Spotlight videos at #NIPS2016

Spotlight videos at NIPS2016, enjoy !

Spotlight Videos

• Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
• Deep ADMM-Net for Compressive Sensing MRI
• A scaled Bregman theorem with applications
• On Regularizing Rademacher Observation Losses
• Fast and Provably Good Seedings for k-Means
• Unsupervised Learning for Physical Interaction through Video Prediction
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
• Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
• Natural-Parameter Networks: A Class of Probabilistic Neural Networks
• SURGE: Surface Regularized Geometry Estimation from a Single Image
• Interpretable Distribution Features with Maximum Testing Power
• Sorting out typicality with the inverse moment matrix SOS polynomial
• CNNpack: Packing Convolutional Neural Networks in the Frequency Domain
• Cooperative Graphical Models
• f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
• Bayesian Optimization for Probabilistic Programs
• Hierarchical Question-Image Co-Attention for Visual Question Answering
• Fairness in Learning: Classic and Contextual Bandits
• DISCO Nets : DISsimilarity COefficients Networks
• Multimodal Residual Learning for Visual QA
• Learning and Forecasting Opinion Dynamics in Social Networks
• Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
• Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images
• Exponential Family Embeddings
• Variational Information Maximization for Feature Selection
• Learning User Perceived Clusters with Feature-Level Supervision
• Residual Networks Behave Like Ensembles of Relatively Shallow Networks
• Adversarial Multiclass Classification: A Risk Minimization Perspective
• Deep Learning without Poor Local Minima
• Optimizing affinity-based binary hashing using auxiliary coordinates
• Double Thompson Sampling for Dueling Bandits
• Computational and Statistical Tradeoffs in Learning to Rank
• Online Convex Optimization with Unconstrained Domains and Losses
• An ensemble diversity approach to supervised binary hashing
• On Explore-Then-Commit strategies
• Sublinear Time Orthogonal Tensor Decomposition
• Dual Learning for Machine Translation
• Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition
• Efficient Second Order Online Learning by Sketching
• Distributed Flexible Nonlinear Tensor Factorization
• Even Faster SVD Decomposition Yet Without Agonizing Pain
• A Multi-Batch L-BFGS Method for Machine Learning
• Semiparametric Differential Graph Models
• VIME: Variational Information Maximizing Exploration
• Solving Marginal MAP Problems with NP Oracles and Parity Constraints
• Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
• Dense Associative Memory for Pattern Recognition
• Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$
• What Makes Objects Similar: A Unified Multi-Metric Learning Approach
• Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
• A Communication-Efficient Parallel Algorithm for Decision Tree
• Convex Two-Layer Modeling with Latent Structure
• Adaptive Concentration Inequalities for Sequential Decision Problems
• Catching heuristics are optimal control policies
• Synthesis of MCMC and Belief Propagation
• Unifying Count-Based Exploration and Intrinsic Motivation
• Large Margin Discriminant Dimensionality Reduction in Prediction Space
• Stochastic Structured Prediction under Bandit Feedback
• SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
• Adaptive Skills Adaptive Partitions (ASAP)
• Multiple-Play Bandits in the Position-Based Model
• Optimal Black-Box Reductions Between Optimization Objectives
• Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters
• Boosting with Abstention
• Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
• A Credit Assignment Compiler for Joint Prediction
• Consistent Kernel Mean Estimation for Functions of Random Variables
• Variational Inference in Mixed Probabilistic Submodular Models
• Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
• A Bandit Framework for Strategic Regression
• Low-Rank Regression with Tensor Responses
• PAC-Bayesian Theory Meets Bayesian Inference
• Data Poisoning Attacks on Factorization-Based Collaborative Filtering
• Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition
• Diffusion-Convolutional Neural Networks
• A Probabilistic Programming Approach To Probabilistic Data Analysis
• Learning Structured Sparsity in Deep Neural Networks
• Sample Complexity of Automated Mechanism Design
• Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products
• Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
• Active Learning from Imperfect Labelers
• Learning to Communicate with Deep Multi-Agent Reinforcement Learning
• Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering
• On the Recursive Teaching Dimension of VC Classes
• Learning Multiagent Communication with Backpropagation
• Finding significant combinations of features in the presence of categorical covariates
• Graphons, mergeons, and so on!
• Pruning Random Forests for Prediction on a Budget
• Contextual semibandits via supervised learning oracles
• Deep Learning for Predicting Human Strategic Behavior
• Eliciting Categorical Data for Optimal Aggregation
• Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
• Improved Dropout for Shallow and Deep Learning
• Cyclades: Conflict-free Asynchronous Machine Learning
• Single Pass PCA of Matrix Products
• Stochastic Variational Deep Kernel Learning
• Causal meets Submodular: Subset Selection with Directed Information
• Deep Neural Networks with Inexact Matching for Person Re-Identification
• Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
• Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model
• Object based Scene Representations using Fisher Scores of Local Subspace Projections
• Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
• Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling
• Combinatorial semi-bandit with known covariance
• Adaptive Averaging in Accelerated Descent Dynamics
• Variational Bayes on Monte Carlo Steroids
• Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
• A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order
• Estimating the Size of a Large Network and its Communities from a Random Sample
• On Robustness of Kernel Clustering
• New Liftable Classes for First-Order Probabilistic Inference
• The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
• Learning shape correspondence with anisotropic convolutional neural networks
• Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
• Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
• Search Improves Label for Active Learning
• Leveraging Sparsity for Efficient Submodular Data Summarization
• Linear Contextual Bandits with Knapsacks
• Reconstructing Parameters of Spreading Models from Partial Observations
• RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
• Exact Recovery of Hard Thresholding Pursuit
• Data Programming: Creating Large Training Sets, Quickly
• Dynamic matrix recovery from incomplete observations under an exact low-rank constraint
• Fast Distributed Submodular Cover: Public-Private Data Summarization
• Communication-Optimal Distributed Clustering
• Probing the Compositionality of Intuitive Functions
• Composing graphical models with neural networks for structured representations and fast inference
• Learning Sparse Gaussian Graphical Models with Overlapping Blocks
• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
• Infinite Hidden Semi-Markov Modulated Interaction Point Process
• Cooperative Inverse Reinforcement Learning
• Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments
• Select-and-Sample for Spike-and-Slab Sparse Coding
• Greedy Feature Construction
• Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes
• Quantum Perceptron Models
• Deep Exploration via Bootstrapped DQN
• Convolutional Neural Fabrics
• A Sparse Interactive Model for Matrix Completion with Side Information
• Coresets for Scalable Bayesian Logistic Regression
• Binarized Neural Networks
• Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
• Learnable Visual Markers
• Learning Deep Embeddings with Histogram Loss
• Spectral Learning of Dynamic Systems from Nonequilibrium Data
• A Minimax Approach to Supervised Learning
• Edge-exchangeable graphs and sparsity
• A Locally Adaptive Normal Distribution
• Completely random measures for modelling block-structured sparse networks
• Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics
• Learning values across many orders of magnitude
• Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
• Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation
• A Consistent Regularization Approach for Structured Prediction
• An urn model for majority voting in classification ensembles
• Tagger: Deep Unsupervised Perceptual Grouping
• Interaction Networks for Learning about Objects, Relations and Physics
• Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
• Professor Forcing: A New Algorithm for Training Recurrent Networks
• Learning brain regions via large-scale online structured sparse dictionary learning
• Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods
• Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information
• Learning Parametric Sparse Models for Image Super-Resolution
• Disease Trajectory Maps
• Learning in Games: Robustness of Fast Convergence
• Algorithms and matching lower bounds for approximately-convex optimization
• Neural Universal Discrete Denoiser
• Achieving budget-optimality with adaptive schemes in crowdsourcing
• Supervised Word Mover's Distance
• Full-Capacity Unitary Recurrent Neural Networks
• k*-Nearest Neighbors: From Global to Local
• A Bayesian method for reducing bias in neural representational similarity analysis
• Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
• Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
• Optimal Binary Classifier Aggregation for General Losses
• A primal-dual method for conic constrained distributed optimization problems

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