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Boundaries of Single-Class Regions in the Input Space of Piece-Wise Linear Neural Networks

Jay Hoon Jung, Youngmin Kwon

Auto-TLDR; Piece-wise Linear Neural Networks with Linear Constraints

Abstract Slides

An input space is a set of all the possible inputs for a neural network. An element or a group of elements in the input space can easily be understood by projecting them on their original forms. Even though Piece-wise Linear Neural Networks (PLNNs) are a nonlinear system in general, a PLNN can also be expressed in terms of linear constraints because the Rectified Linear Units (ReLU) function is a piece-wise linear function. A PLNN divides the input space into disjoint linear regions. We proved that all components of the outputs are continuous at the boundary between two different adjacent regions. This continuity implies that the boundary corresponding to a unit itself should be continuous regardless of the regions. Furthermore, we also obtained the boundaries of a single-class region, which has the same predicted classes in the interior of the region. Finally, we suggested that the point-wise robustness of a neural network can be calculated by investigating the boundaries of linear regions and the single-class regions. We obtained adversarial examples in which Euclidean distances from original inputs are less than 0.01 pixels.

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Color, Edge, and Pixel-Wise Explanation of Predictions Based onInterpretable Neural Network Model

Jay Hoon Jung, Youngmin Kwon

Auto-TLDR; Explainable Deep Neural Network with Edge Detecting Filters

Abstract Poster Similar

We design an interpretable network model by introducing explainable components into a Deep Neural Network (DNN). We substituted the first kernels of a Convolutional Neural Network (CNN) and a ResNet-50 with the well-known edge detecting filters such as Sobel, Prewitt, and other filters. Each filters' relative importance scores are measured with a variant of Layer-wise Relevance Propagation (LRP) method proposed by Bach et al. Since the effects of the edge detecting filters are well understood, our model provides three different scores to explain individual predictions: the scores with respect to (1) colors, (2) edge filters, and (3) pixels of the image. Our method provides more tools to analyze the predictions by highlighting the location of important edges and colors in the images. Furthermore, the general features of a category can be shown in our scores as well as individual predictions. At the same time, the model does not degrade performances on MNIST, Fruit360 and ImageNet datasets.

Dimensionality Reduction for Data Visualization and Linear Classification, and the Trade-Off between Robustness and Classification Accuracy

Martin Becker, Jens Lippel, Thomas Zielke

Auto-TLDR; Robustness Assessment of Deep Autoencoder for Data Visualization using Scatter Plots

Abstract Slides Poster Similar

This paper has three intertwined goals. The first is to introduce a new similarity measure for scatter plots. It uses Delaunay triangulations to compare two scatter plots regarding their relative positioning of clusters. The second is to apply this measure for the robustness assessment of a recent deep neural network (DNN) approach to dimensionality reduction (DR) for data visualization. It uses a nonlinear generalization of Fisher's linear discriminant analysis (LDA) as the encoder network of a deep autoencoder (DAE). The DAE's decoder network acts as a regularizer. The third goal is to look at different variants of the DNN: ones that promise robustness and ones that promise high classification accuracies. This is to study the trade-off between these two objectives -- our results support the recent claim that robustness may be at odds with accuracy; however, results that are balanced regarding both objectives are achievable. We see a restricted Boltzmann machine (RBM) pretraining and the DAE based regularization as important building blocks for achieving balanced results. As a means of assessing the robustness of DR methods, we propose a measure that is based on our similarity measure for scatter plots. The robustness measure comes with a superimposition view of Delaunay triangulations, which allows a fast comparison of results from multiple DR methods.

Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks

Ramin Barati, Reza Safabakhsh, Mohammad Rahmati

Auto-TLDR; Convolutional Neural Networks and Adversarial Training from the Perspective of convergence

Abstract Slides Poster Similar

In this paper, we study the adversarial examples existence and adversarial training from the standpoint of convergence and provide evidence that pointwise convergence in ANNs can explain these observations. The main contribution of our proposal is that it relates the objective of the evasion attacks and adversarial training with concepts already defined in learning theory. Also, we extend and unify some of the other proposals in the literature and provide alternative explanations on the observations made in those proposals. Through different experiments, we demonstrate that the framework is valuable in the study of the phenomenon and is applicable to real-world problems.

RNN Training along Locally Optimal Trajectories via Frank-Wolfe Algorithm

Yun Yue, Ming Li, Venkatesh Saligrama, Ziming Zhang

Auto-TLDR; Frank-Wolfe Algorithm for Efficient Training of RNNs

Abstract Slides Poster Similar

We propose a novel and efficient training method for RNNs by iteratively seeking a local minima on the loss surface within a small region, and leverage this directional vector for the update, in an outer-loop. We propose to utilize the Frank-Wolfe (FW) algorithm in this context. Although, FW implicitly involves normalized gradients, which can lead to a slow convergence rate, we develop a novel RNN training method that, surprisingly, even with the additional cost, the overall training cost is empirically observed to be lower than back-propagation. Our method leads to a new Frank-Wolfe method, that is in essence an SGD algorithm with a restart scheme. We prove that under certain conditions our algorithm has a sublinear convergence rate of $O(1/\epsilon)$ for $\epsilon$ error. We then conduct empirical experiments on several benchmark datasets including those that exhibit long-term dependencies, and show significant performance improvement. We also experiment with deep RNN architectures and show efficient training performance. Finally, we demonstrate that our training method is robust to noisy data.

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.

Regularized Flexible Activation Function Combinations for Deep Neural Networks

Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran

Auto-TLDR; Flexible Activation in Deep Neural Networks using ReLU and ELUs

Abstract Slides Poster Similar

Activation in deep neural networks is fundamental to achieving non-linear mappings. Traditional studies mainly focus on ﬁnding ﬁxed activations for a particular set of learning tasks or model architectures. The research on ﬂexible activation is quite limited in both designing philosophy and application scenarios. In this study, three principles of choosing ﬂexible activation components are proposed and a general combined form of ﬂexible activation functions is implemented. Based on this, a novel family of ﬂexible activation functions that can replace sigmoid or tanh in LSTM cells are implemented, as well as a new family by combining ReLU and ELUs. Also, two new regularisation terms based on assumptions as prior knowledge are introduced. It has been shown that LSTM models with proposed ﬂexible activations P-Sig-Ramp provide signiﬁcant improvements in time series forecasting, while the proposed P-E2-ReLU achieves better and more stable performance on lossy image compression tasks with convolutional auto-encoders. In addition, the proposed regularization terms improve the convergence,performance and stability of the models with ﬂexible activation functions. The code for this paper is available at https://github.com/9NXJRDDRQK/Flexible Activation.

Compression Strategies and Space-Conscious Representations for Deep Neural Networks

Giosuè Marinò, Gregorio Ghidoli, Marco Frasca, Dario Malchiodi

Auto-TLDR; Compression of Large Convolutional Neural Networks by Weight Pruning and Quantization

Abstract Slides Poster Similar

Recent advances in deep learning have made available large, powerful convolutional neural networks (CNN) with state-of-the-art performance in several real-world applications. Unfortunately, these large-sized models have millions of parameters, thus they are not deployable on resource-limited platforms (e.g. where RAM is limited). Compression of CNNs thereby becomes a critical problem to achieve memory-efficient and possibly computationally faster model representations. In this paper, we investigate the impact of lossy compression of CNNs by weight pruning and quantization, and lossless weight matrix representations based on source coding. We tested several combinations of these techniques on four benchmark datasets for classification and regression problems, achieving compression rates up to 165 times, while preserving or improving the model performance.

Multi-Layered Discriminative Restricted Boltzmann Machine with Untrained Probabilistic Layer

Yuri Kanno, Muneki Yasuda

Auto-TLDR; MDRBM: A Probabilistic Four-layered Neural Network for Extreme Learning Machine

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An extreme learning machine (ELM) is a three-layered feed-forward neural network having untrained parameters, which are randomly determined before training. Inspired by the idea of ELM, a probabilistic untrained layer called a probabilistic-ELM (PELM) layer is proposed, and it is combined with a discriminative restricted Boltzmann machine (DRBM), which is a probabilistic three-layered neural network for solving classification problems. The proposed model is obtained by stacking DRBM on the PELM layer. The resultant model (i.e., multi-layered DRBM (MDRBM)) forms a probabilistic four-layered neural network. In MDRBM, the parameters in the PELM layer can be determined using Gaussian-Bernoulli restricted Boltzmann machine. Owing to the PELM layer, MDRBM obtains a strong immunity against noise in inputs, which is one of the most important advantages of MDRBM. Numerical experiments using some benchmark datasets, MNIST, Fashion-MNIST, Urban Land Cover, and CIFAR-10, demonstrate that MDRBM is superior to other existing models, particularly, in terms of the noise-robustness property (or, in other words, the generalization property).

On the Global Self-attention Mechanism for Graph Convolutional Networks

Chen Wang, Deng Chengyuan

Auto-TLDR; Global Self-Attention Mechanism for Graph Convolutional Networks

Abstract Slides Similar

Applying Global Self-Attention (GSA) mechanism over features has achieved remarkable success on Convolutional Neural Networks (CNNs). However, it is not clear if Graph Convolutional Networks (GCNs) can similarly benefit from such a technique. In this paper, inspired by the similarity between CNNs and GCNs, we study the impact of the Global Self-Attention mechanism on GCNs. We find that consistent with the intuition, the GSA mechanism allows GCNs to capture feature-based vertex relations regardless of edge connections; As a result, the GSA mechanism can introduce extra expressive power to the GCNs. Furthermore, we analyze the impacts of the GSA mechanism on the issues of overfitting and over-smoothing. We prove that the GSA mechanism can alleviate both the overfitting and the over-smoothing issues based on some recent technical developments. Experiments on multiple benchmark datasets illustrate both superior expressive power and less significant overfitting and over-smoothing problems for the GSA-augmented GCNs, which corroborate the intuitions and the theoretical results.

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.

Learning Sparse Deep Neural Networks Using Efficient Structured Projections on Convex Constraints for Green AI

Michel Barlaud, Frederic Guyard

Auto-TLDR; Constrained Deep Neural Network with Constrained Splitting Projection

Abstract Slides Poster Similar

In recent years, deep neural networks (DNN) have been applied to different domains and achieved dramatic performance improvements over state-of-the-art classical methods. These performances of DNNs were however often obtained with networks containing millions of parameters and which training required heavy computational power. In order to cope with this computational issue a huge literature deals with proximal regularization methods which are time consuming.\\ In this paper, we propose instead a constrained approach. We provide the general framework for our new splitting projection gradient method. Our splitting algorithm iterates a gradient step and a projection on convex sets. We study algorithms for different constraints: the classical $\ell_1$ unstructured constraint and structured constraints such as the nuclear norm, the $\ell_{2,1} $ constraint (Group LASSO). We propose a new $\ell_{1,1} $ structured constraint for which we provide a new projection algorithm We demonstrate the effectiveness of our method on three popular datasets (MNIST, Fashion MNIST and CIFAR). Experiments on these datasets show that our splitting projection method with our new $\ell_{1,1} $ structured constraint provides the best reduction of memory and computational power. Experiments show that fully connected linear DNN are more efficient for green AI.

Exploiting Non-Linear Redundancy for Neural Model Compression

Muhammad Ahmed Shah, Raphael Olivier, Bhiksha Raj

Auto-TLDR; Compressing Deep Neural Networks with Linear Dependency

Abstract Slides Poster Similar

Deploying deep learning models with millions, even billions, of parameters is challenging given real world memory, power and compute constraints. In an effort to make these models more practical, in this paper, we propose a novel model compression approach that exploits linear dependence between the activations in a layer to eliminate entire structural units (neurons/convolutional filters). Our approach also adjusts the weights of the layer in a manner that is provably lossless while training if the removed neuron was perfectly predictable. We combine this approach with an annealing algorithm that may be applied during training, or even on a trained model, and demonstrate, using popular datasets, that our technique can reduce the parameters of VGG and AlexNet by more than 97\% on \cifar, 85\% on \caltech, and 19\% on ImageNet at less than 2\% loss in accuracy. Furthermore, we provide theoretical results showing that in overparametrized, locally linear (ReLU) neural networks where redundant features exist, and with correct hyperparameter selection, our method is indeed able to capture and suppress those dependencies.

Hcore-Init: Neural Network Initialization Based on Graph Degeneracy

Stratis Limnios, George Dasoulas, Dimitrios Thilikos, Michalis Vazirgiannis

Auto-TLDR; K-hypercore: Graph Mining for Deep Neural Networks

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Neural networks are the pinnacle of Artificial Intelligence, as in recent years we witnessed many novel architectures, learning and optimization techniques for deep learning. Capitalizing on the fact that neural networks inherently constitute multipartite graphs among neuron layers, we aim to analyze directly their structure to extract meaningful information that can improve the learning process. To our knowledge graph mining techniques for enhancing learning in neural networks have not been thoroughly investigated. In this paper we propose an adapted version of the k-core structure for the complete weighted multipartite graph extracted from a deep learning architecture. As a multipartite graph is a combination of bipartite graphs, that are in turn the incidence graphs of hypergraphs, we design k-hypercore decomposition, the hypergraph analogue of k-core degeneracy. We applied k-hypercore to several neural network architectures, more specifically to convolutional neural networks and multilayer perceptrons for image recognition tasks after a very short pretraining. Then we used the information provided by the hypercore numbers of the neurons to re-initialize the weights of the neural network, thus biasing the gradient optimization scheme. Extensive experiments proved that k-hypercore outperforms the state-of-the-art initialization methods.

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.

Energy Minimum Regularization in Continual Learning

Xiaobin Li, Weiqiang Wang

Auto-TLDR; Energy Minimization Regularization for Continuous Learning

Abstract Slides Similar

How to give agents the ability of continuous learning like human and animals is still a challenge. In the regularized continual learning method OWM, the constraint of the model on the energy compression of the learned task is ignored, which results in the poor performance of the method on the dataset with a large number of learning tasks. In this paper, we propose an energy minimization regularization(EMR) method to constrain the energy of learned tasks, providing enough learning space for the following tasks that are not learned, and increasing the capacity of the model to the number of learning tasks. A large number of experiments show that our method can effectively increase the capacity of the model and reduce the sensitivity of the model to the number of tasks and the size of the network.

On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks

Wolfgang Roth, Günther Schindler, Holger Fröning, Franz Pernkopf

Auto-TLDR; Quantization-Aware Bayesian Network Classifiers for Small-Scale Scenarios

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We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a recently proposed differentiable tree-augmented naive Bayes (TAN) structure learning approach to also consider the model size. Both methods are motivated by recent developments in the deep learning community, and they provide effective means to trade off between model size and prediction accuracy, which is demonstrated in extensive experiments. Furthermore, we contrast quantized BN classifiers with quantized deep neural networks (DNNs) for small-scale scenarios which have hardly been investigated in the literature. We show Pareto optimal models with respect to model size, number of operations, and test error and find that both model classes are viable options.

Generalized Conics: Properties and Applications

Aysylu Gabdulkhakova, Walter Kropatsch

Auto-TLDR; A Generalized Conic Representation for Distance Fields

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In this paper the properties of the generalized conics are used to create a unified framework for generating various types of the distance fields. The main concept behind this work is a metric that measures the distance from a point to a line segment according to the definition of the ellipse. The proposed representation provides a possibility to efficiently compute the proximity, arithmetic mean of the distances and a space tessellation with regard to the given set of polygonal objects, line segments and points. In addition, the weights can be introduced for objects, their parts and combinations. This fact leads to a hierarchical representation that can be efficiently obtained using the pixel-wise operations. The practical value of the proposed ideas is demonstrated on example of applications like skeletonization, smoothing, optimal location finding and clustering.

Semantic Segmentation Refinement Using Entropy and Boundary-guided Monte Carlo Sampling and Directed Regional Search

Zitang Sun, Sei-Ichiro Kamata, Ruojing Wang, Weili Chen

Auto-TLDR; Directed Region Search and Refinement for Semantic Segmentation

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Semantic segmentation requires both large receptive field and accurate spatial information. Despite existing methods based on fully convolutional network have greatly improved the accuracy, the prediction results still do not show satisfactory on small objects and boundary regions. We propose a refinement algorithm to improve the result generated by front network. Our method takes a modified U-shape network to generate both of segmentation mask and semantic boundary, which are used as inputs of refinement algorithm. We creatively introduce information entropy to represent the confidence of the neural network's prediction corresponding to each pixel. The information entropy combined with the semantic boundary can capture those unpredictable pixels with low-confidence through Monte Carlo sampling. Each selected pixel will be used as initial seeds for directed region search and refinement. Our purpose is to search the neighbor high-confidence regions according to the initial seeds. The re-labeling approach is based on high-confidence results. Particularly, different from general region growing methods, our method adopts a directed region search strategy based on gradient descent to find the high-confidence region effectively. Our method improves the performance both on Cityscapes and PASCAL VOC datasets. In the evaluation of segmentation accuracy of some small objects, our method surpasses most of state of the art methods.

Learning with Multiplicative Perturbations

Xiulong Yang, Shihao Ji

Auto-TLDR; XAT and xVAT: A Multiplicative Adversarial Training Algorithm for Robust DNN Training

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Adversarial Training (AT) and Virtual Adversarial Training (VAT) are the regularization techniques that train Deep Neural Networks (DNNs) with adversarial examples generated by adding small but worst-case perturbations to input examples. In this paper, we propose xAT and xVAT, new adversarial training algorithms that generate multiplicative perturbations to input examples for robust training of DNNs. Such perturbations are much more perceptible and interpretable than their additive counterparts exploited by AT and VAT. Furthermore, the multiplicative perturbations can be generated transductively or inductively, while the standard AT and VAT only support a transductive implementation. We conduct a series of experiments that analyze the behavior of the multiplicative perturbations and demonstrate that xAT and xVAT match or outperform state-of-the-art classification accuracies across multiple established benchmarks while being about 30% faster than their additive counterparts. Our source code can be found at https://github.com/sndnyang/xvat

3D Pots Configuration System by Optimizing Over Geometric Constraints

Jae Eun Kim, Muhammad Zeeshan Arshad, Seong Jong Yoo, Je Hyeong Hong, Jinwook Kim, Young Min Kim

Auto-TLDR; Optimizing 3D Conﬁgurations for Stable Pottery Restoration from irregular and noisy evidence

Abstract Slides Poster Similar

While potteries are common artifacts excavated in archaeological sites, the restoration process relies on the manual cleaning and reassembling shattered pieces. Since the number of possible 3D conﬁgurations is considerably large, the exhaustive manual trial may result in an abrasion on fractured surfaces and even failure to ﬁnd the correct matches. As a result, many recent works suggest virtual reassembly from 3D scans of the fragments. The problem is challenging in the view of the conventional 3D geometric analysis, as it is hard to extract reliable shape features from the thin break lines. We propose to optimize the global conﬁguration by combining geometric constraints with information from noisy shape features. Speciﬁcally, we enforce bijection and continuity of sequence of correspondences given estimates of corners and pair-wise matching scores between multiple break lines. We demonstrate that our pipeline greatly increases the accuracy of correspondences, resulting in the stable restoration of 3D conﬁgurations from irregular and noisy evidence.

Accuracy-Perturbation Curves for Evaluation of Adversarial Attack and Defence Methods

Jaka Šircelj, Danijel Skocaj

Auto-TLDR; Accuracy-perturbation Curve for Robustness Evaluation of Adversarial Examples

Abstract Slides Poster Similar

With more research published on adversarial examples, we face a growing need for strong and insightful methods for evaluating the robustness of machine learning solutions against their adversarial threats. Previous work contains problematic and overly simplified evaluation methods, where different methods for generating adversarial examples are compared, even though they produce adversarial examples of differing perturbation magnitudes. This creates a biased evaluation environment, as higher perturbations yield naturally stronger adversarial examples. We propose a novel "accuracy-perturbation curve" that visualizes a classifiers classification accuracy response to adversarial examples of different perturbations. To demonstrate the utility of the curve we perform evaluation of responses of different image classifier architectures to four popular adversarial example methods. We also show how adversarial training improves the robustness of a classifier using the "accuracy-perturbation curve".

A Hybrid Metric Based on Persistent Homology and Its Application to Signal Classification

Austin Lawson, Yu-Min Chung, William Cruse

Auto-TLDR; Topological Data Analysis with Persistence Curves

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Topological Data Analysis (TDA) is a rising field in machine learning. TDA considers the shape of data set. Persistence diagrams, one of main tools in TDA, store topological information about the data. Persistence curves, a recently developed framework, provides a canonical and flexible way to encode the information presented in persistence diagrams into vectors. Based on persistence curves, we (1) provide new sets of features for time series, (2) prove that these features are robust to noise, (3) propose a hybrid metric that takes both geometric and topological information of the time series into account. Finally, we apply these metrics to the UCR Time Series Classification Archive. These empirical results show that our metrics perform better than the relevant benchmark in most cases.

Revisiting the Training of Very Deep Neural Networks without Skip Connections

Oyebade Kayode Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Bjorn Ottersten

Auto-TLDR; Optimization of Very Deep PlainNets without shortcut connections with 'vanishing and exploding units' activations'

Abstract Slides Poster Similar

Deep neural networks (DNNs) with many layers of feature representations yield state-of-the-art results on several difficult learning tasks. However, optimizing very deep DNNs without shortcut connections known as PlainNets, is a notoriously hard problem. Considering the growing interest in this area, this paper investigates holistically two scenarios that plague the training of very deep PlainNets: (1) the relatively popular challenge of 'vanishing and exploding units' activations', and (2) the less investigated 'singularity' problem, which is studied in details in this paper. In contrast to earlier works that study only the saturation and explosion of units' activations in isolation, this paper harmonizes the inconspicuous coexistence of the aforementioned problems for very deep PlainNets. Particularly, we argue that the aforementioned problems would have to be tackled simultaneously for the successful training of very deep PlainNets. Finally, different techniques that can be employed for tackling the optimization problem are discussed, and a specific combination of simple techniques that allows the successful training of PlainNets having up to 100 layers is demonstrated.

Stochastic Runge-Kutta Methods and Adaptive SGD-G2 Stochastic Gradient Descent

Gabriel Turinici, Imen Ayadi

Auto-TLDR; Adaptive Stochastic Runge Kutta for the Minimization of the Loss Function

Abstract Slides Poster Similar

The minimization of the loss function is of paramount importance in deep neural networks. Many popular optimization algorithms have been shown to correspond to some evolution equation of gradient flow type. Inspired by the numerical schemes used for general evolution equations, we introduce a second-order stochastic Runge Kutta method and show that it yields a consistent procedure for the minimization of the loss function. In addition, it can be coupled, in an adaptive framework, with the Stochastic Gradient Descent (SGD) to adjust automatically the learning rate of the SGD The resulting adaptive SGD, called SGD-G2, shows good results in terms of convergence speed when tested on standard data-sets.

F-Mixup: Attack CNNs from Fourier Perspective

Xiu-Chuan Li, Xu-Yao Zhang, Fei Yin, Cheng-Lin Liu

Auto-TLDR; F-Mixup: A novel black-box attack in frequency domain for deep neural networks

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Recent research has revealed that deep neural networks are highly vulnerable to adversarial examples. In this paper, different from most adversarial attacks which directly modify pixels in spatial domain, we propose a novel black-box attack in frequency domain, named as f-mixup, based on the property of natural images and perception disparity between human-visual system (HVS) and convolutional neural networks (CNNs): First, natural images tend to have the bulk of their Fourier spectrums concentrated on the low frequency domain; Second, HVS is much less sensitive to high frequencies while CNNs can utilize both low and high frequency information to make predictions. Extensive experiments are conducted and show that deeper CNNs tend to concentrate more on the high frequency domain, which may explain the contradiction between robustness and accuracy. In addition, we compared f-mixup with existing attack methods and observed that our approach possesses great advantages. Finally, we show that f-mixup can be also incorporated in training to make deep CNNs defensible against a kind of perturbations effectively.

Beyond Cross-Entropy: Learning Highly Separable Feature Distributions for Robust and Accurate Classification

Arslan Ali, Andrea Migliorati, Tiziano Bianchi, Enrico Magli

Auto-TLDR; Gaussian class-conditional simplex loss for adversarial robust multiclass classifiers

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Deep learning has shown outstanding performance in several applications including image classification. However, deep classifiers are known to be highly vulnerable to adversarial attacks, in that a minor perturbation of the input can easily lead to an error. Providing robustness to adversarial attacks is a very challenging task especially in problems involving a large number of classes, as it typically comes at the expense of an accuracy decrease. In this work, we propose the Gaussian class-conditional simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that provides adversarial robustness while at the same time achieving or even surpassing the classification accuracy of state-of-the-art methods. Differently from other frameworks, the proposed method learns a mapping of the input classes onto target distributions in a latent space such that the classes are linearly separable. Instead of maximizing the likelihood of target labels for individual samples, our objective function pushes the network to produce feature distributions yielding high inter-class separation. The mean values of the distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy and inherently provides robustness to multiple adversarial attacks, both targeted and untargeted, outperforming state-of-the-art approaches over challenging datasets.

Nearest Neighbor Classification Based on Activation Space of Convolutional Neural Network

Xinbo Ju, Shuo Shao, Huan Long, Weizhe Wang

Auto-TLDR; Convolutional Neural Network with Convex Hull Based Classifier

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In this paper, we propose a new image classifier based on the incorporation of the nearest neighbor algorithm and the activation space of convolutional neural network. The classifier has been successfully used on some state-of-the-art models and further improve their performance. Main technique tools we used are convex hull based classification and its acceleration. We find that 1) in several cases, the classifier can reach higher accuracy than original CNN; 2) by sampling, the classifier can work more efficiently; 3) centroid of each convex hull shows surprising ability in classification. Most of the work has strong geometry meanings, which helps us have a new understanding about convolutional layers.

Locality-Promoting Representation Learning

Johannes Schneider

Auto-TLDR; Locality-promoting Regularization for Neural Networks

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This work investigates questions related to learning features in convolutional neural networks (CNN). Empirical findings across multiple architectures such as VGG, ResNet, Inception and MobileNet indicate that weights near the center of a filter are larger than weights on the outside. Current regularization schemes violate this principle. Thus, we introduce Locality-promoting Regularization, which yields accuracy gains across multiple architectures and datasets. We also show theoretically that the empirical finding could be explained by maximizing feature cohesion under the assumption of spatial locality.

How to Define a Rejection Class Based on Model Learning?

Sarah Laroui, Xavier Descombes, Aurelia Vernay, Florent Villiers, Francois Villalba, Eric Debreuve

Auto-TLDR; An innovative learning strategy for supervised classification that is able, by design, to reject a sample as not belonging to any of the known classes

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In supervised classification, the learning process typically trains a classifier to optimize the accuracy of classifying data into the classes that appear in the learning set, and only them. While this framework fits many use cases, there are situations where the learning process is knowingly performed using a learning set that only represents the data that have been observed so far among a virtually unconstrained variety of possible samples. It is then crucial to define a classifier which has the ability to reject a sample, i.e., to classify it into a rejection class that has not been yet defined. Although obvious solutions can add this ability a posteriori to a classifier that has been learned classically, a better approach seems to directly account for this requirement in the classifier design. In this paper, we propose an innovative learning strategy for supervised classification that is able, by design, to reject a sample as not belonging to any of the known classes. For that, we rely on modeling each class as the combination of a probability density function (PDF) and a threshold that is computed with respect to the other classes. Several alternatives are proposed and compared in this framework. A comparison with straightforward approaches is also provided.

An Efficient Empirical Solver for Localized Multiple Kernel Learning Via DNNs

Ziming Zhang

Auto-TLDR; Localized Multiple Kernel Learning using LMKL-Net

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In this paper we propose solving localized multiple kernel learning (LMKL) using LMKL-Net, a feedforward deep neural network (DNN). In contrast to previous works, as a learning principle we propose parameterizing the gating function for learning kernel combination weights and the multiclass classifier using an attentional network (AN) and a multilayer perceptron (MLP), respectively. Such interpretability helps us better understand how the network solves the problem. Thanks to stochastic gradient descent (SGD), our approach has {\em linear} computational complexity in training. Empirically on benchmark datasets we demonstrate that with comparable or better accuracy than the state-of-the-art, our LMKL-Net can be trained about {\bf two orders of magnitude} faster with about {\bf two orders of magnitude} smaller memory footprint for large-scale learning.

Fractional Adaptation of Activation Functions in Neural Networks

Julio Zamora Esquivel, Jesus Adan Cruz Vargas, Paulo Lopez-Meyer, Hector Alfonso Cordourier Maruri, Jose Rodrigo Camacho Perez, Omesh Tickoo

Auto-TLDR; Automatic Selection of Activation Functions in Neural Networks using Fractional Calculus

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In this work, we introduce a generalization methodology for the automatic selection of the activation functions inside a neural network, taking advantage of concepts defined in fractional calculus. This methodology enables the neural network to search and optimize its own activation functions during the training process, by defining the fractional order of the derivative of a given primitive activation function. This fractional order is tuned as an additional training hyper-parameter a for intrafamily selection and b for cross family selection. By following this approach, the neurons inside the network can adjust their activation functions, e.g. from MLP to RBF networks, to best fit the input data, and reduce the output error. The experimental results obtained show the benefits of using this technique implemented on a ResNet18 topology, by outperforming the accuracy of a ResNet100 trained with CIFAR10 and Improving 1% ImageNet reported in the literature.

Optimal Transport As a Defense against Adversarial Attacks

Quentin Bouniot, Romaric Audigier, Angélique Loesch

Auto-TLDR; Sinkhorn Adversarial Training with Optimal Transport Theory

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Deep learning classifiers are now known to have flaws in the representations of their class. Adversarial attacks can find a human-imperceptible perturbation for a given image that will mislead a trained model. The most effective methods to defend against such attacks trains on generated adversarial examples to learn their distribution. Previous work aimed to align original and adversarial image representations in the same way as domain adaptation to improve robustness. Yet, they partially align the representations using approaches that do not reflect the geometry of space and distribution. In addition, it is difficult to accurately compare robustness between defended models. Until now, they have been evaluated using a fixed perturbation size. However, defended models may react differently to variations of this perturbation size. In this paper, the analogy of domain adaptation is taken a step further by exploiting optimal transport theory. We propose to use a loss between distributions that faithfully reflect the ground distance. This leads to SAT (Sinkhorn Adversarial Training), a more robust defense against adversarial attacks. Then, we propose to quantify more precisely the robustness of a model to adversarial attacks over a wide range of perturbation sizes using a different metric, the Area Under the Accuracy Curve (AUAC). We perform extensive experiments on both CIFAR-10 and CIFAR-100 datasets and show that our defense is globally more robust than the state-of-the-art.

Classification and Feature Selection Using a Primal-Dual Method and Projections on Structured Constraints

Michel Barlaud, Antonin Chambolle, Jean_Baptiste Caillau

Auto-TLDR; A Constrained Primal-dual Method for Structured Feature Selection on High Dimensional Data

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This paper deals with feature selection using supervised classification on high dimensional datasets. A classical approach is to project data on a low dimensional space and classify by minimizing an appropriate quadratic cost. Our first contribution is to introduce a matrix of centers in the definition of this cost. Moreover, as quadratic costs are not robust to outliers, we propose to use an $\ell_1$ cost instead (or Huber loss to mitigate overfitting issues). While control on sparsity is commonly obtained by adding an $\ell_1$ constraint on the vectorized matrix of weights used for projecting the data, our second contribution is to enforce structured sparsity. To this end we propose constraints that take into account the matrix structure of the data, based either on the nuclear norm, on the $\ell_{2,1}$ norm, or on the $\ell_{1,2}$ norm for which we provide a new projection algorithm. We optimize simultaneously the projection matrix and the matrix of centers thanks to a new tailored constrained primal-dual method. The primal-dual framework is general enough to encompass the various robust losses and structured constraints we use, and allows a convergence analysis. We demonstrate the effectiveness of the approach on three biological datasets. Our primal-dual method with robust losses, adaptive centers and structured constraints does significantly better than classical methods, both in terms of accuracy and computational time.

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

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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.

A New Convex Loss Function for Multiple Instance Support Vector Machines

Sang-Baeg Kim, Jung-Man Bae

Auto-TLDR; WR-SVM: A Novel Multiple Instance SVM for Video Classification

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We develop a novel multiple instance SVM based on maximizing the minimum witness rate (WR) among positive bags. We solve this nonlinear integer programming problem by the multiple instance SVM formulation with a convex loss function, where unknown integer labels of instances in positive bags are relaxed to real numbers between -1 and 1 using the tanh(.) to estimate WR of a positive bag. Our new model, WR-SVM, also can incorporates the multiple instance learning (MIL) problem into a simple deep neural network framework with no additional MIL pooling layers. WR-SVM is robust to input perturbation by eliminating the imbalance between positive instances allocated to positive bags. A better generalization ability is expected by the large margin due to the balanced allocation of positive instances to positive bags. We perform experiments on various video datasets to verify the effectiveness of our method for video classification. The results of WR-SVM outperform the state-of-the-art for MIL-based video classification models.

A Multilinear Sampling Algorithm to Estimate Shapley Values

Ramin Okhrati, Aldo Lipani

Auto-TLDR; A sampling method for Shapley values for multilayer Perceptrons

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Shapley values are great analytical tools in game theory to measure the importance of a player in a game. Due to their axiomatic and desirable properties such as efficiency, they have become popular for feature importance analysis in data science and machine learning. However, the time complexity to compute Shapley values based on the original formula is exponential, and as the number of features increases, this becomes infeasible. Castro et al. [1] developed a sampling algorithm, to estimate Shapley values. In this work, we propose a new sampling method based on a multilinear extension technique as applied in game theory. The aim is to provide a more efficient (sampling) method for estimating Shapley values. Our method is applicable to any machine learning model, in particular for either multiclass classifications or regression problems. We apply the method to estimate Shapley values for multilayer Perceptrons (MLPs) and through experimentation on two datasets, we demonstrate that our method provides more accurate estimations of the Shapley values by reducing the variance of the sampling statistics

GAN-Based Gaussian Mixture Model Responsibility Learning

Wanming Huang, Yi Da Xu, Shuai Jiang, Xuan Liang, Ian Oppermann

Auto-TLDR; Posterior Consistency Module for Gaussian Mixture Model

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Mixture Model (MM) is a probabilistic framework allows us to define dataset containing $K$ different modes. When each of the modes is associated with a Gaussian distribution, we refer to it as Gaussian MM or GMM. Given a data point $x$, a GMM may assume the existence of a random index $k \in \{1, \dots , K \}$ identifying which Gaussian the particular data is associated with. In a traditional GMM paradigm, it is straightforward to compute in closed-form, the conditional likelihood $p(x |k, \theta)$ as well as the responsibility probability $p(k|x, \theta)$ describing the distribution weights for each data. Computing the responsibility allows us to retrieve many important statistics of the overall dataset, including the weights of each of the modes/clusters. Modern large data-sets are often containing multiple unlabelled modes, such as paintings dataset may contain several styles; fashion images containing several unlabelled categories. In its raw representation, the Euclidean distances between the data (e.g., images) do not allow them to form mixtures naturally, nor it's feasible to compute responsibility distribution analytically, making GMM unable to apply. In this paper, we utilize the Generative Adversarial Network (GAN) framework to achieve a plausible alternative method to compute these probabilities. The key insight is that we compute them at the data's latent space $z$ instead of $x$. However, this process of $z \rightarrow x$ is irreversible under GAN which renders the computation of responsibility $p(k|x, \theta)$ infeasible. Our paper proposed a novel method to solve it by using a so-called Posterior Consistency Module (PCM). PCM acts like a GAN, except its Generator $C_{\text{PCM}}$ does not output the data, but instead it outputs a distribution to approximate $p(k|x, \theta)$. The entire network is trained in an ``end-to-end'' fashion. Trough these techniques, it allows us to model the dataset of very complex structure using GMM and subsequently to discover interesting properties of an unsupervised dataset, including its segments, as well as generating new ``out-distribution" data by smooth linear interpolation across any combinations of the modes in a completely unsupervised manner.

Uniform and Non-Uniform Sampling Methods for Sub-Linear Time K-Means Clustering

Yuanhang Ren, Ye Du

Auto-TLDR; Sub-linear Time Clustering with Constant Approximation Ratio for K-Means Problem

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The $k$-means problem is arguably the most well-known clustering problem in machine learning, and lots of approximation algorithms have been proposed for it. However, many of these algorithms may become infeasible when data is huge. Sub-linear time algorithms with constant approximation ratios are desirable in this scenario. In this paper, we first improve the analysis of the algorithm proposed by \cite{Mohan:2017:BNA:3172077.3172235} by sharpening the approximation ratio from $4(\alpha+\beta)$ to $\alpha+\beta$. Moreover, on mild assumptions of the data, a constant approximation ratio can be achieved in poly-logarithmic time by the algorithm. Furthermore, we propose a novel sub-linear time clustering algorithm called {\it Double-K-M$\text{C}^2$ sampling} as well. Experiments on the data clustering task and the image segmentation task have validated the effectiveness of our algorithms.

Region and Relations Based Multi Attention Network for Graph Classification

Manasvi Aggarwal, M. Narasimha Murty

Auto-TLDR; R2POOL: A Graph Pooling Layer for Non-euclidean Structures

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Graphs are non-euclidean structures that can represent many relational data efficiently. Many studies have proposed the convolution and the pooling operators on the non-euclidean domain. The graph convolution operators have shown astounding performance on various tasks such as node representation and classification. For graph classification, different pooling techniques are introduced, but none of them has considered both neighborhood of the node and the long-range dependencies of the node. In this paper, we propose a novel graph pooling layer R2POOL, which balances the structure information around the node as well as the dependencies with far away nodes. Further, we propose a new training strategy to learn coarse to fine representations. We add supervision at only intermediate levels to generate predictions using only intermediate-level features. For this, we propose the concept of an alignment score. Moreover, each layer's prediction is controlled by our proposed branch training strategy. This complete training helps in learning dominant class features at each layer for representing graphs. We call the combined model by R2MAN. Experiments show that R2MAN the potential to improve the performance of graph classification on various datasets.

Low-Cost Lipschitz-Independent Adaptive Importance Sampling of Stochastic Gradients

Huikang Liu, Xiaolu Wang, Jiajin Li, Man-Cho Anthony So

Auto-TLDR; Adaptive Importance Sampling for Stochastic Gradient Descent

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Stochastic gradient descent (SGD) usually samples training data based on the uniform distribution, which may not be a good choice because of the high variance of its stochastic gradient. Thus, importance sampling methods are considered in the literature to improve the performance. Most previous work on SGD-based methods with importance sampling requires the knowledge of Lipschitz constants of all component gradients, which are in general difficult to estimate. In this paper, we study an adaptive importance sampling method for common SGD-based methods by exploiting the local first-order information without knowing any Lipschitz constants. In particular, we periodically changes the sampling distribution by only utilizing the gradient norms in the past few iterations. We prove that our adaptive importance sampling non-asymptotically reduces the variance of the stochastic gradients in SGD, and thus better convergence bounds than that for vanilla SGD can be obtained. We extend this sampling method to several other widely used stochastic gradient algorithms including SGD with momentum and ADAM. Experiments on common convex learning problems and deep neural networks illustrate notably enhanced performance using the adaptive sampling strategy.

Learning Stable Deep Predictive Coding Networks with Weight Norm Supervision

Guo Ruohao

Auto-TLDR; Stability of Predictive Coding Network with Weight Norm Supervision

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Predictive Coding Network (PCN) is an important neural network inspired by visual processing models in neuroscience. It combines the feedforward and feedback processing and has the architecture of recurrent neural networks (RNNs). This type of network is usually trained with backpropagation through time (BPTT). With infinite recurrent steps, PCN is a dynamic system. However, as one of the most important properties, stability is rarely studied in this type of network. Inspired by reservoir computing, we investigate the stability of hierarchical RNNs from the perspective of dynamic systems, and propose a sufficient condition for their echo state property (ESP). Our study shows the global stability is determined by stability of the local layers and the feedback between neighboring layers. Based on it, we further propose Weight Norm Supervision, a new algorithm that controls the stability of PCN dynamics by imposing different weight norm constraints on different parts of the network. We compare our approach with other training methods in terms of stability and prediction capability. The experiments show that our algorithm learns stable PCNs with a reliable prediction precision in the most effective and controllable way.

Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution

Gary Shing Wee Goh, Sebastian Lapuschkin, Leander Weber, Wojciech Samek, Alexander Binder

Auto-TLDR; SmoothGrad: bridging Integrated Gradients and SmoothGrad from the Taylor's theorem perspective

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Integrated Gradients as an attribution method for deep neural network models offers simple implementability. However, it suffers from noisiness of explanations which affects the ease of interpretability. The SmoothGrad technique is proposed to solve the noisiness issue and smoothen the attribution maps of any gradient-based attribution method. In this paper, we present SmoothTaylor as a novel theoretical concept bridging Integrated Gradients and SmoothGrad, from the Taylor's theorem perspective. We apply the methods to the image classification problem, using the ILSVRC2012 ImageNet object recognition dataset, and a couple of pretrained image models to generate attribution maps. These attribution maps are empirically evaluated using quantitative measures for sensitivity and noise level. We further propose adaptive noising to optimize for the noise scale hyperparameter value. From our experiments, we find that the SmoothTaylor approach together with adaptive noising is able to generate better quality saliency maps with lesser noise and higher sensitivity to the relevant points in the input space as compared to Integrated Gradients.

Graph Approximations to Geodesics on Metric Graphs

Robin Vandaele, Yvan Saeys, Tijl De Bie

Auto-TLDR; Topological Pattern Recognition of Metric Graphs Using Proximity Graphs

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In machine learning, high-dimensional point clouds are often assumed to be sampled from a topological space of which the intrinsic dimension is significantly lower than the representation dimension. Proximity graphs, such as the Rips graph or kNN graph, are often used as an intermediate representation to learn or visualize topological and geometrical properties of this space. The key idea behind this approach is that distances on the graph preserve the geodesic distances on the unknown space well, and as such, can be used to infer local and global geometric patterns of this space. Prior results provide us with conditions under which these distances are well-preserved for geodesically convex, smooth, compact manifolds. Yet, proximity graphs are ideal representations for a much broader class of spaces, such as metric graphs, i.e., graphs embedded in the Euclidean space. It turns out—as proven in this paper—that these existing conditions cannot be straightforwardly adapted to these spaces. In this work, we provide novel, flexible, and insightful characteristics and results for topological pattern recognition of metric graphs to bridge this gap.

Computing Stable Resultant-Based Minimal Solvers by Hiding a Variable

Snehal Bhayani, Zuzana Kukelova, Janne Heikkilä

Auto-TLDR; Sparse Permian-Based Method for Solving Minimal Systems of Polynomial Equations

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Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e., solving minimal problems, in a RANSAC-style framework. Minimal problems often result in complex systems of polynomial equations. The existing state-of-the-art methods for solving such systems are either based on Groebner Basis and the action matrix method, which have been extensively studied and optimized in the recent years or recently proposed approach based on a resultant computation using an extra variable. In this paper, we study an interesting alternative resultant-based method for solving sparse systems of polynomial equations by hiding one variable. This approach results in a larger eigenvalue problem than the action matrix and extra variable resultant-based methods; however, it does not need to compute an inverse or elimination of large matrices that may be numerically unstable. The proposed approach includes several improvements to the standard sparse resultant algorithms, which significantly improves the efficiency and stability of the hidden variable resultant-based solvers as we demonstrate on several interesting computer vision problems. We show that for the studied problems, our sparse resultant based approach leads to more stable solvers than the state-of-the-art Groebner Basis as well as existing resultant-based solvers, especially in close to critical configurations. Our new method can be fully automated and incorporated into existing tools for the automatic generation of efficient minimal solvers.

Learning Sign-Constrained Support Vector Machines

Kenya Tajima, Kouhei Tsuchida, Esmeraldo Ronnie Rey Zara, Naoya Ohta, Tsuyoshi Kato

Auto-TLDR; Constrained Sign Constraints for Learning Linear Support Vector Machine

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Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop two optimization algorithms for minimizing the empirical risk under the sign constraints. One of the two algorithms is based on the projected gradient method, in which each iteration of the projected gradient method takes O(nd) computational cost and the sublinear convergence of the objective error is guaranteed. The second algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires O(nd) cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an epsilon-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.

Interpretable Structured Learning with Sparse Gated Sequence Encoder for Protein-Protein Interaction Prediction

Kishan K C, Feng Cui, Anne Haake, Rui Li

Auto-TLDR; Predicting Protein-Protein Interactions Using Sequence Representations

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Predicting protein-protein interactions (PPIs) by learning informative representations from amino acid sequences is a challenging yet important problem in biology. Although various deep learning models in Siamese architecture have been proposed to model PPIs from sequences, these methods are computationally expensive for a large number of PPIs due to the pairwise encoding process. Furthermore, these methods are difficult to interpret because of non-intuitive mappings from protein sequences to their sequence representation. To address these challenges, we present a novel deep framework to model and predict PPIs from sequence alone. Our model incorporates a bidirectional gated recurrent unit to learn sequence representations by leveraging contextualized and sequential information from sequences. We further employ a sparse regularization to model long-range dependencies between amino acids and to select important amino acids (protein motifs), thus enhancing interpretability. Besides, the novel design of the encoding process makes our model computationally efficient and scalable to an increasing number of interactions. Experimental results on up-to-date interaction datasets demonstrate that our model achieves superior performance compared to other state-of-the-art methods. Literature-based case studies illustrate the ability of our model to provide biological insights to interpret the predictions.

From Early Biological Models to CNNs: Do They Look Where Humans Look?

Marinella Iole Cadoni, Andrea Lagorio, Enrico Grosso, Jia Huei Tan, Chee Seng Chan

Auto-TLDR; Comparing Neural Networks to Human Fixations for Semantic Learning

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Early hierarchical computational visual models as well as recent deep neural networks have been inspired by the functioning of the primate visual cortex system. Although much effort has been made to dissect neural networks to visualize the features they learn at the individual units, the scope of the visualizations has been limited to a categorization of the features in terms of their semantic level. Considering the ability humans have to select high semantic level regions of a scene, the question whether neural networks can match this ability, and if similarity with humans attention is correlated with neural networks performance naturally arise. To address this question we propose a pipeline to select and compare sets of feature points that maximally activate individual networks units to human fixations. We extract features from a variety of neural networks, from early hierarchical models such as HMAX up to recent deep convolutional neural netwoks such as Densnet, to compare them to human fixations. Experiments over the ETD database show that human fixations correlate with CNNs features from deep layers significantly better than with random sets of points, while they do not with features extracted from the first layers of CNNs, nor with the HMAX features, which seem to have low semantic level compared with the features that respond to the automatically learned filters of CNNs. It also turns out that there is a correlation between CNN’s human similarity and classification performance.

Local Binary Quaternion Rotation Pattern for Colour Texture Retrieval

Hela Jebali, Noel Richard, Mohamed Naouai

Auto-TLDR; Local Binary Quaternion Rotation Pattern for Color Texture Classification

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Color is very important feature for image representation, it assumes very essential in the human visual recognition process. Most existing approaches usually extract features from the three color channels separately (Marginal way). Although, it exists few vector expressions of texture features. Aware of the high interaction that exists between different channels in the color image, this work introduces a compact texture descriptor, named Local Binary Quaternion Rotation Pattern (LBQRP). In this LBQRP purpose, the quaternion representation is used to represent color texture. The distance between two color can be expressed as the angle of rotation between two unit quaternions using the geodesic distance. After a LBQRP coding, local histograms are extracted and used as features. Experiments on three challenging color datasets: Vistex, Outex-TC13 and USPtex are carried out to evaluate the LBQRP performance in texture classification. Results show the high efficiency of the proposed approach facing to several stat-of-art methods.