### NeurIPS 2019 Events with Videos

## Orals

- Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input
- Uniform convergence may be unable to explain generalization in deep learning
- Causal Confusion in Imitation Learning
- Parameter elimination in particle Gibbs sampling
- Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
- Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

## Posters

- Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
- On Fenchel Mini-Max Learning
- Fooling Neural Network Interpretations via Adversarial Model Manipulation
- Sampling Networks and Aggregate Simulation for Online POMDP Planning
- A Simple Baseline for Bayesian Uncertainty in Deep Learning
- Communication-efficient Distributed SGD with Sketching
- Exact Rate-Distortion in Autoencoders via Echo Noise
- Explanations can be manipulated and geometry is to blame
- Chasing Ghosts: Instruction Following as Bayesian State Tracking
- Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
- Uniform convergence may be unable to explain generalization in deep learning
- Solving Interpretable Kernel Dimensionality Reduction
- Model Compression with Adversarial Robustness: A Unified Optimization Framework
- Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input
- Multi-marginal Wasserstein GAN
- Identification of Conditional Causal Effects under Markov Equivalence
- Differentiable Cloth Simulation for Inverse Problems
- A Game Theoretic Approach to Class-wise Selective Rationalization
- Correlation clustering with local objectives
- Visualizing the PHATE of Neural Networks
- Adversarial Examples Are Not Bugs, They Are Features
- A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models
- Adversarial training for free!
- Neural networks grown and self-organized by noise
- Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
- Point-Voxel CNN for Efficient 3D Deep Learning
- Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
- Learning Positive Functions with Pseudo Mirror Descent
- When to Trust Your Model: Model-Based Policy Optimization
- Hindsight Credit Assignment
- Residual Flows for Invertible Generative Modeling
- Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
- Maximum Mean Discrepancy Gradient Flow
- Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
- The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
- Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
- Parameter elimination in particle Gibbs sampling
- Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
- Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems
- Causal Confusion in Imitation Learning
- Guided Meta-Policy Search
- Limitations of the empirical Fisher approximation for natural gradient descent
- Asymmetric Valleys: Beyond Sharp and Flat Local Minima
- On Adversarial Mixup Resynthesis
- On the Utility of Learning about Humans for Human-AI Coordination
- Implicit Generation and Modeling with Energy Based Models
- Multiclass Learning from Contradictions
- Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD
- TAB-VCR: Tags and Attributes based Visual Commonsense Reasoning Baselines
- Joint-task Self-supervised Learning for Temporal Correspondence
- Learning Robust Options by Conditional Value at Risk Optimization
- Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees
- From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
- Language as an Abstraction for Hierarchical Deep Reinforcement Learning
- Structured Graph Learning Via Laplacian Spectral Constraints
- Calibration tests in multi-class classification: A unifying framework
- Likelihood Ratios for Out-of-Distribution Detection
- Multiview Aggregation for Learning Category-Specific Shape Reconstruction
- Learning Macroscopic Brain Connectomes via Group-Sparse Factorization
- Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces
- Disentangled behavioural representations
- Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller
- Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
- Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration
- Fast structure learning with modular regularization
- Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations
- Multiclass Performance Metric Elicitation
- Learning Conditional Deformable Templates with Convolutional Networks
- Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians
- Multiway clustering via tensor block models
- Uncertainty-based Continual Learning with Adaptive Regularization
- Function-Space Distributions over Kernels
- Image Synthesis with a Single (Robust) Classifier
- Optimal Sparse Decision Trees
- Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression
- Few-shot Video-to-Video Synthesis
- MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies
- Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction
- Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control
- McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds
- A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening
- Decentralized sketching of low rank matrices
- Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model
- DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging
- Visual Concept-Metaconcept Learning
- Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization
- ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
- Unsupervised Meta-Learning for Few-Shot Image Classification
- PoincarĂ© Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
- Fast and Accurate Stochastic Gradient Estimation
- Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
- Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
- Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
- The Impact of Regularization on High-dimensional Logistic Regression
- Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
- Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning
- This Looks Like That: Deep Learning for Interpretable Image Recognition
- Imitation-Projected Programmatic Reinforcement Learning
- Reducing Noise in GAN Training with Variance Reduced Extragradient
- The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection
- Multi-Criteria Dimensionality Reduction with Applications to Fairness
- Multi-Resolution Weak Supervision for Sequential Data
- Thompson Sampling for Multinomial Logit Contextual Bandits
- Nonlinear scaling of resource allocation in sensory bottlenecks
- Control What You Can: Intrinsically Motivated Task-Planning Agent
- Online Continual Learning with Maximal Interfered Retrieval
- Adversarial Training and Robustness for Multiple Perturbations
- Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning
- Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
- The spiked matrix model with generative priors
- Efficiently avoiding saddle points with zero order methods: No gradients required
- Neural Similarity Learning
- Practical Two-Step Lookahead Bayesian Optimization
- PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
- Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network
- Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
- Recurrent Space-time Graph Neural Networks
- Write, Execute, Assess: Program Synthesis with a REPL
- Universality in Learning from Linear Measurements
- Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
- The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic
- Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
- On the Downstream Performance of Compressed Word Embeddings
- Piecewise Strong Convexity of Neural Networks
- Ask not what AI can do, but what AI should do: Towards a framework of task delegability
- ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
- Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks
- GENO -- GENeric Optimization for Classical Machine Learning
- A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions
- STAR-Caps: Capsule Networks with Straight-Through Attentive Routing
- Online Forecasting of Total-Variation-bounded Sequences
- Learning Deep Bilinear Transformation for Fine-grained Image Representation

## Spotlights

- Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
- Identification of Conditional Causal Effects under Markov Equivalence
- Point-Voxel CNN for Efficient 3D Deep Learning
- Adversarial Examples Are Not Bugs, They Are Features
- Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
- Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
- Asymmetric Valleys: Beyond Sharp and Flat Local Minima
- Implicit Generation and Modeling with Energy Based Models
- Residual Flows for Invertible Generative Modeling
- Guided Meta-Policy Search
- Learning Positive Functions with Pseudo Mirror Descent
- Hindsight Credit Assignment
- Calibration tests in multi-class classification: A unifying framework
- Fast structure learning with modular regularization
- PoincarĂ© Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
- McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds
- Optimal Sparse Decision Trees
- This Looks Like That: Deep Learning for Interpretable Image Recognition
- Adversarial Training and Robustness for Multiple Perturbations
- Multi-Criteria Dimensionality Reduction with Applications to Fairness
- On the Downstream Performance of Compressed Word Embeddings
- Ask not what AI can do, but what AI should do: Towards a framework of task delegability

Report issues here.