Poster session information will become available in the future.
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation
Yee Whye Teh · David Newman · Max Welling
An Information Theoretic Framework for Eukaryotic Gradient Sensing
Joseph Kimmel · Richard Salter · Peter Thomas
A Nonparametric Approach to Bottom-Up Visual Saliency
Wolf Kienzle · Felix A Wichmann · Bernhard Schölkopf · Matthias Franz
A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments
Daniel Navarro · Tom Griffiths
A Probabilistic Algorithm Integrating Source Localization and Noise Suppression for MEG and EEG data
Johanna M Zumer · Hagai Attias · Kensuke Sekihara · Srikantan Nagarajan
Attentional Processing on a Spike-Based VLSI Neural Network
Yingxue Wang · Rodney J Douglas · Shih-Chii Liu
Attribute-efficient learning of linear threshold functions under unconcentrated distributions
Phil Long · Rocco A Servedio
Automated Hierarchy Discovery for Planning in Partially Observable Domains
Laurent Charlin · Pascal Poupart · Romy Shioda
Branch and Bound for Semi-Supervised Support Vector Machines
Olivier Chapelle · Vikas Sindhwani · Sathiya Selvaraj Keerthi
Comparative Gene Prediction using Conditional Random Fields
Jade P Vinson · David DeCaprio · Stacey Luoma · James Galagan
Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data
Ping Li · Kenneth W Church · Trevor Hastie
Context Effects in Category Learning: An Investigation of Four Probabilistic Models
Michael Mozer · Michael Jones · Michael Shettel
Distributed Inference in Dynamical Systems
Stanislav Funiak · Carlos Guestrin · Mark A Paskin · Rahul Sukthankar
Efficient sparse coding algorithms, end-stopping and nCRF surround suppression
Honglak Lee · Alexis Battle · Raina Rajat · Andrew Y Ng
Efficient Structure Learning of Markov Networks using L1-Regularization
Su-In Lee · Varun Ganapathi · Daphne Koller
Estimating Observation Functions in Dynamical Systems Using Unsupervised Regression
ali rahimi · Benjamin Recht
Gaussian Process Models for Discriminative Link Prediction
Kai Yu · Wei Chu · Shipeng Yu · Volker Tresp · Zhao Xu
Graph Regularization for Maximum Variance Unfolding with an Application to Sensor Localization
Kilian Q Weinberger · Fei Sha · Qihui Zhu · Lawrence Saul
iLSTD: Convergence, Eligibility Traces, and Mountain Car
Alborz Geramifard · Michael Bowling · Martin A Zinkevich · Richard Sutton
Image Retrieval and Classification Using Local Distance Functions
Andrea Frome · Yoram Singer · Jitendra Malik
implicit Online Learning with Kernels
Li Cheng · Vishwanathan S V N · Dale Schuurmans · Shaojun Wang · Terry Caelli
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm
Robert Jenssen · Torbjorn Eltoft · Mark A Girolami · Deniz Erdogmus
Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural P
Amit Gore · Shantanu Chakrabartty
Learning Nonparametric Models for Probabilistic Imitation
David Grimes · Daniel Rashid · Rajesh PN Rao
Learning with Hypergraphs: Clustering, Classification, and Embedding
Denny Zhou · Jiayuan Huang · Bernhard Schölkopf
Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General Sum Stochastic Games
Chris D Murray · Geoffrey Gordon
Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints
Carla Gomes · Ashish Sabharwal · Bart Selman
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension
Manfred K. Warmuth · Dima Kuzmin
Training Conditional Random Fields for Maximum Parse Accuracy
Samuel Gross · Olga Russakovsky · Chuong B Do · Serafim Batzoglou