# Downloads

Number of events: 427

- 3D Gaze Concurrences from Head-mounted Cameras
- 3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model
- A Bayesian Approach for Policy Learning from Trajectory Preference Queries
- A Better Way to Pre-Train Deep Boltzmann Machines
- Accelerated Training for Matrix-norm Regularization: A Boosting Approach
- Accuracy at the Top
- A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation
- Action-Model Based Multi-agent Plan Recognition
- Active Comparison of Prediction Models
- Active Learning of Model Evidence Using Bayesian Quadrature
- Active Learning of Multi-Index Function Models
- Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting
- Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions
- A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation
- A Fast Accurate Training-less P300 Speller: Unsupervised Learning Uncovers new Possibilities
- Affine Independent Variational Inference
- A Generative Model for Parts-based Object Segmentation
- A Geometric take on Metric Learning
- A latent factor model for highly multi-relational data
- A lattice filter model of the visual pathway
- Algebraic Topology and Machine Learning
- Algorithmic and Statistical Approaches for Large Social Network Data Sets
- Algorithms for Learning Markov Field Policies
- A Linear Time Active Learning Algorithm for Link Classification
- A Marginalized Particle Gaussian Process Regression
- A mechanistic model of early sensory processing based on subtracting sparse representations
- Analog readout for optical reservoir computers
- Analysis Operator Learning vs. Dictionary Learning: Fraternal Twins in Sparse Modeling
- Analyzing 3D Objects in Cluttered Images
- Ancestor Sampling for Particle Gibbs
- A Neural Autoregressive Topic Model
- A new metric on the manifold of kernel matrices with application to matrix geometric means
- Angular Quantization based Binary Codes for Fast Similarity Search
- A Nonparametric Conjugate Prior Distribution for the Maximizing Argument of a Noisy Function
- A nonparametric variable clustering model
- A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling
- A Polylog Pivot Steps Simplex Algorithm for Classification
- A Polynomial-time Form of Robust Regression
- Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning
- Approximating Concavely Parameterized Optimization Problems
- Approximating Equilibria in Sequential Auctions with Incomplete Information and Multi-Unit Demand
- A quasi-Newton proximal splitting method
- A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound
- A Simple and Practical Algorithm for Differentially Private Data Release
- A Spectral Algorithm for Latent Dirichlet Allocation
- Assessing Blinding in Clinical Trials
- A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets
- A Stochastic Spiking Network Model of Sensorimotor Control
- A systematic approach to extracting semantic information from functional MRI data
- A System for Predicting Action Content On-Line and in Real Time before Action Onset in Humans – an Intracranial Study
- Augment-and-Conquer Negative Binomial Processes
- Augmented-SVM: Automatic space partitioning for combining multiple non-linear dynamics
- A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes
- Automatic Feature Induction for Stagewise Collaborative Filtering
- Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button
- Bayesian active learning with localized priors for fast receptive field characterization
- Bayesian estimation of discrete entropy with mixtures of stick-breaking priors
- Bayesian Hierarchical Reinforcement Learning
- Bayesian models for Large-scale Hierarchical Classification
- Bayesian n-Choose-k Models for Classification and Ranking
- Bayesian Nonparametric Maximum Margin Matrix Factorization for Collaborative Prediction
- Bayesian Nonparametric Modeling of Suicide Attempts
- Bayesian nonparametric models for bipartite graphs
- Bayesian nonparametric models for ranked data
- Bayesian Nonparametric Models For Reliable Planning And Decision-Making Under Uncertainty
- Bayesian Optimization and Decision Making
- Bayesian Pedigree Analysis using Measure Factorization
- Bayesian Probabilistic Co-Subspace Addition
- Bayesian Warped Gaussian Processes
- Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence
- Big Data Meets Computer Vision: First International Workshop on Large Scale Visual Recognition and Retrieval
- Big Learning : Algorithms, Systems, and Tools
- Burn-in, bias, and the rationality of anchoring
- Calibrated Elastic Regularization in Matrix Completion
- Cardinality Restricted Boltzmann Machines
- Causal discovery with scale-mixture model for spatiotemporal variance dependencies
- Challenges for Machine Learning in Computational Sustainability
- Classification Calibration Dimension for General Multiclass Losses
- Classification with Deep Invariant Scattering Networks
- Clustering Aggregation as Maximum-Weight Independent Set
- Clustering by Nonnegative Matrix Factorization Using Graph Random Walk
- Clustering Sparse Graphs
- Cocktail Party Processing via Structured Prediction
- Coding efficiency and detectability of rate fluctuations with non-Poisson neuronal firing
- Coffee break
- Coffee break
- Collaborative Gaussian Processes for Preference Learning
- Collaborative Ranking With 17 Parameters
- Communication/Computation Tradeoffs in Consensus-Based Distributed Optimization
- Communication-Efficient Algorithms for Statistical Optimization
- Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models
- Compressive neural representation of sparse, high-dimensional probabilities
- Compressive Sensing MRI with Wavelet Tree Sparsity
- Confluence between Kernel Methods and Graphical Models
- Confusion-Based Online Learning and a Passive-Aggressive Scheme
- Connectomics: Opportunities and Challenges for Machine Learning
- Consciousness and Information Theory
- Context-Sensitive Decision Forests for Object Detection
- Continuous Relaxations for Discrete Hamiltonian Monte Carlo
- Controlled Recognition Bounds for Visual Learning and Exploration
- Convergence and Energy Landscape for Cheeger Cut Clustering
- Convergence Rate Analysis of MAP Coordinate Minimization Algorithms
- Convex Multi-view Subspace Learning
- Co-Regularized Hashing for Multimodal Data
- Cost-Sensitive Exploration in Bayesian Reinforcement Learning
- Coupling Nonparametric Mixtures via Latent Dirichlet Processes
- CPRL -- An Extension of Compressive Sensing to the Phase Retrieval Problem
- Cynomix: A Machine Learning Aided Workbench for Rapid Comprehension of Large Malware Corpora
- Deep Learning and Unsupervised Feature Learning
- Deep Learning of invariant features via tracked video sequences
- Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images
- Deep Representations and Codes for Image Auto-Annotation
- Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction
- Delay Compensation with Dynamical Synapses
- Density-Difference Estimation
- Density Propagation and Improved Bounds on the Partition Function
- Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification
- Dimensionality Dependent PAC-Bayes Margin Bound
- Dip-means: an incremental clustering method for estimating the number of clusters
- DIRTBIS - Distributed Real-Time Bayesian Inference Service
- Discrete Optimization in Machine Learning (DISCML): Structure and Scalability
- Discriminative Learning of Sum-Product Networks
- Discriminatively Trained Sparse Code Gradients for Contour Detection
- Distributed Non-Stochastic Experts
- Distributed Probabilistic Learning for Camera Networks with Missing Data
- Dropout: A simple and effective way to improve neural networks
- Dual-Space Analysis of the Sparse Linear Model
- Dynamical And-Or Graph Learning for Object Shape Modeling and Detection
- Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction
- Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data
- Efficient and direct estimation of a neural subunit model for sensory coding
- Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search
- Efficient coding connects prior and likelihood function in perceptual Bayesian inference
- Efficient high dimensional maximum entropy modeling via symmetric partition functions
- Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions
- Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems
- Efficient Sampling for Bipartite Matching Problems
- Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model
- Emergence of Object-Selective Features in Unsupervised Feature Learning
- Ensemble weighted kernel estimators for multivariate entropy estimation
- Entangled Monte Carlo
- Entropy Estimations Using Correlated Symmetric Stable Random Projections
- EVA: Engine for Visual Annotation
- Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential ℓ1-Minimization
- Exact Approximate Learning
- Expectation Propagation in Gaussian Process Dynamical Systems
- Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress
- Exponential Concentration for Mutual Information Estimation with Application to Forests
- Factorial LDA: Sparse Multi-Dimensional Text Models
- Factoring nonnegative matrices with linear programs
- Fast Algorithms, Matrix Compression and Design by Simulation
- Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression
- FastEx: Fast Clustering with Exponential Families
- Fast Resampling Weighted v-Statistics
- Fast Variational Inference in the Conjugate Exponential Family
- Feature-aware Label Space Dimension Reduction for Multi-label Classification
- Feature Clustering for Accelerating Parallel Coordinate Descent
- Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling
- Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery
- Finite Sample Convergence Rates of Zero-Order Stochastic Optimization Methods
- Forging The Graphs: A Low Rank and Positive Semidefinite Graph Learning Approach
- Forward-Backward Activation Algorithm for Hierarchical Hidden Markov Models
- From Deformations to Parts: Motion-based Segmentation of 3D Objects
- Fully Bayesian inference for neural models with negative-binomial spiking
- Fused sparsity and robust estimation for linear models with unknown variance
- Fusion with Diffusion for Robust Visual Tracking
- Gait analysis using the Kinect sensor
- GenDeR: A Generic Diversified Ranking Algorithm
- Generalization Bounds for Domain Adaptation
- Gesture recognition with Kinect
- Globally Convergent Dual MAP LP Relaxation Solvers using Fenchel-Young Margins
- Gradient-based kernel method for feature extraction and variable selection
- Gradient Weights help Nonparametric Regressors
- Graphical Gaussian Vector for Image Categorization
- Graphical Models via Generalized Linear Models
- GraphLab: A Framework For Machine Learning in the Cloud
- Hamming Distance Metric Learning
- Hardware Accelerated Belief Propagation
- Hierarchical Optimistic Region Selection driven by Curiosity
- Hierarchical spike coding of sound
- High-dimensional Nonparanormal Graph Estimation via Smooth-projected Neighborhood Pursuit
- High Dimensional Semiparametric Scale-invariant Principal Component Analysis
- High Dimensional Transelliptical Graphical Models
- High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimer Disease Progression Prediction
- Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints
- How Prior Probability Influences Decision Making: A Unifying Probabilistic Model
- How They Vote: Issue-Adjusted Models of Legislative Behavior
- Human Computation for Science and Computational Sustainability
- Human memory search as a random walk in a semantic network
- Identifiability and Unmixing of Latent Parse Trees
- Identification of Recurrent Patterns in the Activation of Brain Networks
- Image Denoising and Inpainting with Deep Neural Networks
- ImageNet Classification with Deep Convolutional Neural Networks
- Imitation Learning by Coaching
- Information in Perception and Action
- Interpreting prediction markets: a stochastic approach
- Inverse Reinforcement Learning through Structured Classification
- Isotropic Hashing
- Iterative ranking from pair-wise comparisons
- Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation
- Joint Modeling of a Matrix with Associated Text via Latent Binary Features
- Kernel Hyperalignment
- Kernel Latent SVM for Visual Recognition
- Label Ranking with Partial Abstention based on Thresholded Probabilistic Models
- Large Scale Distributed Deep Networks
- Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning
- Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs
- Learned Prioritization for Trading Off Accuracy and Speed
- Learning about Canonical Views from Internet Image Collections
- Learning as MAP Inference in Discrete Graphical Models
- Learning curves for multi-task Gaussian process regression
- Learning from Distributions via Support Measure Machines
- Learning from the Wisdom of Crowds by Minimax Entropy
- Learning Halfspaces with the Zero-One Loss: Time-Accuracy Tradeoffs
- Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data
- Learning Image Descriptors with the Boosting-Trick
- Learning Invariant Representations of Molecules for Atomization Energy Prediction
- Learning Label Trees for Probabilistic Modelling of Implicit Feedback
- Learning Manifolds with K-Means and K-Flats
- Learning Mixtures of Tree Graphical Models
- Learning Multiple Tasks using Shared Hypotheses
- Learning Networks of Heterogeneous Influence
- Learning optimal spike-based representations
- Learning Partially Observable Models Using Temporally Abstract Decision Trees
- Learning Probability Measures with respect to Optimal Transport Metrics
- Learning the Architecture of Sum-Product Networks Using Clustering on Variables
- Learning the Dependency Structure of Latent Factors
- Learning to Align from Scratch
- Learning to Discover Social Circles in Ego Networks
- Learning visual motion in recurrent neural networks
- Learning with Partially Absorbing Random Walks
- Learning with Recursive Perceptual Representations
- Learning with Target Prior
- Link Prediction in Graphs with Autoregressive Features
- Localizing 3D cuboids in single-view images
- Local Supervised Learning through Space Partitioning
- Locating Changes in Highly Dependent Data with Unknown Number of Change Points
- Log-Linear Models
- LUCID: Locally Uniform Comparison Image Descriptor
- Machine Learning Approaches to Mobile Context Awareness
- Machine Learning for Student Learning
- Machine Learning in Computational Biology
- Majorization for CRFs and Latent Likelihoods
- Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification
- MAP Inference in Chains using Column Generation
- Matrix reconstruction with the local max norm
- Max-Margin Structured Output Regression for Spatio-Temporal Action Localization
- MCMC for continuous-time discrete-state systems
- Meta-Gaussian Information Bottleneck
- Minimax Multi-Task Learning and a Generalized Loss-Compositional Paradigm for MTL
- Minimization of Continuous Bethe Approximations: A Positive Variation
- Minimizing Sparse High-Order Energies by Submodular Vertex-Cover
- Minimizing Uncertainty in Pipelines
- Mirror Descent Meets Fixed Share (and feels no regret)
- Mixability in Statistical Learning
- Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions
- MLINI - 2nd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (2 day)
- MLINI - 2nd NIPS Workshop on Machine Learning and Interpretation in Neuroimaging (2 day)
- Modeling the Forgetting Process using Image Regions
- Modelling Reciprocating Relationships with Hawkes processes
- Modern Nonparametric Methods in Machine Learning
- Monte Carlo Methods for Maximum Margin Supervised Topic Models
- Multiclass Learning Approaches: A Theoretical Comparison with Implications
- Multiclass Learning with Simplex Coding
- Multi-criteria Anomaly Detection using Pareto Depth Analysis
- Multilabel Classification using Bayesian Compressed Sensing
- Multimodal Learning with Deep Boltzmann Machines
- Multiple Choice Learning: Learning to Produce Multiple Structured Outputs
- Multiple Operator-valued Kernel Learning
- Multiplicative Forests for Continuous-Time Processes
- Multiresolution analysis on the symmetric group
- Multiresolution Gaussian Processes
- Multi-scale Hyper-time Hardware Emulation of Human Motor Nervous System Based on Spiking Neurons using FPGA
- Multi-Stage Multi-Task Feature Learning
- Multi-Task Averaging
- Multi-task Vector Field Learning
- Multi-Trade-offs in Machine Learning
- Natural Images, Gaussian Mixtures and Dead Leaves
- NCS: A Large-Scale Brain Simulator
- Near Optimal Chernoff Bounds for Markov Decision Processes
- Near-optimal Differentially Private Principal Components
- Near-Optimal MAP Inference for Determinantal Point Processes
- Neurally Plausible Reinforcement Learning of Working Memory Tasks
- Neuronal spike generation mechanism as an oversampling, noise-shaping A-to-D converter
- Newton-Like Methods for Sparse Inverse Covariance Estimation
- Nonconvex Penalization, Levy Processes and Concave Conjugates
- Non-linear Metric Learning
- Non-parametric Approximate Dynamic Programming via the Kernel Method
- Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions
- Nonparametric Reduced Rank Regression
- Nonparanormal Belief Propagation (NPBP)
- No-Regret Algorithms for Unconstrained Online Convex Optimization
- No voodoo here! Learning discrete graphical models via inverse covariance estimation
- Nystr{ö}m Method vs Random Fourier Features: A Theoretical and Empirical Comparison
- One Permutation Hashing
- On Lifting the Gibbs Sampling Algorithm
- Online allocation and homogeneous partitioning for piecewise constant mean-approximation
- Online L1-Dictionary Learning with Application to Novel Document Detection
- Online Regret Bounds for Undiscounted Continuous Reinforcement Learning
- On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization
- Online Sum-Product Computation
- On Multilabel Classification and Ranking with Partial Feedback
- On the connections between saliency and tracking
- On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking
- On the Sample Complexity of Robust PCA
- On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes
- On Triangular versus Edge Representations --- Towards Scalable Modeling of Networks
- Optimal kernel choice for large-scale two-sample tests
- Optimal Neural Tuning Curves for Arbitrary Stimulus Distributions: Discrimax, Infomax and Minimum $L_p$ Loss
- Optimal Regularized Dual Averaging Methods for Stochastic Optimization
- Optimization for Machine Learning
- Parametric Local Metric Learning for Nearest Neighbor Classification
- Patient Risk Stratification for Hospital-Associated C. Diff as a Time-Series Classification Task
- Perceptron Learning of SAT
- Perfect Dimensionality Recovery by Variational Bayesian PCA
- Persistent Homology for Learning Densities with Bounded Support
- Personalizing education with machine learning
- Perturbations, Optimization, and Statistics
- Phoneme Classification using Constrained Variational Gaussian Process Dynamical System
- Pointwise Tracking the Optimal Regression Function
- Practical Bayesian Optimization of Machine Learning Algorithms
- Priors for Diversity in Generative Latent Variable Models
- Privacy Aware Learning
- Probabilistic Event Cascades for Alzheimer's disease
- Probabilistic Low-Rank Subspace Clustering
- Probabilistic Numerics
- Probabilistic Programming: Foundations and Applications (2 day)
- Probabilistic Programming: Foundations and Applications (2 day)
- Probabilistic Topic Coding for Superset Label Learning
- Projection Retrieval for Classification
- Proper losses for learning from partial labels
- Protocols and Structures for Inference: A RESTful API for Machine Learning Services
- Provable ICA with Unknown Gaussian Noise, with Implications for Gaussian Mixtures and Autoencoders
- Proximal Newton-type Methods for Minimizing Convex Objective Functions in Composite Form
- Putting Bayes to sleep
- Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging
- Quantum information and the Brain
- Query Complexity of Derivative-Free Optimization
- Random function priors for exchangeable graphs and arrays
- Random Utility Theory for Social Choice: Theory and Algorithms
- Rational inference of relative preferences
- Real-time Fusion/normalization of Multiple SVM with libMR
- Recognizing Activities by Attribute Dynamics
- Recovery of Sparse Probability Measures via Convex Programming
- Recursive Deep Learning on 3D Point Clouds
- Reducing statistical time-series problems to binary classification
- Regularized Off-Policy TD-Learning
- Relax and Randomize : From Value to Algorithms
- Representation, Inference and Learning in Structured Statistical Models
- Repulsive Mixtures
- Risk-Aversion in Multi-armed Bandits
- Robustness and risk-sensitivity in Markov decision processes
- Scalable imputation of genetic data with a discrete fragmentation-coagulation process
- Scalable Inference of Overlapping Communities
- Scalable nonconvex inexact proximal splitting
- Scaled Gradients on Grassmann Manifolds for Matrix Completion
- Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization
- Searching for objects driven by context
- Selecting Diverse Features via Spectral Regularization
- Selective Labeling via Error Bound Minimization
- Semantic Kernel Forests from Multiple Taxonomies
- Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning
- Semi-Supervised Domain Adaptation with Non-Parametric Copulas
- Semi-supervised Eigenvectors for Locally-biased Learning
- Shifting Weights: Adapting Object Detectors from Image to Video
- Signatures of Conscious Processing in the Human Brain
- Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression
- Sketch-Based Linear Value Function Approximation
- Slice Normalized Dynamic Markov Logic Networks
- Slice sampling normalized kernel-weighted completely random measure mixture models
- Small-Variance Asymptotics for Exponential Family Dirichlet Process Mixture Models
- Social Choice: Theory and Practice
- Social network and social media analysis: Methods, models and applications
- Sparse Approximate Manifolds for Differential Geometric MCMC
- Sparse Prediction with the $k$-Support Norm
- Spectral Algorithms for Latent Variable Models
- Spectral Learning of General Weighted Automata via Constrained Matrix Completion
- Spectral learning of linear dynamics from generalised-linear observations with application to neural population data
- Spiking and saturating dendrites differentially expand single neuron computation capacity.
- Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space
- Stochastic Gradient Descent with Only One Projection
- Stochastic optimization and sparse statistical recovery: Optimal algorithms for high dimensions
- Stochastic Search and Optimization
- Strategic Impatience in Go/NoGo versus Forced-Choice Decision-Making
- Structured learning of Gaussian graphical models
- Submodular Bregman Divergences with Applications
- Super-Bit Locality-Sensitive Hashing
- Supervised Learning with Similarity Functions
- Suspicious Coincidences in the Brain
- Symbolic Dynamic Programming for Continuous State and Observation POMDPs
- Symmetric Correspondence Topic Models for Multilingual Text Analysis
- Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes
- TCA: High Dimensional Principal Component Analysis for non-Gaussian Data
- Tensor Decomposition for Fast Parsing with Latent-Variable PCFGs
- The Bethe Partition Function of Log-supermodular Graphical Models
- The BUDS POMDP Spoken Dialogue System
- The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes
- The Lovasz $\theta$ function, SVMs and finding large dense subgraphs
- The Perturbed Variation
- The representer theorem for Hilbert spaces: a necessary and sufficient condition
- The Time-Marginalized Coalescent Prior for Hierarchical Clustering
- The topographic unsupervised learning of natural sounds in the auditory cortex
- The variational hierarchical EM algorithm for clustering hidden Markov models.
- Tight Bounds on Redundancy and Distinguishability of Label-Invariant Distributions
- Timely Object Recognition
- Topic-Partitioned Multinetwork Embeddings
- Topology Constraints in Graphical Models
- Towards a learning-theoretic analysis of spike-timing dependent plasticity
- Tractable Objectives for Robust Policy Optimization
- Training sparse natural image models with a fast Gibbs sampler of an extended state space
- Trajectory-Based Short-Sighted Probabilistic Planning
- Transferring Expectations in Model-based Reinforcement Learning
- Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes
- Truncation-free Online Variational Inference for Bayesian Nonparametric Models
- Ubiquitous Content: How musicians will search for every riff, musical phrase, and idea ever recorded.
- Unsupervised Structure Discovery for Semantic Analysis of Audio
- Unsupervised template learning for fine-grained object recognition
- User-Friendly Tools for Studying Random Matrices
- Value Pursuit Iteration
- Variational Inference for Crowdsourcing
- Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity
- Volume Regularization for Binary Classification
- Waveform Driven Plasticity in BiFeO3 Memristive Devices: Model and Implementation
- Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination
- Weighted Likelihood Policy Search with Model Selection
- Why MCA? Nonlinear Spike-and-slab Sparse Coding for Neurally Plausible Image Encoding
- xLiTe: Cross-Lingual Technologies