Downloads 2006
Number of events: 265
- A Bayesian Approach to Diffusion Models of Decision-Making and Response Time
- Accelerated Variational Dirichlet Process Mixtures
- A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
- Active learning for misspecified generalized linear models
- AdaBoost is Consistent
- Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces
- Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Mod
- Advances in Gaussian Processes
- Advances in Models for Acoustic Processing
- Afternoon Session
- Afternoon Session
- Aggregating Classification Accuracy through Time - Classifying Single Trial EEG
- A Hidden Markov Dirichlet Process Model for Genetic Recombination in Open Ancestral Space
- A Humanlike Predictor of Facial Attractiveness
- A Kernel Method for the Two-Sample-Problem
- A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems
- A Local Learning Approach for Clustering
- Ambient Intelligence for Better Buildings
- Analysis of Contour Motions
- Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization
- Analysis of Representations for Domain Adaptation
- An Application of Reinforcement Learning to Aerobatic Helicopter Flight
- An Approach to Bounded Rationality
- An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models
- An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments
- An Information Theoretic Framework for Eukaryotic Gradient Sensing
- A Nonparametric Approach to Bottom-Up Visual Saliency
- A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments
- An Oracle Inequality for Clipped Regularized Risk Minimizers
- A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical
- A PAC-Bayes Risk Bound for General Loss Functions
- Approximate Correspondences in High Dimensions
- Approximate inference using planar graph decomposition
- A Probabilistic Algorithm Integrating Source Localization and Noise Suppression for MEG and EEG data
- A Rate-Distortion Approach to Joint Pattern Alignment
- A recipe for optimizing a time-histogram
- A Scalable Machine Learning Approach to Go
- A selective attention multi--chip system with dynamic synapses and spiking neurons
- A Small World Threshold for Economic Network Formation
- A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular St
- A Theory of Retinal Population Coding
- Attentional Processing on a Spike-Based VLSI Neural Network
- Attribute-efficient learning of linear threshold functions under unconcentrated distributions
- Automated Hierarchy Discovery for Planning in Partially Observable Domains
- Balanced Graph Matching
- Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure
- Bayesian Ensemble Learning
- Bayesian Image Super-resolution, Continued
- Bayesian Models of Human Learning and Inference
- Bayesian Policy Gradient Algorithms
- Blaise: A System for Interactive Development of High Performance Inference Algorithms
- Blind Motion Deblurring Using Image Statistics
- Blind source separation for over-determined delayed mixtures
- Boosting Structured Prediction for Imitation Learning
- Branch and Bound for Semi-Supervised Support Vector Machines
- Causal inference in sensorimotor integration
- Causality and feature selection
- Chained Boosting
- Clustering appearance and shape by learning jigsaws
- Clustering Under Prior Knowledge with Application to Image Segmentation
- Combining causal and similarity-based reasoning
- Comparative Gene Prediction using Conditional Random Fields
- Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees
- Computer Aided Diagnosis of Early Stage Cancer
- Conditional mean field
- Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data
- Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons
- Context Effects in Category Learning: An Investigation of Four Probabilistic Models
- Continuous Attractor Neural Networks
- Convergence of Laplacian Eigenmaps
- Convex Repeated Games and Fenchel Duality
- Correcting Sample Selection Bias by Unlabeled Data
- Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
- Current Trends in Brain-Computer Interfacing
- Data Integration for Classification Problems Employing Gaussian Process Priors
- Decoding the neural code
- Demonstrations
- Demonstrations
- Demonstrations
- Demonstrations
- Denoising and Dimension Reduction in Feature Space
- Detecting Humans via Their Pose
- Differential Entropic Clustering of Multivariate Gaussians
- Diffusion Tensor Imaging and Fiber Tracking of Human Brain Pathways
- Dirichlet-Enhanced Spam Filtering based on Biased Samples
- Distributed Inference in Dynamical Systems
- Distributed PCA and Network Anomaly Detection
- Doubly Stochastic Normalization for Spectral Clustering
- Dynamical Systems, Stochastic Processes and Bayesian Inference
- Dynamic Foreground/Background Extraction from Images and Videos using Random Patches
- Echo State Networks and Liquid State Machines
- Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning
- Efficient Learning of Sparse Representations with an Energy-Based Model
- Efficient Methods for Privacy Preserving Classification
- Efficient sparse coding algorithms, end-stopping and nCRF surround suppression
- Efficient Structure Learning of Markov Networks using L1-Regularization
- EHuM: Evaluation of Articulated Human Motion and Pose Estimation
- Emergence of conjunctive visual features by quadratic independent component analysis
- Emerging Capacity to Synthesize Data and Process: Application to the Biodiversity Paradox
- Energy-Based Models: Structured Learning Beyond Likelihoods
- Estimating Observation Functions in Dynamical Systems Using Unsupervised Regression
- Examining the Human Brain Mechanisms for Language, Memory, and Learning During Awake Neurosurgery
- Fast Computation of Graph Kernels
- Fast Iterative Kernel PCA
- Free Lunches: Insights from Behavioral Economics
- Fundamental Limitations of Spectral Clustering Methods
- Game Theoretic Algorithms for Protein-DNA binding
- Gaussian and Wishart Hyperkernels
- Gaussian Process Models for Discriminative Link Prediction
- Generalized Maximum Margin Clustering and Unsupervised Kernel Learning
- Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space
- Geometric entropy minimization (GEM) for anomaly detection and localization
- Global Optimization Challenges in High Resolution Protein Structure Prediction
- Graph-Based Visual Saliency
- Graph Regularization for Maximum Variance Unfolding with an Application to Sensor Localization
- Greedy Layer-Wise Training of Deep Networks
- Grounding Perception, Knowledge and Cognition in Sensori-Motor Experience
- Handling Advertisements of Unknown Quality in Search Advertising
- Hardware speech recognition using Reservoir Computing
- Hierarchical Dirichlet Processes with Random Effects
- Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms
- iLSTD: Convergence, Eligibility Traces, and Mountain Car
- Image Retrieval and Classification Using Local Distance Functions
- implicit Online Learning with Kernels
- Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions
- Inducing Metric Violations in Human Similarity Judgements
- Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood
- Inferring Network Structure from Co-Occurrences
- Information Bottleneck for Non Co-Occurrence Data
- Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons
- Intelligent Ink Processing
- Isotonic Conditional Random Fields and Local Sentiment Flow
- Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm
- Kernels on Structured Objects Through Nested Histograms
- Large Margin Component Analysis
- Large Margin Gaussian Mixture Models for Automatic Speech Recognition
- Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural P
- Large Scale Hidden Semi-Markov SVMs
- Large-Scale Sparsified Manifold Regularization
- Learnability and the doubling dimension
- Learning annotated hierarchies from relational data
- Learning Applied to Ground Robots: Sensing and Locomotion
- Learning Dense 3D Correspondence
- Learning from Multiple Sources
- Learning Motion Style Synthesis from Perceptual Observations
- Learning Nonparametric Models for Probabilistic Imitation
- Learning on Graph with Laplacian Regularization
- Learning Structural Equation Models for fMRI
- Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models
- Learning to be Bayesian without Supervision
- Learning to Compare Examples
- Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields
- Learning to parse images of articulated bodies
- Learning to Rank with Nonsmooth Cost Functions
- Learning to Traverse Image Manifolds
- Learning when test and training inputs have different distributions
- Learning with Hypergraphs: Clustering, Classification, and Embedding
- Linearly-solvable Markov decision problems
- Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning
- Logistic Regression for Single Trial EEG Classification
- Machine Learning for Multilingual Information Access
- Machine Learning for Natural Language Processing: New Developments and Challenges
- Manifold Denoising
- Map-Reduce for Machine Learning on Multicore
- Max-margin classification of incomplete data
- MIROCKET
- Mixture Regression for Covariate Shift
- MLLE: Modified Locally Linear Embedding Using Multiple Weights
- Modeling Dyadic Data with Binary Latent Features
- Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model
- Modeling Human Motion Using Binary Latent Variables
- Modelling transcriptional regulation using Gaussian Processes
- MoGo: exploration-exploitation in Monte-Carlo Go using UCT and patterns
- Morning Session
- Morning Session
- Multi-dynamic Bayesian Networks
- Multi-Instance Multi-Label Learning with Application to Scene Classification
- Multi-level Inference Workshop and Model Selection Game
- Multiple Instance Learning for Computer Aided Diagnosis
- Multiple timescales and uncertainty in motor adaptation
- Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General Sum Stochastic Games
- Multi-Task Feature Learning
- Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype
- Natural Actor-Critic for Road Traffic Optimisation
- Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints
- Neural Mechanisms of Auditory Pattern Processing and Pattern Learning in Songbirds
- Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation
- New directions on decoding mental states from fMRI data
- New Problems and Methods in Computational Biology
- Nonlinear physically-based models for decoding motor-cortical population activity
- Nonnegative Sparse PCA
- Non-rigid point set registration: Coherent Point Drift
- No-regret algorithms for Online Convex Programs
- Novel Applications of Dimensionality Reduction
- Online Classification for Complex Problems Using Simultaneous Projections
- Online Clustering of Moving Subspaces
- On-line Trading of Exploration and Exploitation
- On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts
- OntoGen
- On Transductive Regression
- Optimal Change-Detection and Spiking Neurons
- Optimal Single-Class Classification Strategies
- Ordinal Regression by Extended Binary Classification
- PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier
- Parallel Machine Learning Toolbox
- Parameter Expanded Variational Bayesian Methods
- Part-based Probabilistic Point Matching using Equivalence Constraints
- Particle Filtering for Nonparametric Bayesian Matrix Factorization
- Peripheral-Foveal Vision for Real-time Object Recognition
- PG-means: learning the number of clusters in data
- Predicting spike times from subthreshold dynamics of a neuron
- Prediction on a Graph with a Perceptron
- Randomized Clustering Forests for Building Fast and Discriminative Visual Vocabularies
- Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
- Real-time adaptive information-theoretic optimization of neurophysiological experiments
- Real-time bio-inspired retina simulation software with non-linear, adaptive and chromatic spatio-temporal processing
- Real-time bio-inspired retina simulation software with non-linear, adaptive and chromatic spatio-temporal processing
- Recursive Attribute Factoring
- Recursive ICA
- Relational Learning with Gaussian Processes
- Revealing Hidden Elements of Dynamical Systems
- RN-Spikes: A bio-inspired neural processor for face recognition
- Robotic Grasping of Novel Objects
- Sample Complexity of Policy Search with Known Dynamics
- Scalable Discriminative Learning for Natural Language Parsing and Translation
- Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds
- SHOGUN Machine Learning Toolbox
- Similarity by Composition
- Simplifying Mixture Models through Function Approximation
- Single Channel Speech Separation Using Layered Hidden Markov Models
- Sparse Kernel Orthonormalized PLS for feature extraction in large data sets
- Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation
- Sparse Representation for Signal Classification
- Speakers optimize information density through syntactic reduction
- Stability of $K$-Means Clustering
- Statistical Modeling of Images with Fields of Gaussian Scale Mixtures
- Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds
- Structure Learning in Markov Random Fields
- Subordinate class recognition using relational object models
- Support Vector Machines on a Budget
- Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Task
- Temporal Coding using the Response Properties of Spiking Neurons
- Temporal dynamics of information content carried by neurons in the primary visual cortex
- Testing of Deployable Learning & Decision Systems
- The First Annual Reinforcement Learning Competition
- The Neurodynamics of Belief Propagation on Binary Markov Random Fields
- Theory and Dynamics of Perceptual Bistability
- The Robustness-Performance Tradeoff in Markov Decision Processes
- The Role of Computational Methods in Creating a Systems Level View from Biological Data
- The Vocal Joystick
- Tighter PAC-Bayes Bounds
- Towards a general independent subspace analysis
- Towards a New Reinforcement Learning?
- Towards Zero-Training for Brain-Computer Interface Experiments
- Training Conditional Random Fields for Maximum Parse Accuracy
- TrueSkill: A Bayesian Skill Rating System
- Uncertainty, phase and oscillatory hippocampal recall
- Unified Inference for Variational Bayesian Linear Gaussian State-Space Models
- Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing
- User Adaptive Systems
- Using an event-based silicon retina for fast sensory motor processing
- Using Combinatorial Optimization within Max-Product Belief Propagation
- Workshop On Machine Learning Open Source Software
- Workshop session
- Workshop Session