Downloads 2015
Number of events: 467
- 3D Object Proposals for Accurate Object Class Detection
- A Bayesian Framework for Modeling Confidence in Perceptual Decision Making
- ABC in Montreal
- Accelerated Deep Learning on GPUs: From Large Scale Training to Embedded Deployment
- Accelerated Mirror Descent in Continuous and Discrete Time
- Accelerated Proximal Gradient Methods for Nonconvex Programming
- A class of network models recoverable by spectral clustering
- A Complete Recipe for Stochastic Gradient MCMC
- A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
- Action-Conditional Video Prediction using Deep Networks in Atari Games
- Active Learning from Weak and Strong Labelers
- Adaptive Data Analysis
- Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models
- Adaptive Online Learning
- Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing
- Adaptive Stochastic Optimization: From Sets to Paths
- A Dual Augmented Block Minimization Framework for Learning with Limited Memory
- Advances in Approximate Bayesian Inference
- Adversarial Prediction Games for Multivariate Losses
- A fast, universal algorithm to learn parametric nonlinear embeddings
- A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure
- A Gaussian Process Model of Quasar Spectral Energy Distributions
- A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice
- A hybrid sampler for Poisson-Kingman mixture models
- Algorithmic Stability and Uniform Generalization
- Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits
- Alternating Minimization for Regression Problems with Vector-valued Outputs
- A Market Framework for Eliciting Private Data
- An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching
- Analysis of Robust PCA via Local Incoherence
- An interactive system for the extraction of meaningful visualizations from high-dimensional data
- A Nonconvex Optimization Framework for Low Rank Matrix Estimation
- A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks
- Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks
- Applying (machine) Learning to Experimental Physics (ALEPH) and «Flavours of Physics» challenge
- Approximating Sparse PCA from Incomplete Data
- A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA
- A Recurrent Latent Variable Model for Sequential Data
- A Reduced-Dimension fMRI Shared Response Model
- Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question
- Associative Memory via a Sparse Recovery Model
- A Structural Smoothing Framework For Robust Graph Comparison
- Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization
- Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care
- A Theory of Decision Making Under Dynamic Context
- A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding
- Attention-Based Models for Speech Recognition
- Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments
- A Universal Catalyst for First-Order Optimization
- A Universal Primal-Dual Convex Optimization Framework
- Automatic Variational Inference in Stan
- Backpropagation for Energy-Efficient Neuromorphic Computing
- BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions
- Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff
- Bandits with Unobserved Confounders: A Causal Approach
- Barrier Frank-Wolfe for Marginal Inference
- Basis refinement strategies for linear value function approximation in MDPs
- Bayesian Active Model Selection with an Application to Automated Audiometry
- Bayesian dark knowledge
- Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
- Bayesian Nonparametrics: The Next Generation
- Bayesian Optimization: Scalability and Flexibility
- Bayesian Optimization with Exponential Convergence
- b-bit Marginal Regression
- Beyond Convexity: Stochastic Quasi-Convex Optimization
- Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs
- Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution
- Bidirectional Recurrent Neural Networks as Generative Models
- BigNeuro 2015: Making sense of big neural data
- BinaryConnect: Training Deep Neural Networks with binary weights during propagations
- Biologically Inspired Dynamic Textures for Probing Motion Perception
- Black box learning and inference
- Black-box optimization of noisy functions with unknown smoothness
- Bounded Optimality and Rational Metareasoning
- Bounding errors of Expectation-Propagation
- Bounding the Cost of Search-Based Lifted Inference
- Calibrated Structured Prediction
- Challenges in Machine Learning (CiML 2015): "Open Innovation" and "Coopetitions"
- Character-level Convolutional Networks for Text Classification
- Claudico: The World's Strongest No-Limit Texas Hold'em Poker AI
- Closed-form Estimators for High-dimensional Generalized Linear Models
- CodaLab Worksheets for Reproducible, Executable Papers
- COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
- Cognitive Computation: Integrating neural and symbolic approaches
- Cognitive Computation: Integrating neural and symbolic approaches (day 2)
- Collaborative Filtering with Graph Information: Consistency and Scalable Methods
- Collaboratively Learning Preferences from Ordinal Data
- Color Constancy by Learning to Predict Chromaticity from Luminance
- Column Selection via Adaptive Sampling
- Combinatorial Bandits Revisited
- Combinatorial Cascading Bandits
- Communication Complexity of Distributed Convex Learning and Optimization
- Community Detection via Measure Space Embedding
- Competitive Distribution Estimation: Why is Good-Turing Good
- Compressive spectral embedding: sidestepping the SVD
- Computational Principles for Deep Neuronal Architectures
- Consistent Multilabel Classification
- Convergence Analysis of Prediction Markets via Randomized Subspace Descent
- Convergence Rates of Active Learning for Maximum Likelihood Estimation
- Convergence rates of sub-sampled Newton methods
- Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
- Convolutional Networks on Graphs for Learning Molecular Fingerprints
- Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling
- Convolutional spike-triggered covariance analysis for neural subunit models
- Copeland Dueling Bandits
- Copula variational inference
- Cornering Stationary and Restless Mixing Bandits with Remix-UCB
- Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling
- Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions
- Data-Driven Speech Animation
- Data Generation as Sequential Decision Making
- Decomposition Bounds for Marginal MAP
- Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
- Deep Convolutional Inverse Graphics Network
- Deep Generative Image Models using a ￼Laplacian Pyramid of Adversarial Networks
- Deep Knowledge Tracing
- Deep Learning
- Deep Learning using Approximate Hardware
- Deep learning with Elastic Averaging SGD
- Deeply Learning the Messages in Message Passing Inference
- Deep Poisson Factor Modeling
- Deep Reinforcement Learning
- Deep Temporal Sigmoid Belief Networks for Sequence Modeling
- Deep Visual Analogy-Making
- Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation
- Diagnosis and Therapy of Psychiatric Disorders Based on Brain Dynamics
- DIANNE - Distributed Artificial Neural Networks
- Differentially Private Learning of Structured Discrete Distributions
- Differentially private subspace clustering
- Discrete Rényi Classifiers
- Discriminative Robust Transformation Learning
- Distributed Submodular Cover: Succinctly Summarizing Massive Data
- Distributionally Robust Logistic Regression
- Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
- Efficient and Parsimonious Agnostic Active Learning
- Efficient and Robust Automated Machine Learning
- Efficient Compressive Phase Retrieval with Constrained Sensing Vectors
- Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets
- Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction
- Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression
- Efficient Non-greedy Optimization of Decision Trees
- Efficient Output Kernel Learning for Multiple Tasks
- Efficient Thompson Sampling for Online ￼Matrix-Factorization Recommendation
- Embedding Inference for Structured Multilabel Prediction
- Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
- Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
- End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture
- End-To-End Memory Networks
- Enforcing balance allows local supervised learning in spiking recurrent networks
- Equilibrated adaptive learning rates for non-convex optimization
- Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach
- Estimating Mixture Models via Mixtures of Polynomials
- Evaluating the statistical significance of biclusters
- Exactness of Approximate MAP Inference in Continuous MRFs
- Expectation Particle Belief Propagation
- Explore no more: Improved high-probability regret bounds for non-stochastic bandits
- Exploring Models and Data for Image Question Answering
- Expressing an Image Stream with a Sequence of Natural Sentences
- Extending Gossip Algorithms to Distributed Estimation of U-statistics
- Extreme Classification 2015: Multi-class and Multi-label Learning in Extremely Large Label Spaces
- Fast and Accurate Inference of Plackett–Luce Models
- Fast and Guaranteed Tensor Decomposition via Sketching
- Fast and Memory Optimal Low-Rank Matrix Approximation
- Fast Bidirectional Probability Estimation in Markov Models
- Fast Classification Rates for High-dimensional Gaussian Generative Models
- Fast Convergence of Regularized Learning in Games
- Fast Distributed k-Center Clustering with Outliers on Massive Data
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Fast Lifted MAP Inference via Partitioning
- Fast, Provable Algorithms for Isotonic Regression in all L_p-norms
- Fast Randomized Kernel Ridge Regression with Statistical Guarantees
- Fast Rates for Exp-concave Empirical Risk Minimization
- Fast sampling with neuromorphic hardware
- Fast Second Order Stochastic Backpropagation for Variational Inference
- Fast Two-Sample Testing with Analytic Representations of Probability Measures
- Fighting Bandits with a New Kind of Smoothness
- Finite-Time Analysis of Projected Langevin Monte Carlo
- Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial
- Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
- From random walks to distances on unweighted graphs
- Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning
- GAP Safe screening rules for sparse multi-task and multi-class models
- Gaussian Process Random Fields
- Generalization in Adaptive Data Analysis and Holdout Reuse
- Generative Image Modeling Using Spatial LSTMs
- GP Kernels for Cross-Spectrum Analysis
- Gradient Estimation Using Stochastic Computation Graphs
- Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
- Grammar as a Foreign Language
- Halting in Random Walk Kernels
- Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks
- Hidden Technical Debt in Machine Learning Systems
- High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality
- High-dimensional neural spike train analysis with generalized count linear dynamical systems
- High-Performance Hardware for Machine Learning
- HONOR: Hybrid Optimization for NOn-convex Regularized problems
- Human Memory Search as Initial-Visit Emitting Random Walk
- Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems
- Incremental Methods for Additive Cost Convex Optimization
- Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability
- Inference for determinantal point processes without spectral knowledge
- Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
- Infinite Factorial Dynamical Model
- Information-theoretic lower bounds for convex optimization with erroneous oracles
- Interactive Control of Diverse Complex Characters with Neural Networks
- Interactive Incremental Question Answering
- Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
- Introduction to Reinforcement Learning with Function Approximation
- Inverse Reinforcement Learning with Locally Consistent Reward Functions
- Is Approval Voting Optimal Given Approval Votes?
- Kullback-Leibler Proximal Variational Inference
- Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings
- Large-Scale Distributed Systems for Training Neural Networks
- Large-scale probabilistic predictors with and without guarantees of validity
- LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements
- Latent Bayesian melding for integrating individual and population models
- Learnability of Influence in Networks
- Learning and privacy with incomplete data and weak supervision
- Learning Bayesian Networks with Thousands of Variables
- Learning both Weights and Connections for Efficient Neural Network
- Learning Causal Graphs with Small Interventions
- Learning Continuous Control Policies by Stochastic Value Gradients
- Learning Faster from Easy Data II
- Learning From Small Samples: An Analysis of Simple Decision Heuristics
- Learning, Inference and Control of Multi-Agent Systems
- Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring
- Learning spatiotemporal trajectories from manifold-valued longitudinal data
- Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels
- Learning structured densities via infinite dimensional exponential families
- Learning Structured Output Representation using Deep Conditional Generative Models
- Learning Theory and Algorithms for Forecasting Non-stationary Time Series
- Learning to Linearize Under Uncertainty
- Learning to Segment Object Candidates
- Learning to Transduce with Unbounded Memory
- Learning visual biases from human imagination
- Learning Wake-Sleep Recurrent Attention Models
- Learning with a Wasserstein Loss
- Learning with Group Invariant Features: A Kernel Perspective.
- Learning with Incremental Iterative Regularization
- Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer
- Learning with Relaxed Supervision
- Learning with Symmetric Label Noise: The Importance of Being Unhinged
- Less is More: Nyström Computational Regularization
- Lifelong Learning with Non-i.i.d. Tasks
- Lifted Inference Rules With Constraints
- Lifted Symmetry Detection and Breaking for MAP Inference
- Linear Multi-Resource Allocation with Semi-Bandit Feedback
- Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
- Local Causal Discovery of Direct Causes and Effects
- Local Expectation Gradients for Black Box Variational Inference
- Local Smoothness in Variance Reduced Optimization
- Logarithmic Time Online Multiclass prediction
- Machine Learning and Interpretation in Neuroimaging (day 1)
- Machine Learning and Interpretation in Neuroimaging (day 2)
- Machine Learning for (e-)Commerce
- Machine Learning For Healthcare (MLHC)
- Machine Learning for Spoken Language Understanding and Interactions
- Machine Learning From and For Adaptive User Technologies: From Active Learning & Experimentation to Optimization & Personalization
- Machine Learning in Computational Biology
- Machine Learning Systems
- Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents
- Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation
- Matrix Completion Under Monotonic Single Index Models
- Matrix Completion with Noisy Side Information
- Matrix Manifold Optimization for Gaussian Mixtures
- Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets
- Max-Margin Deep Generative Models
- Max-Margin Majority Voting for Learning from Crowds
- M-Best-Diverse Labelings for Submodular Energies and Beyond
- MCMC for Variationally Sparse Gaussian Processes
- Measuring Sample Quality with Stein's Method
- Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
- Minimax Time Series Prediction
- Minimum Weight Perfect Matching via Blossom Belief Propagation
- Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications
- Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path
- Model-Based Relative Entropy Stochastic Search
- Modelling and inference for dynamics on complex interaction networks: joining up machine learning and statistical physics
- Monotone k-Submodular Function Maximization with Size Constraints
- Monte Carlo Inference Methods
- M-Statistic for Kernel Change-Point Detection
- Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms
- Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection
- Multimodal Machine Learning
- Multiresolution methods for large-scale learning
- Natural Neural Networks
- Nearly Optimal Private LASSO
- Networks in the Social and Information Sciences
- Neural Adaptive Sequential Monte Carlo
- Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma
- NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning
- Non-convex Optimization for Machine Learning: Theory and Practice
- Non-convex Statistical Optimization for Sparse Tensor Graphical Model
- Nonparametric Methods for Large Scale Representation Learning
- Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations
- No-Regret Learning in Bayesian Games
- On Elicitation Complexity
- Online F-Measure Optimization
- Online Gradient Boosting
- Online Learning for Adversaries with Memory: Price of Past Mistakes
- Online Learning with Adversarial Delays
- Online Learning with Gaussian Payoffs and Side Observations
- Online Prediction at the Limit of Zero Temperature
- Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach
- On some provably correct cases of variational inference for topic models
- On the Accuracy of Self-Normalized Log-Linear Models
- On the consistency theory of high dimensional variable screening
- On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators
- On the Global Linear Convergence of Frank-Wolfe Optimization Variants
- On-the-Job Learning with Bayesian Decision Theory
- On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors
- On the Optimality of Classifier Chain for Multi-label Classification
- On the Pseudo-Dimension of Nearly Optimal Auctions
- On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs
- On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
- Optimal Linear Estimation under Unknown Nonlinear Transform
- Optimal Rates for Random Fourier Features
- Optimal Ridge Detection using Coverage Risk
- Optimal Testing for Properties of Distributions
- Optimization for Machine Learning (OPT2015)
- Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference
- Orthogonal NMF through Subspace Exploration
- Parallel Correlation Clustering on Big Graphs
- Parallelizing MCMC with Random Partition Trees
- Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation
- Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
- Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models
- Particle Gibbs for Infinite Hidden Markov Models
- Path-SGD: Path-Normalized Optimization in Deep Neural Networks
- Planar Ultrametrics for Image Segmentation
- Pointer Networks
- Policy Evaluation Using the Ω-Return
- Policy Gradient for Coherent Risk Measures
- Post-selection Inference for Forward Stepwise Regression, Lasso and other Adaptive Statistical procedures
- Practical and Optimal LSH for Angular Distance
- Precision-Recall-Gain Curves: PR Analysis Done Right
- Preconditioned Spectral Descent for Deep Learning
- Predtron: A Family of Online Algorithms for General Prediction Problems
- Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
- Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric
- Private Graphon Estimation for Sparse Graphs
- Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process
- Probabilistic Integration
- Probabilistic Line Searches for Stochastic Optimization
- Probabilistic Machine Learning: Foundations and Frontiers
- Probabilistic Programming
- Probabilistic Variational Bounds for Graphical Models
- Quantum Machine Learning
- Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling
- Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition
- Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width
- Rate-Agnostic (Causal) Structure Learning
- Reasoning, Attention, Memory (RAM) Workshop
- Recognizing retinal ganglion cells in the dark
- Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters
- Rectified Factor Networks
- Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction
- Reflection, Refraction, and Hamiltonian Monte Carlo
- Regressive Virtual Metric Learning
- Regret-Based Pruning in Extensive-Form Games
- Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring
- Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices
- Regularization Path of Cross-Validation Error Lower Bounds
- Regularized EM Algorithms: A Unified Framework and Statistical Guarantees
- Rethinking LDA: Moment Matching for Discrete ICA
- Revenue Optimization against Strategic Buyers
- Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
- Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis
- Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
- Robust PCA with compressed data
- Robust Portfolio Optimization
- Robust Regression via Hard Thresholding
- Robust Spectral Inference for Joint Stochastic Matrix Factorization
- Saliency, Scale and Information: Towards a Unifying Theory
- Sample Complexity Bounds for Iterative Stochastic Policy Optimization
- Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning
- Sample Complexity of Learning Mahalanobis Distance Metrics
- Sample Efficient Path Integral Control under Uncertainty
- Sampling from Probabilistic Submodular Models
- Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models
- Scalable Inference for Gaussian Process Models with Black-Box Likelihoods
- Scalable Monte Carlo Methods for Bayesian Analysis of Big Data
- Scalable Semi-Supervised Aggregation of Classifiers
- Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients
- Scaling up visual search for product recommendation
- Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
- Secure Multi-party Differential Privacy
- Segregated Graphs and Marginals of Chain Graph Models
- Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization
- Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding
- Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
- Semi-supervised Learning with Ladder Networks
- Semi-supervised Sequence Learning
- SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk
- Shepard Convolutional Neural Networks
- Skip-Thought Vectors
- Smooth and Strong: MAP Inference with Linear Convergence
- Smooth Interactive Submodular Set Cover
- Softstar: Heuristic-Guided Probabilistic Inference
- Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
- Space-Time Local Embeddings
- Sparse and Low-Rank Tensor Decomposition
- Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent
- Sparse Local Embeddings for Extreme Multi-label Classification
- Sparse PCA via Bipartite Matchings
- Spatial Transformer Networks
- Spectral Learning of Large Structured HMMs for Comparative Epigenomics
- Spectral Norm Regularization of Orthonormal Representations for Graph Transduction
- Spectral Representations for Convolutional Neural Networks
- Spherical Random Features for Polynomial Kernels
- Statistical Methods for Understanding Neural Systems
- Statistical Model Criticism using Kernel Two Sample Tests
- Statistical Topological Data Analysis - A Kernel Perspective
- Stochastic Expectation Propagation
- Stochastic Online Greedy Learning with Semi-bandit Feedbacks
- StopWasting My Gradients: Practical SVRG
- Streaming, Distributed Variational Inference for Bayesian Nonparametrics
- Streaming Min-max Hypergraph Partitioning
- Structured Estimation with Atomic Norms: General Bounds and Applications
- Structured Transforms for Small-Footprint Deep Learning
- SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals
- Submodular Hamming Metrics
- Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's
- Subset Selection by Pareto Optimization
- Subspace Clustering with Irrelevant Features via Robust Dantzig Selector
- Sum-of-Squares Lower Bounds for Sparse PCA
- Super-Resolution Off the Grid
- Supervised Learning for Dynamical System Learning
- Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring
- Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms
- Teaching Machines to Read and Comprehend
- Tensorizing Neural Networks
- Testing Closeness With Unequal Sized Samples
- Texture Synthesis Using Convolutional Neural Networks
- The 1st International Workshop "Feature Extraction: Modern Questions and Challenges"
- The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions
- The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels
- The Human Kernel
- The Pareto Regret Frontier for Bandits
- The pMMF multiresolution matrix factorization library
- The Poisson Gamma Belief Network
- The Population Posterior and Bayesian Modeling on Streams
- The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors
- The Self-Normalized Estimator for Counterfactual Learning
- Time-Sensitive Recommendation From Recurrent User Activities
- Time Series Workshop
- Top-k Multiclass SVM
- Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number
- Tractable Learning for Complex Probability Queries
- Training Restricted Boltzmann Machine via the ￼Thouless-Anderson-Palmer free energy
- Training Very Deep Networks
- Transfer and Multi-Task Learning: Trends and New Perspectives
- Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models
- Unified View of Matrix Completion under General Structural Constraints
- Unlocking neural population non-stationarities using hierarchical dynamics models
- Unsupervised Learning by Program Synthesis
- Variance Reduced Stochastic Gradient Descent with Neighbors
- Variational Consensus Monte Carlo
- Variational Dropout and the Local Reparameterization Trick
- Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
- Visalogy: Answering Visual Analogy Questions
- Vitruvian Science: a visual editor for quickly building neural networks in the cloud
- Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis
- Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization
- When are Kalman-Filter Restless Bandits Indexable?
- Where are they looking?
- Winner-Take-All Autoencoders