Stochastic Runge-Kutta Methods and Adaptive SGD-G2 Stochastic Gradient Descent
![Responsive image](/icpr/media/video_thumbnails/11872.jpg)
Auto-TLDR; Adaptive Stochastic Runge Kutta for the Minimization of the Loss Function
Similar papers
Low-Cost Lipschitz-Independent Adaptive Importance Sampling of Stochastic Gradients
Huikang Liu, Xiaolu Wang, Jiajin Li, Man-Cho Anthony So
![Responsive image](/icpr/media/video_thumbnails/11106.jpg)
Auto-TLDR; Adaptive Importance Sampling for Stochastic Gradient Descent
RNN Training along Locally Optimal Trajectories via Frank-Wolfe Algorithm
Yun Yue, Ming Li, Venkatesh Saligrama, Ziming Zhang
![Responsive image](/icpr/media/video_thumbnails/12161.jpg)
Auto-TLDR; Frank-Wolfe Algorithm for Efficient Training of RNNs
Abstract Slides Poster Similar
Unveiling Groups of Related Tasks in Multi-Task Learning
Jordan Frecon, Saverio Salzo, Massimiliano Pontil
![Responsive image](/icpr/media/video_thumbnails/11735.jpg)
Auto-TLDR; Continuous Bilevel Optimization for Multi-Task Learning
Abstract Slides Poster Similar
A Multilinear Sampling Algorithm to Estimate Shapley Values
![Responsive image](/icpr/media/video_thumbnails/11843.jpg)
Auto-TLDR; A sampling method for Shapley values for multilayer Perceptrons
Abstract Slides Poster Similar
Bayesian Active Learning for Maximal Information Gain on Model Parameters
Kasra Arnavaz, Aasa Feragen, Oswin Krause, Marco Loog
![Responsive image](/icpr/media/video_thumbnails/12160.jpg)
Auto-TLDR; Bayesian assumptions for Bayesian classification
Abstract Slides Poster Similar
Learning Sign-Constrained Support Vector Machines
Kenya Tajima, Kouhei Tsuchida, Esmeraldo Ronnie Rey Zara, Naoya Ohta, Tsuyoshi Kato
![Responsive image](/icpr/media/video_thumbnails/11245.jpg)
Auto-TLDR; Constrained Sign Constraints for Learning Linear Support Vector Machine
Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks
Denis Huseljic, Bernhard Sick, Marek Herde, Daniel Kottke
![Responsive image](/icpr/media/video_thumbnails/11987.jpg)
Auto-TLDR; AE-DNN: Modeling Uncertainty in Deep Neural Networks
Abstract Slides Poster Similar
Dimensionality Reduction for Data Visualization and Linear Classification, and the Trade-Off between Robustness and Classification Accuracy
Martin Becker, Jens Lippel, Thomas Zielke
![Responsive image](/icpr/media/video_thumbnails/11652.jpg)
Auto-TLDR; Robustness Assessment of Deep Autoencoder for Data Visualization using Scatter Plots
Abstract Slides Poster Similar
Learning Sparse Deep Neural Networks Using Efficient Structured Projections on Convex Constraints for Green AI
Michel Barlaud, Frederic Guyard
![Responsive image](/icpr/media/thumbnails/0577_FI.pdf.jpg)
Auto-TLDR; Constrained Deep Neural Network with Constrained Splitting Projection
Abstract Slides Poster Similar
Learning Stable Deep Predictive Coding Networks with Weight Norm Supervision
![Responsive image](/icpr/media/video_thumbnails/12169.jpg)
Auto-TLDR; Stability of Predictive Coding Network with Weight Norm Supervision
Abstract Slides Poster Similar
Classification and Feature Selection Using a Primal-Dual Method and Projections on Structured Constraints
Michel Barlaud, Antonin Chambolle, Jean_Baptiste Caillau
![Responsive image](/icpr/media/thumbnails/1797_FI.pdf.jpg)
Auto-TLDR; A Constrained Primal-dual Method for Structured Feature Selection on High Dimensional Data
Abstract Slides Poster Similar
Mean Decision Rules Method with Smart Sampling for Fast Large-Scale Binary SVM Classification
Alexandra Makarova, Mikhail Kurbakov, Valentina Sulimova
![Responsive image](/icpr/media/video_thumbnails/11871.jpg)
Auto-TLDR; Improving Mean Decision Rule for Large-Scale Binary SVM Problems
Abstract Slides Poster Similar
Can Data Placement Be Effective for Neural Networks Classification Tasks? Introducing the Orthogonal Loss
Brais Cancela, Veronica Bolon-Canedo, Amparo Alonso-Betanzos
![Responsive image](/icpr/media/video_thumbnails/10880.jpg)
Auto-TLDR; Spatial Placement for Neural Network Training Loss Functions
Abstract Slides Poster Similar
Generalization Comparison of Deep Neural Networks Via Output Sensitivity
Mahsa Forouzesh, Farnood Salehi, Patrick Thiran
![Responsive image](/icpr/media/video_thumbnails/11769.jpg)
Auto-TLDR; Generalization of Deep Neural Networks using Sensitivity
Speeding-Up Pruning for Artificial Neural Networks: Introducing Accelerated Iterative Magnitude Pruning
Marco Zullich, Eric Medvet, Felice Andrea Pellegrino, Alessio Ansuini
![Responsive image](/icpr/media/video_thumbnails/11320.jpg)
Auto-TLDR; Iterative Pruning of Artificial Neural Networks with Overparametrization
Abstract Slides Poster Similar
Improving Batch Normalization with Skewness Reduction for Deep Neural Networks
Pak Lun Kevin Ding, Martin Sarah, Baoxin Li
![Responsive image](/icpr/media/video_thumbnails/11739.jpg)
Auto-TLDR; Batch Normalization with Skewness Reduction
Abstract Slides Poster Similar
Energy Minimum Regularization in Continual Learning
![Responsive image](/icpr/media/video_thumbnails/11642.jpg)
Auto-TLDR; Energy Minimization Regularization for Continuous Learning
Improved Time-Series Clustering with UMAP Dimension Reduction Method
Clément Pealat, Vincent Cheutet, Guillaume Bouleux
![Responsive image](/icpr/media/video_thumbnails/11549.jpg)
Auto-TLDR; Time Series Clustering with UMAP as a Pre-processing Step
Abstract Slides Poster Similar
Hcore-Init: Neural Network Initialization Based on Graph Degeneracy
Stratis Limnios, George Dasoulas, Dimitrios Thilikos, Michalis Vazirgiannis
![Responsive image](/icpr/media/video_thumbnails/11573.jpg)
Auto-TLDR; K-hypercore: Graph Mining for Deep Neural Networks
Abstract Slides Poster Similar
A Close Look at Deep Learning with Small Data
![Responsive image](/icpr/media/video_thumbnails/11148.jpg)
Auto-TLDR; Low-Complex Neural Networks for Small Data Conditions
Abstract Slides Poster Similar
Interpolation in Auto Encoders with Bridge Processes
Carl Ringqvist, Henrik Hult, Judith Butepage, Hedvig Kjellstrom
![Responsive image](/icpr/media/video_thumbnails/11588.jpg)
Auto-TLDR; Stochastic interpolations from auto encoders trained on flattened sequences
Abstract Slides Poster Similar
Auto Encoding Explanatory Examples with Stochastic Paths
Cesar Ali Ojeda Marin, Ramses J. Sanchez, Kostadin Cvejoski, Bogdan Georgiev
![Responsive image](/icpr/media/video_thumbnails/11620.jpg)
Auto-TLDR; Semantic Stochastic Path: Explaining a Classifier's Decision Making Process using latent codes
Abstract Slides Poster Similar
ResNet-Like Architecture with Low Hardware Requirements
Elena Limonova, Daniil Alfonso, Dmitry Nikolaev, Vladimir V. Arlazarov
![Responsive image](/icpr/media/video_thumbnails/11618.jpg)
Auto-TLDR; BM-ResNet: Bipolar Morphological ResNet for Image Classification
Abstract Slides Poster Similar
Towards Explaining Adversarial Examples Phenomenon in Artificial Neural Networks
Ramin Barati, Reza Safabakhsh, Mohammad Rahmati
![Responsive image](/icpr/media/video_thumbnails/11723.jpg)
Auto-TLDR; Convolutional Neural Networks and Adversarial Training from the Perspective of convergence
Abstract Slides Poster Similar
An Efficient Empirical Solver for Localized Multiple Kernel Learning Via DNNs
![Responsive image](/icpr/media/video_thumbnails/10913.jpg)
Auto-TLDR; Localized Multiple Kernel Learning using LMKL-Net
Abstract Slides Poster Similar
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?
![Responsive image](/icpr/media/video_thumbnails/10847.jpg)
Auto-TLDR; Meta-learning Based Training of Deep Neural Networks for Few-Shot Learning
Abstract Slides Poster Similar
Exploiting Non-Linear Redundancy for Neural Model Compression
Muhammad Ahmed Shah, Raphael Olivier, Bhiksha Raj
![Responsive image](/icpr/media/video_thumbnails/12085.jpg)
Auto-TLDR; Compressing Deep Neural Networks with Linear Dependency
Abstract Slides Poster Similar
Uniform and Non-Uniform Sampling Methods for Sub-Linear Time K-Means Clustering
![Responsive image](/icpr/media/video_thumbnails/11816.jpg)
Auto-TLDR; Sub-linear Time Clustering with Constant Approximation Ratio for K-Means Problem
Abstract Slides Poster Similar
3CS Algorithm for Efficient Gaussian Process Model Retrieval
Fabian Berns, Kjeld Schmidt, Ingolf Bracht, Christian Beecks
![Responsive image](/icpr/media/video_thumbnails/11058.jpg)
Auto-TLDR; Efficient retrieval of Gaussian Process Models for large-scale data using divide-&-conquer-based approach
Abstract Slides Poster Similar
Norm Loss: An Efficient yet Effective Regularization Method for Deep Neural Networks
Theodoros Georgiou, Sebastian Schmitt, Thomas Baeck, Wei Chen, Michael Lew
![Responsive image](/icpr/media/video_thumbnails/11942.jpg)
Auto-TLDR; Weight Soft-Regularization with Oblique Manifold for Convolutional Neural Network Training
Abstract Slides Poster Similar
Probability Guided Maxout
Claudio Ferrari, Stefano Berretti, Alberto Del Bimbo
![Responsive image](/icpr/media/video_thumbnails/11657.jpg)
Auto-TLDR; Probability Guided Maxout for CNN Training
Abstract Slides Poster Similar
A Randomized Algorithm for Sparse Recovery
Huiyuan Yu, Maggie Cheng, Yingdong Lu
![Responsive image](/icpr/media/thumbnails/2279_FI.pdf.jpg)
Auto-TLDR; A Constrained Graph Optimization Algorithm for Sparse Signal Recovery
Meta Soft Label Generation for Noisy Labels
![Responsive image](/icpr/media/video_thumbnails/11736.jpg)
Auto-TLDR; MSLG: Meta-Learning for Noisy Label Generation
Abstract Slides Poster Similar
Feature Extraction and Selection Via Robust Discriminant Analysis and Class Sparsity
![Responsive image](/icpr/media/video_thumbnails/11751.jpg)
Auto-TLDR; Hybrid Linear Discriminant Embedding for supervised multi-class classification
Abstract Slides Poster Similar
Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning
Beomjo Shin, Junsu Cho, Hwanjo Yu, Seungjin Choi
![Responsive image](/icpr/media/video_thumbnails/11349.jpg)
Auto-TLDR; Improving Attention-based Deep Multiple Instance Learning for Key Instance Detection (KID)
Abstract Slides Poster Similar
Deep Transformation Models: Tackling Complex Regression Problems with Neural Network Based Transformation Models
Beate Sick, Torsten Hothorn, Oliver Dürr
![Responsive image](/icpr/media/video_thumbnails/11146.jpg)
Auto-TLDR; A Deep Transformation Model for Probabilistic Regression
Abstract Slides Poster Similar
Generative Latent Implicit Conditional Optimization When Learning from Small Sample
![Responsive image](/icpr/media/video_thumbnails/11915.jpg)
Auto-TLDR; GLICO: Generative Latent Implicit Conditional Optimization for Small Sample Learning
Abstract Slides Poster Similar
Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification
![Responsive image](/icpr/media/video_thumbnails/11262.jpg)
Auto-TLDR; Adaptive Optimization for Black-Box Multi-Objective Optimizing Problems with Binary Constraints
Naturally Constrained Online Expectation Maximization
Daniela Pamplona, Antoine Manzanera
![Responsive image](/icpr/media/video_thumbnails/11521.jpg)
Auto-TLDR; Constrained Online Expectation-Maximization for Probabilistic Principal Components Analysis
Abstract Slides Poster Similar
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
![Responsive image](/icpr/media/video_thumbnails/11787.jpg)
Auto-TLDR; Automatic Selection of Activation Functions in Neural Networks using Fractional Calculus
Abstract Slides Poster Similar
Boundary Optimised Samples Training for Detecting Out-Of-Distribution Images
Luca Marson, Vladimir Li, Atsuto Maki
![Responsive image](/icpr/media/video_thumbnails/12155.jpg)
Auto-TLDR; Boundary Optimised Samples for Out-of-Distribution Input Detection in Deep Convolutional Networks
Abstract Slides Poster Similar
Neuron-Based Network Pruning Based on Majority Voting
Ali Alqahtani, Xianghua Xie, Ehab Essa, Mark W. Jones
![Responsive image](/icpr/media/video_thumbnails/11223.jpg)
Auto-TLDR; Large-Scale Neural Network Pruning using Majority Voting
Abstract Slides Poster Similar
Quantifying Model Uncertainty in Inverse Problems Via Bayesian Deep Gradient Descent
Riccardo Barbano, Chen Zhang, Simon Arridge, Bangti Jin
![Responsive image](/icpr/media/video_thumbnails/11008.jpg)
Auto-TLDR; Bayesian Neural Networks for Inverse Reconstruction via Bayesian Knowledge-Aided Computation
Abstract Slides Poster Similar
Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution
Gary Shing Wee Goh, Sebastian Lapuschkin, Leander Weber, Wojciech Samek, Alexander Binder
![Responsive image](/icpr/media/video_thumbnails/12604.jpg)
Auto-TLDR; SmoothGrad: bridging Integrated Gradients and SmoothGrad from the Taylor's theorem perspective
Supervised Domain Adaptation Using Graph Embedding
Lukas Hedegaard, Omar Ali Sheikh-Omar, Alexandros Iosifidis
![Responsive image](/icpr/media/video_thumbnails/11825.jpg)
Auto-TLDR; Domain Adaptation from the Perspective of Multi-view Graph Embedding and Dimensionality Reduction
Abstract Slides Poster Similar
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks
Sebastian Palacio, Philipp Engler, Jörn Hees, Andreas Dengel
![Responsive image](/icpr/media/video_thumbnails/11958.jpg)
Auto-TLDR; Self-Supervised Autogenous Learning for Deep Neural Networks
Abstract Slides Poster Similar
Killing Four Birds with One Gaussian Process: The Relation between Different Test-Time Attacks
Kathrin Grosse, Michael Thomas Smith, Michael Backes
![Responsive image](/icpr/media/video_thumbnails/11427.jpg)
Auto-TLDR; Security of Gaussian Process Classifiers against Attack Algorithms
Abstract Slides Poster Similar
Progressive Gradient Pruning for Classification, Detection and Domain Adaptation
Le Thanh Nguyen-Meidine, Eric Granger, Marco Pedersoli, Madhu Kiran, Louis-Antoine Blais-Morin
![Responsive image](/icpr/media/video_thumbnails/11187.jpg)
Auto-TLDR; Progressive Gradient Pruning for Iterative Filter Pruning of Convolutional Neural Networks
Abstract Slides Poster Similar