Classification via Minimum Incremental Coding Length (MICL)
John Wright, Yangyu Tao, Zhouchen Lin, Yi Ma, Heung-Yeung Shum
Optimal models of sound localization by barn owls
Brian Fischer
A Unified Near-Optimal Estimator For Dimension Reduction in $l_\alpha$ ($0<\alpha\leq 2$) Using Stable Random Projections
Ping Li, Trevor Hastie
Support Vector Machine Classification with Indefinite Kernels
Ronny Luss, Alexandre d'Aspremont
Nearest-Neighbor-Based Active Learning for Rare Category Detection
Jingrui He, Jaime Carbonell
A complexity measure for intuitive theories
Charles Kemp, Noah Goodman, Joshua Tenenbaum
Topmoumoute Online Natural Gradient Algorithm
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FilterBoost: Regression and Classification on Large Datasets
Joseph Bradley, Robert Schapire
Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains
Andrea Lecchini-Visintini, John Lygeros, Jan Maciejowski
Variational inference for Markov jump processes
Manfred Opper, Guido Sanguinetti
Cooled and Relaxed Survey Propagation for MRFs
Hai Leong Chieu, Wee Sun Lee, Yee Whye Teh
Cluster Stability for Finite Samples
Ohad Shamir, Naftali Tishby
Infinite State Bayes-Nets for Structured Domains
Max Welling, Ian Porteous, Evgeniy Bart
Spatial Latent Dirichlet Allocation
Xiaogang Wang, Eric Grimson
EEG-Based Brain-Computer Interaction: Improved Accuracy by Automatic Single-Trial Error Detection
Pierre Ferrez, José del R. Millán
People Tracking with the Laplacian Eigenmaps Latent Variable Model
Lu Zhengdong, Miguel Carreira-Perpinan, Cristian Sminchisescu
Kernels on Attributed Pointsets with Applications
Mehul Parsana, Sourangshu Bhattacharya, Chiru Bhattacharya, K. R. Ramakrishnan
Learning the structure of manifolds using random projections
Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma
Ensemble Clustering using Semidefinite Programming
Vikas Singh, Lopamudra Mukherjee, Jiming Peng, Jinhui Xu
Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms
Dominik Endres, Mike Oram, Johannes Schindelin, Peter Foldiak
Regularized Boost for Semi-Supervised Learning
Ke Chen, Shihai Wang
Inferring Elapsed Time from Stochastic Neural Processes
Misha Ahrens, Maneesh Sahani
An online Hebbian learning rule that performs Independent Component Analysis
Claudia Clopath, André Longtin, Wulfram Gerstner
A general agnostic active learning algorithm
Sanjoy Dasgupta, Daniel Hsu, Claire Monteleoni
A Spectral Regularization Framework for Multi-Task Structure Learning
Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying
HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation
Bing Zhao, Eric P. Xing
Compressed Regression
Shuheng Zhou, John Lafferty, Larry Wasserman
Stability Bounds for Non-i.i.d. Processes
Afshin Rostamizadeh, Mehryar Mohri
Selecting Observations against Adversarial Objectives
Andreas Krause, Brendan McMahan, Carlos Guestrin, Anupam Gupta
Learning with Transformation Invariant Kernels
Christian Walder, Olivier Chapelle
Anytime Induction of Cost-sensitive Trees
Saher Esmeir, Shaul Markovitch
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Buenau, Motoaki Kawanabe
Receptive Fields without Spike-Triggering
Jakob Macke, Günther Zeck, Matthias Bethge
On higher-order perceptron algorithms
Claudio Gentile, Fabio Vitale, Cristian Brotto
Scene Segmentation with CRFs Learned from Partially Labeled Images
Jakob Verbeek, Bill Triggs
What makes some POMDP problems easy to approximate?
David Hsu, Wee Sun Lee, Nan Rong
Contraction Properties of VLSI Cooperative Competitive Neural Networks of Spiking Neurons
Emre Neftci, Elisabetta Chicca, Giacomo Indiveri, Jean-Jeacques Slotine, Rodney Douglas
An Analysis of Convex Relaxations for MAP Estimation
Pawan Mudigonda, Vladimir Kolmogorov, Philip Torr
Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression
Sennay Ghebreab, Arnold Smeulders, Pieter Adriaans
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
Sebastian Gerwinn, Jakob Macke, Matthias Seeger, Matthias Bethge
Markov Chain Monte Carlo with People
Adam Sanborn, Thomas Griffiths
Statistical Analysis of Semi-Supervised Regression
John Lafferty, Larry Wasserman
Incremental Natural-Gradient Actor-Critic Algorithms
Shalabh Bhatnagar, Richard Sutton, Mohammad Ghavamzadeh, Mark Lee
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images
Matthias Bethge, Philipp Berens
Convex Clustering with Exemplar-Based Models
Danial Lashkari, Polina Golland
Transfer Learning using Kolmogorov Complexity: Basic Theory and Empirical Evaluations
M. M. Mahmud, Sylvian Ray
Random Sampling of States in Dynamic Programming
Chris Atkeson, Benjamin Stephens
The Infinite Gamma-Poisson Feature Model
Michalis Titsias
Better than least squares: comparison of objective functions for estimating linear-nonlinear models
Tatyana Sharpee
Retrieved context and the discovery of semantic structure
Vinayak Rao, Marc Howard
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems
Sajid Siddiqi, Byron Boots, Geoffrey Gordon
Boosting the Area under the ROC Curve
Phil Long, Rocco Servedio
Scan Strategies for Meteorological Radars
Victoria Manfredi, Jim Kurose, Don Towsley
Experience-Guided Search: A Theory of Attentional Control
Michael Mozer, David Baldwin
Theoretical Analysis of Heuristic Search Methods for Online POMDPs
Stephane Ross, Brahim Chaib-draa, Joelle Pineau
Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes
John Cunningham, Byron Yu, Krishna Shenoy, Maneesh Sahani
Comparing Bayesian models for multisensory cue combination without mandatory integration
Ulrik Beierholm, Konrad Kording, Ladan Shams, Wei Ji Ma
Learning Horizontal Connections in a Sparse Coding Model of Natural Images
Pierre Garrigues, Bruno Olshausen
Rapid Inference on a novel AND/OR graph: Detection, Segmentation and Parsing of Articulated Deformable Objects in Cluttered Backgrounds
Long Zhu, Yuanhao Chen, Chenxi Lin, Alan Yuille
SpAM: Sparse Additive Models
Pradeep Ravikumar, Han Liu, John Lafferty, Larry Wasserman
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons
Lars Buesing, Wolfgang Maass
Consistent Minimization of Clustering Objective Functions
Ulrike von Luxburg, Sebastien Bubeck, Stefanie Jegelka, Michael Kaufmann
Optimal ROC Curve for a Combination of Classifiers
Marco Barreno, Alvaro Cardenas, J.D. Tygar
Multi-task Gaussian Process Prediction
Chris Williams, Kian Ming Chai, Edwin Bonilla
Parallelizing Support Vector Machines on Distributed Computers
Edward Chang, Kaihua Zhu, Hao Wang, Hingjie Bai, Jian Li, Zhihuan Qiu, Hang Cui
Heterogeneous Component Analysis
Shigeyuki Oba, Motoaki Kawanabe, Klaus-Robert Müller, Shin Ishii
A probabilistic model for generating realistic lip movements from speech
Gwenn Englebienne, Tim Cootes, Magnus Rattray
Multi-Task Learning via Conic Programming
Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, Kiyoshi Asai
A neural network implementing optimal state estimation based on dynamic spike train decoding
Omer Bobrowski , Ron Meir, Shy Shoham, Yonina Eldar
Bundle Methods for Machine Learning
Alex Smola, S V N Vishwanathan, Quoc Le
GRIFT: A graphical model for inferring visual classification features from human data
Michael Ross, Andrew Cohen
Progressive mixture rules are deviation suboptimal
Jean-Yves Audibert
Colored Maximum Variance Unfolding
Le Song, Alex Smola, Karsten Borgwardt, Arthur Gretton
How SVMs can estimate quantiles and the median
Andreas CHRISTMANN, Ingo Steinwart
Bayesian Multi-View Learning
Shipeng Yu, Balaji Krishnapuram, Romer Rosales, Harald Steck, R. Bharat Rao
Temporal Difference with Eligibility Traces Derived from First Principles
Marcus Hutter, Shane Legg
A Randomized Algorithm for Large Scale Support Vector Learning
Krishnan Kumar, Chiru Bhattacharya, Ramesh Hariharan
Collective Inference on Markov Models for Modeling Bird Migration
Daniel Sheldon, M.A. Saleh Elmohamed, Dexter Kozen
Efficient Convex Relaxation for Transductive Support Vector Machine
Zenglin Xu, Rong Jin, Jianke Zhu, Irwin King, Michael Lyu
On Ranking in Survival Analysis: Bounds on the Concordance Index
Vikas Raykar, Harald Steck, Balaji Krishnapuram, Cary Dehing-Oberije, Philippe Lambin
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis
Venkat Chandrasekaran, Jason Johnson, Alan Willsky
Learning Visual Attributes
Vittorio Ferrari, Andrew Zisserman
The Generalized FITC Approximation
Andrew Naish-Guzman
Continuous Time Particle Filtering for fMRI
Lawrence Murray, Amos Storkey
Kernel Measures of Conditional Dependence
Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Schölkopf
One-Pass Boosting
Zafer Barutcuoglu, Phil Long, Rocco Servedio
Measuring Neural Synchrony by Message Passing
Justin Dauwels, François Vialatte, Tomasz Rutkowski, Andrzej Cichocki
Multiple-Instance Pruning For Learning Efficient Cascade Detectors
Cha Zhang, Paul Viola
Convex Relaxations of EM
Yuhong Guo, Dale Schuurmans
Stable Dual Dynamic Programming
Tao Wang, Daniel Lizotte, Michael Bowling, Dale Schuurmans
A Risk Minimization Principle for a Class of Parzen Estimators
Kristiaan Pelckmans, Johan Suykens, Bart De Moor
Exponential Family Predictive Representations of State
David Wingate, Satinder Singh Baveja
Modeling homophily and stochastic equivalence in symmetric relational data
Peter Hoff
Agreement-Based Learning
Percy Liang, Dan Klein, Michael Jordan
Competition Adds Complexity
Judy Goldsmith, Martin Mundhenk
Learning to classify complex patterns using a VLSI network of spiking neurons
Srinjoy Mitra, Giacomo Indiveri, Stefano Fusi
Extending position/phase-shift tuning to motion energy neurons improves velocity discrimination
Yiu Man LAM, Bertram Shi
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking
Markus Weimer, Alexandros Karatzoglou, Quoc Le, Alex Smola
Hierarchical Penalization
Marie Szafranski, Yves Grandvalet, Pierre Morizet-Mahoudeaux
Efficient multiple hyperparameter learning for log-linear models
Chuong Do, Chuan-Sheng Foo, Andrew Ng
Learning Bounds for Domain Adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman
Discriminative Log-Linear Grammars with Latent Variables
Slav Petrov, Dan Klein
Online Linear Regression and Its Application to Model-Based Reinforcement Learning
Alexander Strehl, Michael Littman
Mining Internet-Scale Software Repositories
erik linstead, paul rigor, sushil bajracharya, cristina lopes, Pierre Baldi
Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity
Robert Legenstein, Dejan Pecevski, Wolfgang Maass
Configuration Estimates Improve Pedestrian Finding
Duan Tran, David Forsyth
Efficient Bayesian Inference for Dynamically Changing Graphs
Ozgur Sumer, Umut Acar, Alexander T. Ihler, Ramgopal R. Mettu
The Noisy-Logical Distribution and its Application to Causal Inference
Alan Yuille, HongJing Lu
Modelling motion primitives and their timing in biologically executed movements
Ben Williams, Marc Toussaint, Amos Storkey
Distributed Inference for Latent Dirichlet Allocation
David Newman, Arthur Asuncion, Padhraic Smyth, Max Welling
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs
Ambuj Tewari, Peter Bartlett
A learning framework for nearest neighbor search
Lawrence Cayton, Sanjoy Dasgupta
Modeling Natural Sounds with Modulation Cascade Processes
Richard Turner, Maneesh Sahani
Regret Minimization in Games with Incomplete Information
Martin Zinkevich, Michael Johanson, Michael Bowling, Carmelo Piccione
Manifold Sculpting
Michael Gashler, Dan Ventura, Tony Martinez
Computational Equivalence of Fixed Points and No Regret Algorithms, and Convergence to Equilibria
Elad Hazan, Satyen Kale
Adaptive Online Gradient Descent
Peter Bartlett, Elad Hazan, Alexander Rakhlin
Catching Change-points with Lasso
Zaid Harchaoui, Céline LEVY-LEDUC
Discriminative Keyword Selection Using Support Vector Machines
Fred Richardson, William Campbell
Sequential Hypothesis Testing under Stochastic Deadlines
Peter Frazier, Angela Yu
A configurable analog VLSI neural network with spiking neurons and self-regulating plastic synapses
massimiliano giulioni, mario pannunzi, davide badoni, vittorio dante, paolo del giudice
The Distribution Family of Similarity Distances
Gertjan Burghouts, Arnold Smeulders, Jan-Mark Geusebroek
The Tradeoffs of Large Scale Learning
Leon Bottou, Olivier Bousquet
A Kernel Statistical Test of Independence
Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alex Smola
Discriminative K-means for Clustering
Jieping Ye, Zheng Zhao, Mingrui Wu
A General Boosting Method and its Application to Learning Ranking Functions for Web Search
Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun
Hidden Common Cause Relations in Relational Learning
Ricardo Silva, Wei Chu, Zoubin Ghahramani
Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks
Ben Carterette, Rosie Jones
The Price of Bandit Information for Online Optimization
Varsha Dani, Thomas Hayes, Kakade Sham
Bayesian Agglomerative Clustering with Coalescents
Yee Whye Teh, Hal Daume III, Daniel Roy
Collapsed Variational Inference for HDP
Yee Whye Teh, Kenichi Kurihara, Max Welling
Second Order Bilinear Discriminant Analysis for single trial EEG analysis
Christoforos Christoforou, Paul Sajda, Lucas C. Parra
Object Recognition by Scene Alignment
Bryan Russell, Antonio Torralba, Ce Liu, Rob Fergus, William Freeman
An Analysis of Inference with the Universum
Fabian Sinz, Olivier Chapelle, Alekh Agarwal, Bernhard Schölkopf
Local Algorithms for Approximate Inference in Minor-Excluded Graphs
Kyomin Jung, Devavrat Shah
Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization
XuanLong Nguyen, Martin Wainwright, Michael Jordan
The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information
John Langford, Tong Zhang
Blind channel identification for speech dereverberation using l1-norm sparse learning
Yuanqing Lin, Jingdong Chen, Youngmoo Kim, Daniel Lee
A Unified Model for Content Based Image Suggestion and Feature Selection
Sabri Boutemedjet, Djemel Ziou, Nizar Bouguila
Subspace-Based Face Recognition in Analog VLSI
Miguel Figueroa, Gonzalo Carvajal, Waldo Valenzuela
Computing Robust Counter-Strategies
Michael Johanson, Martin Zinkevich, Michael Bowling
Structured Learning with Approximate Inference
Alex Kulesza, Fernando Pereira
Augmented Functional Time Series Representation and Forecasting with Gaussian Processes
Nicolas Chapados, Yoshua Bengio
New Outer Bounds on the Marginal Polytope
David Sontag, Tommi Jaakkola
Random Features for Large-Scale Kernel Machines
Ali Rahimi, Benjamin Recht
The Infinite Markov Model
Daichi Mochihashi, Eiichiro Sumita
Learning to Rank Using Classification and Gradient Boosting
Ping Li, Christopher Burges, Qiang Wu
Non-parametric Modeling of Partially Ranked Data
Guy Lebanon, Yi Mao
Fast Variational Inference for Large-scale Internet Diagnosis
John Platt, Emre Kıcıman, David Maltz
Efficient Inference for Distributions on Permutations
Jonathan Huang, Carlos Guestrin, Leonidas Guibas
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition
Maryam Mahdaviani, Tanzeem Choudhury
Bayes-Adaptive POMDPs
Stephane Ross, Joelle Pineau
DIFFRAC: a discriminative and flexible framework for clustering
Francis Bach, Zaid Harchaoui
The discriminant center-surround hypothesis for bottom-up saliency
Vijay Mahadevan, Dashan Gao, Nuno Vasconcelos
A Bayesian Framework for Cross-Situational Word-Learning
Michael Frank, Noah Goodman, Joshua Tenenbaum
Privacy-Preserving Belief Propagation and Sampling
Michael Kearns, Jinsong Tan, Jennifer Wortman
A Probabilistic Approach to Language Change
Alexandre Bouchard-Côté, Percy Liang, Thomas Griffiths, Dan Klein
Boosting Algorithms for Maximizing the Soft Margin
Manfred Warmuth, Karen Glocer, Gunnar Rätsch
Automatic Generation of Social Tags for Music Recommendation
Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux, Stephen Green
Supervised Topic Models
David Blei, Jon McAuliffe
Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks
Alex Graves, Santiago Fernandez, Juergen Schmidhuber
Predictive Matrix-Variate t Models
Shenghuo Zhu, Kai Yu, Yihong Gong
Active Preference Learning with Discrete Choice Data
Brochu Eric, Nando de Freitas, Abhijeet Ghosh
Linear programming analysis of loopy belief propagation for weighted matching
Sujay Sanghavi, Dmitry Malioutov, Alan Willsky
Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning
Gerald Tesauro, Rajarshi Das, Hoi Chan, Jeffrey Kephart, David Levine, Freeman Rawson, Charles Lefurgy
Congruence between model and human attention reveals unique signatures of critical visual events
Robert Peters, Laurent Itti
Locality and low-dimensions in the prediction of natural experience from fMRI
Francois Meyer, Greg Stephens
Fitted Q-iteration in continuous action-space MDPs
Csaba Szepesvari, András Antos, Remi Munos
Expectation Maximization, Posterior Constraints, and Statistical Alignment
Kuzman Ganchev, Joao Graca, Ben Taskar
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach
José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes
Discriminative Batch Mode Active Learning
Yuhong Guo, Dale Schuurmans
Message Passing for Max-weight Independent Set
Sujay Sanghavi, Devavrat Shah, Alan Willsky
Learning the 2-D Topology of Images
Nicolas Le Roux, Yoshua Bengio, Pascal Lamblin, Marc Joliveau, Balázs Kégl
Hippocampal Contributions to Control: The Third Way
Máté Lengyel, Peter Dayan
Gaussian Process Models for Link Analysis and Transfer Learning
Kai Yu, Wei Chu
TrueSkill Through Time: Revisiting the History of Chess
Pierre Dangauthier, Ralf Herbrich, Tom Minka, Thore Graepel
Sparse deep belief net model for visual area V2
Honglak Lee, Ekanadham Chaitanya , Andrew Ng
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations
Amir Globerson, Tommi Jaakkola
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes
Ruslan Salakhutdinov, Geoffrey Hinton
A Multiplicative Weights Algorithm for Apprenticeship Learning
Umar Syed, Robert Schapire
Modeling image patches with a directed hierarchy of Markov random fields
Simon Osindero, Geoffrey Hinton
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods
Alessandro Lazaric, Marcello Restelli, Andrea Bonarini
A Bayesian LDA-based model for semi-supervised part-of-speech tagging
Kristina Toutanova, Mark Johnson
Globally Convergent Updates and Analysis for Automatic Relevance Determination
David Wipf, Srikantan Nagarajan
Robust Regression with Twinned Gaussian Processes
Andrew Naish-Guzman
Invariant Common Spatial Patterns: Alleviating Nonstationarities in Brain-Computer Interfaci
Benjamin Blankertz, Motoaki Kawanabe, Ryota Tomioka, Friederike Hohlefeld, Vadim Nikulin, Klaus-Robert Müller
Hierarchical Apprenticeship Learning with Applications to Quadruped Locomotion
J. Zico Kolter, Pieter Abbeel, Andrew Ng
CPR for CSPs: A Probabilistic Relaxation of Constraint Propagation
Luis E. Ortiz
Combined discriminative and generative articulated pose and non-rigid shape estimation
Leonid Sigal, Alexandru Balan , Michael Black
Neural characterization in partially observed populations of spiking neurons
Jonathan Pillow, Peter Latham
The rat as particle filter
Nathaniel Daw, Aaron Courville
The Value of Labeled and Unlabeled Examples when the Model is Imperfect
Kaushik Sinha, Mikhail Belkin
Probabilistic Matrix Factorization Applied to the Netflix Rating Prediction Problem
Ruslan Salakhutdinov, Andriy Mnih
A Bayesian Model of Conditioned Perception
Alan Stocker, Eero Simoncelli
Efficient Principled Learning of Thin Junction Trees
Anton Chechetka, Carlos Guestrin
Semi-Supervised Multitask Learning
Qiuhua Liu, Xuejun Liao, Lawrence Carin
Sparse Overcomplete Latent Variable Decomposition of Counts Data
Madhusudana Shashanka, Bhiksha Raj, Paris Smaragdis
Learning Monotonic Transformations for Classification
Andrew Howard, Tony Jebara
Convex Learning with Invariances
Choon Hui Teo, Amir Globerson, Sam Roweis, Alex Smola
On Sparsity and Overcompleteness in Image Models
Pietro Berkes, Richard Turner, Maneesh Sahani
Resampling Methods for Protein Structure Prediction with Rosetta
Benjamin Blum, David Baker, Michael Jordan, Philip Bradley, Rhiju Das, David Kim
Testing for Homogeneity with Kernel Fisher Discriminant Analysis
Zaid Harchaoui, Francis Bach, Moulines Eric
Density Estimation under Independent Similarly Distributed Sampling Assumptions
Tony Jebara, Yingbo Song, Kapil Thadani
Multi-Stage Monte Carlo Approximation for Fast Generalized Data Summations
Michael Holmes, Alexander Gray, Charles Isbell
Multiple Instance Active Learning
Burr Settles, Mark Craven, Soumya Ray
Predicting human gaze using low-level saliency combined with face detection
Moran Cerf, Jonathan Harel, Wolfgang Einhaeuser, Christof Koch
Estimating disparity with confidence from energy neurons
Eric Kong Chau Tsang, Bertram Shi
Loop Series and Bethe Variational Bounds in Attractive Graphical Models
Erik Sudderth, Martin Wainwright, Alan Willsky
Learning with Tree-Averaged Densities and Distributions
Sergey Kirshner
Variational Inference for Diffusion Processes
Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor
Random Projections for Manifold Learning
Chinmay Hegde, Michael Wakin, Richard Baraniuk
Receding Horizon Differential Dynamic Programming
Yuval Tassa, Tom Erez, William Smart
An in-silico Neural Model of Dynamic Routing through Neuronal Coherence
Devarajan Sridharan, Brian Percival, Arthur John, Kwabena Boahen
Sparse Feature Learning for Deep Belief Networks
Marc'Aurelio Ranzato, Y-Lan Bou, Yann LeCun
Discovering Weakly-Interacting Factors in a Complex Stochastic Process
Charlie Frogner, Avi Pfeffer
Bayesian Policy Learning with Trans-Dimensional MCMC
Matthew Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra