anomalies, predicting their image coordinates and classifying them. Moreover, due to the absolute lack of publicly released datasets collected in the railway context for anomaly detection, we introduce a new multi-modal dataset, acquired from a rail drone, used to evaluate the proposed framework. Experimental results confirm the accuracy of the framework and its suitability, in terms of computational load, performance, and inference time, to be implemented on a self-powered inspection system.