General Keywords

[ Algorithms ] [ Algorithms; Optimization ] [ Applications ] [ Data, Challenges, Implementations, and Software ] [ Deep Learning ] [ Deep Learning; Deep Learning ] [ Neuroscience and Cognitive Science ] [ Optimization ] [ Optimization; Optimization ] [ Probabilistic Methods ] [ Probabilistic Methods; Probabilistic Methods ] [ Reinforcement Learning and Planning ] [ Social Aspects of Machine Learning ] [ Theory ] [ Theory; Theory ]

Topic Keywords

[ Active Learning ] [ Active Learning; Algorithms ] [ Activity and Event Recognition ] [ Adaptive Data Analysis; Optimization ] [ Adversarial Examples ] [ Adversarial Learning ] [ Adversarial Learning; Algorithms ] [ Adversarial Networks ] [ Adversarial Networks ] [ Adversarial Networks; Deep Learning ] [ Adversarial Networks; Deep Learning ] [ AI Safety ] [ Algorithms Evaluation ] [ Approximate Inference ] [ Architectures ] [ Attention Models ] [ Audio and Speech Processing ] [ AutoML ] [ Bandit Algorithms ] [ Bandit Algorithms; Algorithms ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandits ] [ Bayesian Deep Learning ] [ Bayesian Methods ] [ Bayesian Nonparametrics ] [ Bayesian Theory ] [ Bayesian Theory ] [ Benchmarks ] [ Biologically Plausible Deep Networks ] [ Biologically Plausible Deep Networks; Deep Learning ] [ Biologically Plausible Deep Networks; Neuroscience and Cognitive Science ] [ Body Pose, Face, and Gesture Analysis ] [ Body Pose, Face, and Gesture Analysis; Applications ] [ Boosting and Ensemble Methods ] [ Boosting and Ensemble Methods; Algorithms ] [ Boosting and Ensemble Methods; Probabilistic Methods; Probabilistic Methods ] [ Causal Inference ] [ Classification ] [ Classification; Algorithms ] [ Classification; Algorithms ] [ Classification; Applications ] [ Classification; Deep Learning; Deep Learning ] [ Classification; Deep Learning; Deep Learning ] [ Clustering ] [ Clustering; Applications ] [ Clustering; Theory ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Theory ] [ Cognitive Science; Neuroscience and Cognitive Science ] [ Collaborative Filtering ] [ Collaborative Filtering; Algorithms ] [ Collaborative Filtering; Applications ] [ Combinatorial Optimization ] [ Components Analysis (e.g., CCA, ICA, LDA, PCA) ] [ Computational Biology and Bioinformatics ] [ Computational Biology and Bioinformatics; Applications ] [ Computational Complexity ] [ Computational Learning Theory ] [ Computational Photography ] [ Computational Social Science ] [ Computer Vision ] [ Computer Vision; Applications ] [ Computer Vision; Applications ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Continual Learning ] [ Convex Optimization ] [ Convex Optimization; Optimization ] [ Convex Optimization; Probabilistic Methods; Theory; Theory ] [ Convex Optimization; Theory ] [ Crowdsourcing ] [ Decision and Control ] [ Deep Autoencoders; Deep Learning ] [ Deep learning Theory ] [ Deep RL ] [ Density Estimation ] [ Density Estimation; Deep Learning ] [ Derivative Free Optimization ] [ Dialog- or Communication-Based Learning ] [ Dimensionality Reduction ] [ Distributed and Parallel Optimization ] [ Distributed Inference ] [ Efficient Inference Methods ] [ Efficient Training Methods; Deep Learning ] [ Embedding and Representation learning ] [ Embedding Approaches ] [ Exploration ] [ Fairness, Accountability, and Transparency ] [ Fairness, Accountability, and Transparency ] [ Few-Shot Learning ] [ Few-Shot Learning; Algorithms ] [ Frequentist Statistics ] [ Game Theory and Computational Economics ] [ Gaussian Processes ] [ Gaussian Processes and Bayesian non-parametrics ] [ Generative Models ] [ Generative Models ] [ Graphical Models ] [ Graphical Models ] [ Hardware and Systems ] [ Healthcare ] [ Human or Animal Learning ] [ Human or Animal Learning; Probabilistic Methods ] [ Image Segmentation ] [ Image Segmentation; Algorithms ] [ Image Segmentation; Applications ] [ Information Theory ] [ Kernel Methods ] [ Kernel Methods; Optimization ] [ Large Deviations and Asymptotic Analysis ] [ Large Scale Learning ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Applications ] [ Large Scale Learning; Deep Learning ] [ Large Scale Learning; Probabilistic Methods ] [ Latent Variable Models ] [ Learning Theory ] [ Markov Decision Processes ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Matrix and Tensor Factorization ] [ MCMC ] [ Memory ] [ Memory; Optimization ] [ Meta-Learning ] [ Meta-Learning; Applications ] [ Metric Learning ] [ Missing Data; Algorithms ] [ Missing Data; Algorithms ] [ Missing Data; Theory ] [ Model Selection and Structure Learning ] [ Models of Learning and Generalization ] [ Monte Carlo Methods ] [ Multi-Agent RL ] [ Multimodal Learning ] [ Multitask and Transfer Learning ] [ Multitask and Transfer Learning; Algorithms ] [ Multitask and Transfer Learning; Probabilistic Methods ] [ Multitask, Transfer, and Meta Learning ] [ Natural Language Processing ] [ Network Analysis ] [ Networks and Relational Learning ] [ Neural Coding; Neuroscience and Cognitive Science ] [ Neuroscience ] [ Neuroscience and Cognitive Science ] [ Non-Convex Optimization ] [ Non-Convex Optimization ] [ Non-Convex Optimization; Theory ] [ Non-parametric models ] [ Object Detection; Deep Learning ] [ Object Detection; Neuroscience and Cognitive Science ] [ Online Learning ] [ Online Learning Algorithms ] [ Online Learning Theory ] [ Online Learning; Theory ] [ Optimal Transport ] [ Optimization for Deep Networks ] [ Others ] [ Others ] [ Others ] [ Others ] [ Others ] [ Planning and Control ] [ Plasticity and Adaptation ] [ Predictive Models ] [ Predictive Models; Deep Learning ] [ Predictive Models; Deep Learning ] [ Privacy, Anonymity, and Security ] [ Privacy, Anonymity, and Security ] [ Probabilistic Methods ] [ Probabilistic Programming ] [ Program Understanding and Generation ] [ Quantitative Finance and Econometrics ] [ Ranking and Preference Learning ] [ Ranking and Preference Learning; Theory ] [ Reasoning; Optimization ] [ Recommender Systems ] [ Recurrent Networks ] [ Recurrent Networks; Theory ] [ Regression ] [ Regression; Algorithms ] [ Regression; Applications ] [ Regression; Optimization ] [ Regression; Probabilistic Methods; Probabilistic Methods ] [ Regularization ] [ Regularization ] [ Reinforcement Learning ] [ Reinforcement Learning and Planning ] [ Relational Learning ] [ Representation Learning ] [ Representation Learning; Algorithms ] [ Representation Learning; Algorithms ] [ Representation Learning; Neuroscience and Cognitive Science ] [ Representation Learning; Neuroscience and Cognitive Science; Neuroscience and Cognitive Science ] [ Representation Learning; Optimization ] [ RL, Decisions and Control Theory ] [ Robotics ] [ Robust statistics ] [ Semi-Supervised Learning ] [ Social Aspects of Machine Learning ] [ Software Toolkits ] [ Spaces of Functions and Kernels ] [ Sparse Coding and Dimensionality Expansion; Applications ] [ Sparsity and Compressed Sensing ] [ Sparsity and Compressed Sensing; Applications ] [ Sparsity and Compressed Sensing; Optimization; Theory ] [ Speech Recognition ] [ Statistical Learning Theory ] [ Statistical Physics of Learning ] [ Stochastic Optimization ] [ Structured Prediction ] [ Submodular Optimization ] [ Supervised Learning ] [ Sustainability and Environment ] [ Theory ] [ Time Series Analysis ] [ Time Series Analysis; Deep Learning ] [ Time Series Analysis; Probabilistic Methods; Probabilistic Methods ] [ Time Series and Sequences ] [ Topic Models ] [ Uncertainty Estimation ] [ Uncertainty Estimation; Applications; Probabilistic Methods ] [ Unsupervised Learning ] [ Unsupervised Learning; Applications ] [ Unsupervised Learning; Deep Learning ] [ Variational Inference ] [ Visualization or Exposition Techniques for Deep Networks ] [ Visual Question Answering ] [ Visual Scene Analysis and Interpretation ]

105 Results

Spotlight
Tue 5:35 Preferential Temporal Difference Learning
Nishanth Anand, Doina Precup
Oral
Tue 6:00 Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris Maddison
Spotlight
Tue 6:20 Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
Shumao Zhang, Pengchuan Zhang, Thomas Hou
Spotlight
Tue 6:25 GraphDF: A Discrete Flow Model for Molecular Graph Generation
Youzhi Luo, Keqiang Yan, Shuiwang Ji
Spotlight
Tue 6:30 Hierarchical VAEs Know What They Don’t Know
Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe
Spotlight
Tue 6:35 Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu, Francisco R Ruiz, Liping Liu
Spotlight
Tue 6:40 Self Normalizing Flows
T. Anderson Keller, Jorn Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling
Spotlight
Tue 6:40 Generative Video Transformer: Can Objects be the Words?
Yi-Fu Wu, Jaesik Yoon, Sungjin Ahn
Spotlight
Tue 6:45 Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks
Xuhui Fan, Bin Li, Yaqiong Li, Scott SIsson
Oral
Tue 7:00 Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders
Adeel Pervez, Efstratios Gavves
Spotlight
Tue 7:20 Riemannian Convex Potential Maps
samuel cohen, Brandon Amos, Yaron Lipman
Spotlight
Tue 7:25 Autoencoding Under Normalization Constraints
Sangwoong Yoon, Yung-Kyun Noh, Frank Chongwoo Park
Spotlight
Tue 7:30 PixelTransformer: Sample Conditioned Signal Generation
Shubham Tulsiani, Abhinav Gupta
Spotlight
Tue 7:35 Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation
Sung Woo Park, Dong Wook Shu, Junseok Kwon
Spotlight
Tue 7:35 MSA Transformer
Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, Alexander Rives
Spotlight
Tue 7:40 Autoencoder Image Interpolation by Shaping the Latent Space
Alon Oring, Zohar Yakhini, Yacov Hel-Or
Spotlight
Tue 7:40 Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond
Dennis Wei
Spotlight
Tue 7:45 Improved Denoising Diffusion Probabilistic Models
Alexander Nichol, Prafulla Dhariwal
Poster
Tue 9:00 NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Adam Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokra, Danilo J. Rezende
Poster
Tue 9:00 PixelTransformer: Sample Conditioned Signal Generation
Shubham Tulsiani, Abhinav Gupta
Poster
Tue 9:00 Autoencoder Image Interpolation by Shaping the Latent Space
Alon Oring, Zohar Yakhini, Yacov Hel-Or
Poster
Tue 9:00 Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference
Shumao Zhang, Pengchuan Zhang, Thomas Hou
Poster
Tue 9:00 Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
Xiaohui Chen, Xu Han, Jiajing Hu, Francisco R Ruiz, Liping Liu
Poster
Tue 9:00 Riemannian Convex Potential Maps
samuel cohen, Brandon Amos, Yaron Lipman
Poster
Tue 9:00 Autoencoding Under Normalization Constraints
Sangwoong Yoon, Yung-Kyun Noh, Frank Chongwoo Park
Poster
Tue 9:00 Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris Maddison
Poster
Tue 9:00 Spectral Smoothing Unveils Phase Transitions in Hierarchical Variational Autoencoders
Adeel Pervez, Efstratios Gavves
Poster
Tue 9:00 Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks
Xuhui Fan, Bin Li, Yaqiong Li, Scott SIsson
Poster
Tue 9:00 Improved Denoising Diffusion Probabilistic Models
Alexander Nichol, Prafulla Dhariwal
Poster
Tue 9:00 Hierarchical VAEs Know What They Don’t Know
Jakob D. Havtorn, Jes Frellsen, Søren Hauberg, Lars Maaløe
Poster
Tue 9:00 MSA Transformer
Roshan Rao, Jason Liu, Robert Verkuil, Joshua Meier, John Canny, Pieter Abbeel, Tom Sercu, Alexander Rives
Poster
Tue 9:00 Generative Video Transformer: Can Objects be the Words?
Yi-Fu Wu, Jaesik Yoon, Sungjin Ahn
Poster
Tue 9:00 Decision-Making Under Selective Labels: Optimal Finite-Domain Policies and Beyond
Dennis Wei
Poster
Tue 9:00 Preferential Temporal Difference Learning
Nishanth Anand, Doina Precup
Poster
Tue 9:00 GraphDF: A Discrete Flow Model for Molecular Graph Generation
Youzhi Luo, Keqiang Yan, Shuiwang Ji
Poster
Tue 9:00 Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation
Sung Woo Park, Dong Wook Shu, Junseok Kwon
Poster
Tue 9:00 Self Normalizing Flows
T. Anderson Keller, Jorn Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling
Oral
Tue 17:00 NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Adam Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokra, Danilo J. Rezende
Spotlight
Tue 17:20 Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
Akira Nakagawa, Keizo Kato, Taiji Suzuki
Spotlight
Tue 17:25 The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression
Florian List
Spotlight
Tue 17:25 Soft then Hard: Rethinking the Quantization in Neural Image Compression
Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen
Spotlight
Tue 17:25 Quantization Algorithms for Random Fourier Features
Xiaoyun Li, Ping Li
Spotlight
Tue 17:30 Improved Contrastive Divergence Training of Energy-Based Models
Yilun Du, Shuang Li, Josh Tenenbaum, Igor Mordatch
Spotlight
Tue 17:35 Deep Generative Learning via Schrödinger Bridge
Gefei Wang, Yuling Jiao, Qian Xu, Yang Wang, Can Yang
Spotlight
Tue 17:40 Partially Observed Exchangeable Modeling
Yang Li, Junier Oliva
Spotlight
Tue 17:45 Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Lily Zhang, Mark Goldstein, Rajesh Ranganath
Oral
Tue 18:00 Generating images with sparse representations
Charlie Nash, Jacob Menick, Sander Dieleman, Peter Battaglia
Spotlight
Tue 18:20 An Identifiable Double VAE For Disentangled Representations
Graziano Mita, Maurizio Filippone, Pietro Michiardi
Spotlight
Tue 18:25 A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention
Tomoki Watanabe, Paolo Favaro
Spotlight
Tue 18:30 On Characterizing GAN Convergence Through Proximal Duality Gap
Sahil Sidheekh, Aroof Aimen, Narayanan Chatapuram Krishnan
Spotlight
Tue 18:35 Scalable Normalizing Flows for Permutation Invariant Densities
Marin Biloš, Stephan Günnemann
Spotlight
Tue 18:40 Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
Vivek Jayaram, John Thickstun
Spotlight
Tue 18:45 Zero-Shot Text-to-Image Generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
Spotlight
Tue 19:40 RRL: Resnet as representation for Reinforcement Learning
Rutav Shah, Vikash Kumar
Poster
Tue 21:00 Quantization Algorithms for Random Fourier Features
Xiaoyun Li, Ping Li
Poster
Tue 21:00 Understanding Failures in Out-of-Distribution Detection with Deep Generative Models
Lily Zhang, Mark Goldstein, Rajesh Ranganath
Poster
Tue 21:00 Generating images with sparse representations
Charlie Nash, Jacob Menick, Sander Dieleman, Peter Battaglia
Poster
Tue 21:00 Zero-Shot Text-to-Image Generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, Ilya Sutskever
Poster
Tue 21:00 A Unified Generative Adversarial Network Training via Self-Labeling and Self-Attention
Tomoki Watanabe, Paolo Favaro
Poster
Tue 21:00 Partially Observed Exchangeable Modeling
Yang Li, Junier Oliva
Poster
Tue 21:00 Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics
Vivek Jayaram, John Thickstun
Poster
Tue 21:00 The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression
Florian List
Poster
Tue 21:00 Deep Generative Learning via Schrödinger Bridge
Gefei Wang, Yuling Jiao, Qian Xu, Yang Wang, Can Yang
Poster
Tue 21:00 Improved Contrastive Divergence Training of Energy-Based Models
Yilun Du, Shuang Li, Josh Tenenbaum, Igor Mordatch
Poster
Tue 21:00 RRL: Resnet as representation for Reinforcement Learning
Rutav Shah, Vikash Kumar
Poster
Tue 21:00 An Identifiable Double VAE For Disentangled Representations
Graziano Mita, Maurizio Filippone, Pietro Michiardi
Poster
Tue 21:00 On Characterizing GAN Convergence Through Proximal Duality Gap
Sahil Sidheekh, Aroof Aimen, Narayanan Chatapuram Krishnan
Poster
Tue 21:00 Scalable Normalizing Flows for Permutation Invariant Densities
Marin Biloš, Stephan Günnemann
Poster
Tue 21:00 Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
Akira Nakagawa, Keizo Kato, Taiji Suzuki
Poster
Tue 21:00 Soft then Hard: Rethinking the Quantization in Neural Image Compression
Zongyu Guo, Zhizheng Zhang, Runsen Feng, Zhibo Chen
Spotlight
Wed 5:30 Active Feature Acquisition with Generative Surrogate Models
Yang Li, Junier Oliva
Spotlight
Wed 5:45 Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies
Jimmy Yang, Justinian Rosca, Karthik Narasimhan, Peter Ramadge
Spotlight
Wed 6:25 Joint Online Learning and Decision-making via Dual Mirror Descent
Alfonso Lobos Ruiz, Paul Grigas, Zheng Wen
Spotlight
Wed 6:40 Learning from Nested Data with Ornstein Auto-Encoders
Youngwon Choi, Sungdong Lee, Joong-Ho (Johann) Won
Spotlight
Wed 7:25 How to Learn when Data Reacts to Your Model: Performative Gradient Descent
Zachary Izzo, Lexing Ying, James Zou
Poster
Wed 9:00 Learning from Nested Data with Ornstein Auto-Encoders
Youngwon Choi, Sungdong Lee, Joong-Ho (Johann) Won
Poster
Wed 9:00 Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies
Jimmy Yang, Justinian Rosca, Karthik Narasimhan, Peter Ramadge
Poster
Wed 9:00 Active Feature Acquisition with Generative Surrogate Models
Yang Li, Junier Oliva
Poster
Wed 9:00 Joint Online Learning and Decision-making via Dual Mirror Descent
Alfonso Lobos Ruiz, Paul Grigas, Zheng Wen
Poster
Wed 9:00 How to Learn when Data Reacts to Your Model: Performative Gradient Descent
Zachary Izzo, Lexing Ying, James Zou
Spotlight
Thu 5:40 Addressing Catastrophic Forgetting in Few-Shot Problems
Pauching Yap, Hippolyt Ritter, David Barber
Spotlight
Thu 5:40 Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia Procopiuc, Claudio Gentile
Spotlight
Thu 5:45 Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data
Sung Woo Park, Junseok Kwon
Spotlight
Thu 7:20 Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline
Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng
Spotlight
Thu 7:20 Nonmyopic Multifidelity Acitve Search
Quan Nguyen, Arghavan Modiri, Roman Garnett
Poster
Thu 9:00 Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline
Ankit Goyal, Hei Law, Bowei Liu, Alejandro Newell, Jia Deng
Poster
Thu 9:00 Nonmyopic Multifidelity Acitve Search
Quan Nguyen, Arghavan Modiri, Roman Garnett
Poster
Thu 9:00 Hierarchical Clustering of Data Streams: Scalable Algorithms and Approximation Guarantees
Anand Rajagopalan, Fabio Vitale, Danny Vainstein, Gui Citovsky, Cecilia Procopiuc, Claudio Gentile
Poster
Thu 9:00 Wasserstein Distributional Normalization For Robust Distributional Certification of Noisy Labeled Data
Sung Woo Park, Junseok Kwon
Poster
Thu 9:00 Addressing Catastrophic Forgetting in Few-Shot Problems
Pauching Yap, Hippolyt Ritter, David Barber
Oral
Thu 17:00 Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm
Mingkang Zhu, Tianlong Chen, Zhangyang Wang
Spotlight
Thu 17:30 MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space
Sophie Laturnus, Philipp Berens
Spotlight
Thu 17:40 Robust Learning for Data Poisoning Attacks
Yunjuan Wang, Poorya Mianjy, Raman Arora
Spotlight
Thu 18:20 Demystifying Inductive Biases for (Beta-)VAE Based Architectures
Dominik Zietlow, Michal Rolinek, Georg Martius
Spotlight
Thu 19:30 Conjugate Energy-Based Models
Hao Wu, Babak Esmaeili, Michael Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent
Spotlight
Thu 19:40 Streaming and Distributed Algorithms for Robust Column Subset Selection
Shuli Jiang, Dongyu Li, Irene Mengze Li, Arvind Mahankali, David Woodruff
Spotlight
Thu 20:45 Diffusion Earth Mover's Distance and Distribution Embeddings
Alexander Tong, Guillaume Huguet, Amine Natik, Kincaid Macdonald, MANIK KUCHROO, Ronald Coifman, Guy Wolf, Smita Krishnaswamy
Poster
Thu 21:00 Diffusion Earth Mover's Distance and Distribution Embeddings
Alexander Tong, Guillaume Huguet, Amine Natik, Kincaid Macdonald, MANIK KUCHROO, Ronald Coifman, Guy Wolf, Smita Krishnaswamy
Poster
Thu 21:00 Streaming and Distributed Algorithms for Robust Column Subset Selection
Shuli Jiang, Dongyu Li, Irene Mengze Li, Arvind Mahankali, David Woodruff
Poster
Thu 21:00 Robust Learning for Data Poisoning Attacks
Yunjuan Wang, Poorya Mianjy, Raman Arora
Poster
Thu 21:00 MorphVAE: Generating Neural Morphologies from 3D-Walks using a Variational Autoencoder with Spherical Latent Space
Sophie Laturnus, Philipp Berens
Poster
Thu 21:00 Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm
Mingkang Zhu, Tianlong Chen, Zhangyang Wang
Poster
Thu 21:00 Demystifying Inductive Biases for (Beta-)VAE Based Architectures
Dominik Zietlow, Michal Rolinek, Georg Martius
Poster
Thu 21:00 Conjugate Energy-Based Models
Hao Wu, Babak Esmaeili, Michael Wick, Jean-Baptiste Tristan, Jan-Willem van de Meent
Workshop
Non-Robust Feature Mapping in Deep Reinforcement Learning
Ezgi Korkmaz