NeurIPS 2019

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Dec. 8, 2019 Expo schedule » New in ML »

Timezone: America/Vancouver »

Indicates the beginning of a session

Time	East Exhibition Hall B + C
Tue Dec 10, 2019
10:45 AM (Posters)	Algorithms -- Adversarial Learning Algorithms -- Bandit Algorithms Algorithms -- Clustering Algorithms -- Components Analysis (e.g., CCA, ICA, LDA, PCA) Algorithms -- Density Estimation Algorithms -- Dynamical Systems Algorithms -- Kernel Methods Algorithms -- Missing Data Algorithms -- Representation Learning Algorithms -- Similarity and Distance Learning Applications -- Communication- or Memory-Bounded Learning Applications -- Dialog- or Communication-Based Learning Applications -- Game Playing Applications -- Privacy, Anonymity, and Security Applications -- Recommender Systems Applications -- Web Applications and Internet Data Deep Learning -- Biologically Plausible Deep Networks Deep Learning -- Deep Autoencoders Deep Learning -- Efficient Inference Methods Deep Learning -- Generative Models Deep Learning -- Interaction-Based Deep Networks Deep Learning -- Optimization for Deep Networks Deep Learning -- Predictive Models Deep Learning -- Recurrent Networks Deep Learning -- Visualization or Exposition Techniques for Deep Networks Optimization -- Combinatorial Optimization Probabilistic Methods -- Causal Inference Reinforcement Learning and Planning -- Decision and Control Reinforcement Learning and Planning -- Exploration Reinforcement Learning and Planning -- Markov Decision Processes Reinforcement Learning and Planning -- Navigation Theory -- Computational Complexity Theory -- Frequentist Statistics Theory -- Hardness of Learning and Approximations Theory -- Learning Theory
05:30 PM (Posters)	Algorithms -- Bandit Algorithms Deep Learning -- Generative Models Deep Learning -- Optimization for Deep Networks Probabilistic Methods -- Causal Inference Reinforcement Learning and Planning -- Decision and Control Reinforcement Learning and Planning -- Exploration Theory -- Learning Theory Algorithms -- AutoML Algorithms -- Large Scale Learning Algorithms -- Meta-Learning Algorithms -- Regression Algorithms -- Structured Prediction Algorithms -- Unsupervised Learning Applications -- Body Pose, Face, and Gesture Analysis Optimization -- Convex Optimization Optimization -- Non-Convex Optimization Optimization -- Stochastic Optimization Probabilistic Methods -- Distributed Inference Probabilistic Methods -- Gaussian Processes Probabilistic Methods -- Hierarchical Models Probabilistic Methods -- MCMC Probabilistic Methods -- Variational Inference Reinforcement Learning and Planning -- Model-Based RL Reinforcement Learning and Planning -- Multi-Agent RL Reinforcement Learning and Planning -- Reinforcement Learning Theory -- Control Theory

Time	East Exhibition Hall B + C
Wed Dec 11, 2019
10:45 AM (Posters)	Algorithms -- Adaptive Data Analysis Algorithms -- Boosting and Ensemble Methods Algorithms -- Model Selection and Structure Learning Algorithms -- Regression Algorithms -- Semi-Supervised Learning Algorithms -- Uncertainty Estimation Algorithms -- Unsupervised Learning Applications -- Computer Vision Applications -- Image Segmentation Applications -- Object Detection Applications -- Robotics Applications -- Visual Scene Analysis and Interpretation Deep Learning -- Efficient Inference Methods Deep Learning -- Generative Models Deep Learning -- Supervised Deep Networks Neuroscience and Cognitive Science -- Brain Imaging Neuroscience and Cognitive Science -- Brain Mapping Neuroscience and Cognitive Science -- Brain--Computer Interfaces and Neural Prostheses Neuroscience and Cognitive Science -- Connectomics Neuroscience and Cognitive Science -- Human or Animal Learning Neuroscience and Cognitive Science -- Language for Cognitive Science Neuroscience and Cognitive Science -- Memory Neuroscience and Cognitive Science -- Neuroscience Neuroscience and Cognitive Science -- Perception Neuroscience and Cognitive Science -- Problem Solving Neuroscience and Cognitive Science -- Visual Perception Optimization -- Stochastic Optimization Optimization -- Submodular Optimization Probabilistic Methods -- Bayesian Nonparametrics Probabilistic Methods -- Belief Propagation Probabilistic Methods -- Graphical Models Probabilistic Methods -- Latent Variable Models Probabilistic Methods -- Topic Models Reinforcement Learning and Planning -- Decision and Control Reinforcement Learning and Planning -- Hierarchical RL Reinforcement Learning and Planning -- Reinforcement Learning Theory -- Game Theory and Computational Economics Theory -- Learning Theory Theory -- Regularization
05:00 PM (Posters)	Applications -- Computer Vision Deep Learning -- Generative Models Reinforcement Learning and Planning -- Reinforcement Learning Theory -- Game Theory and Computational Economics Theory -- Learning Theory Algorithms -- Bandit Algorithms Algorithms -- Classification Algorithms -- Collaborative Filtering Algorithms -- Few-Shot Learning Algorithms -- Meta-Learning Algorithms -- Metric Learning Algorithms -- Multitask and Transfer Learning Algorithms -- Online Learning Algorithms -- Ranking and Preference Learning Algorithms -- Relational Learning Algorithms -- Spectral Methods Applications -- Audio and Speech Processing Applications -- Information Retrieval Applications -- Matrix and Tensor Factorization Applications -- Natural Language Processing Applications -- Signal Processing Applications -- Visual Question Answering Data, Challenges, Implementations, and Software -- Benchmarks Data, Challenges, Implementations, and Software -- Data Sets or Data Repositories Data, Challenges, Implementations, and Software -- Virtual Environments Deep Learning -- Attention Models Optimization -- Convex Optimization Probabilistic Methods -- Gaussian Processes Probabilistic Methods -- Hierarchical Models Probabilistic Methods -- MCMC Probabilistic Methods -- Variational Inference Reinforcement Learning and Planning -- Multi-Agent RL Theory -- Large Deviations and Asymptotic Analysis Theory -- Statistical Physics of Learning

Time	East Exhibition Hall B + C
Thu Dec 12, 2019
10:45 AM (Posters)	Algorithms -- Active Learning Algorithms -- Adversarial Learning Algorithms -- AutoML Algorithms -- Bandit Algorithms Algorithms -- Classification Algorithms -- Multitask and Transfer Learning Algorithms -- Representation Learning Applications -- Fairness, Accountability, and Transparency Applications -- Privacy, Anonymity, and Security Applications -- Quantitative Finance and Econometrics Applications -- Time Series Analysis Deep Learning -- Adversarial Networks Deep Learning -- CNN Architectures Deep Learning -- Efficient Training Methods Deep Learning -- Optimization for Deep Networks Neuroscience and Cognitive Science -- Cognitive Science Neuroscience and Cognitive Science -- Neural Coding Neuroscience and Cognitive Science -- Neuroscience Neuroscience and Cognitive Science -- Reasoning Optimization -- Non-Convex Optimization Optimization -- Stochastic Optimization Reinforcement Learning and Planning -- Planning Reinforcement Learning and Planning -- Reinforcement Learning Theory -- Information Theory Theory -- Regularization
05:00 PM (Posters)	Algorithms -- Representation Learning Applications -- Fairness, Accountability, and Transparency Applications -- Privacy, Anonymity, and Security Deep Learning -- Optimization for Deep Networks Optimization -- Non-Convex Optimization Algorithms -- Components Analysis (e.g., CCA, ICA, LDA, PCA) Algorithms -- Kernel Methods Algorithms -- Nonlinear Dimensionality Reduction and Manifold Learning Algorithms -- Online Learning Algorithms -- Sparse Coding and Dimensionality Expansion Algorithms -- Sparsity and Compressed Sensing Algorithms -- Stochastic Methods Algorithms -- Structured Prediction Algorithms -- Unsupervised Learning Applications -- Activity and Event Recognition Applications -- Computational Biology and Bioinformatics Applications -- Computational Photography Applications -- Computational Social Science Applications -- Computer Vision Applications -- Denoising Applications -- Hardware and Systems Applications -- Health Applications -- Natural Language Processing Applications -- Network Analysis Applications -- Object Recognition Applications -- Program Understanding and Generation Applications -- Sustainability Applications -- Tracking and Motion in Video Applications -- Video Analysis Data, Challenges, Implementations, and Software -- Data Sets or Data Repositories Data, Challenges, Implementations, and Software -- Software Toolkits Deep Learning -- Embedding Approaches Deep Learning -- Memory-Augmented Neural Networks Theory -- Frequentist Statistics Theory -- Game Theory and Computational Economics Theory -- Large Deviations and Asymptotic Analysis Theory -- Learning Theory Theory -- Spaces of Functions and Kernels Deep Learning -- Predictive Models