Book
Advances in Neural Information Processing Systems 17 (NIPS 2004)
Edited by:
L. Saul and Y. Weiss and L. Bottou
- PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data Mario Marchand, Mohak Shah
- A Second Order Cone programming Formulation for Classifying Missing Data Chiranjib Bhattacharyya, Pannagadatta Shivaswamy, Alex Smola
- Learning first-order Markov models for control Pieter Abbeel, Andrew Ng
- Who's In the Picture Tamara Berg, Alexander Berg, Jaety Edwards, David Forsyth
- Optimal Aggregation of Classifiers and Boosting Maps in Functional Magnetic Resonance Imaging Vladimir Koltchinskii, Manel Martínez-ramón, Stefan Posse
- Binet-Cauchy Kernels Alex Smola, S.v.n. Vishwanathan
- Synergistic Face Detection and Pose Estimation with Energy-Based Models Margarita Osadchy, Matthew Miller, Yann Cun
- The power of feature clustering: An application to object detection Shai Avidan, Moshe Butman
- Synergies between Intrinsic and Synaptic Plasticity in Individual Model Neurons Jochen Triesch
- A Probabilistic Model for Online Document Clustering with Application to Novelty Detection Jian Zhang, Zoubin Ghahramani, Yiming Yang
- Non-Local Manifold Tangent Learning Yoshua Bengio, Martin Monperrus
- Maximal Margin Labeling for Multi-Topic Text Categorization Hideto Kazawa, Tomonori Izumitani, Hirotoshi Taira, Eisaku Maeda
- Conditional Random Fields for Object Recognition Ariadna Quattoni, Michael Collins, Trevor Darrell
- Inference, Attention, and Decision in a Bayesian Neural Architecture Angela J. Yu, Peter Dayan
- Exponential Family Harmoniums with an Application to Information Retrieval Max Welling, Michal Rosen-zvi, Geoffrey E. Hinton
- Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation Erik Sudderth, Michael Mandel, William Freeman, Alan Willsky
- An Investigation of Practical Approximate Nearest Neighbor Algorithms Ting Liu, Andrew Moore, Ke Yang, Alexander Gray
- Hierarchical Distributed Representations for Statistical Language Modeling John Blitzer, Fernando Pereira, Kilian Q. Weinberger, Lawrence Saul
- Discrete profile alignment via constrained information bottleneck Sean O'rourke, Gal Chechik, Robin Friedman, Eleazar Eskin
- Multiple Relational Embedding Roland Memisevic, Geoffrey E. Hinton
- Conditional Models of Identity Uncertainty with Application to Noun Coreference Andrew McCallum, Ben Wellner
- Outlier Detection with One-class Kernel Fisher Discriminants Volker Roth
- Kernel Projection Machine: a New Tool for Pattern Recognition Laurent Zwald, Gilles Blanchard, Pascal Massart, Régis Vert
- Seeing through water Alexei Efros, Volkan Isler, Jianbo Shi, Mirkó Visontai
- Generative Affine Localisation and Tracking John Winn, Andrew Blake
- Schema Learning: Experience-Based Construction of Predictive Action Models Michael Holmes, Charles Jr.
- Machine Learning Applied to Perception: Decision Images for Gender Classification Felix A. Wichmann, Arnulf Graf, Heinrich Bülthoff, Eero Simoncelli, Bernhard Schölkopf
- An Information Maximization Model of Eye Movements Laura Renninger, James Coughlan, Preeti Verghese, Jitendra Malik
- Learning Hyper-Features for Visual Identification Andras Ferencz, Erik Learned-miller, Jitendra Malik
- Parametric Embedding for Class Visualization Tomoharu Iwata, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas Griffiths, Joshua Tenenbaum
- A Topographic Support Vector Machine: Classification Using Local Label Configurations Johannes Mohr, Klaus Obermayer
- New Criteria and a New Algorithm for Learning in Multi-Agent Systems Rob Powers, Yoav Shoham
- The Cerebellum Chip: an Analog VLSI Implementation of a Cerebellar Model of Classical Conditioning Constanze Hofstoetter, Manuel Gil, Kynan Eng, Giacomo Indiveri, Matti Mintz, Jörg Kramer, Paul Verschure
- Pictorial Structures for Molecular Modeling: Interpreting Density Maps Frank Dimaio, George Phillips, Jude Shavlik
- Support Vector Classification with Input Data Uncertainty Jinbo Bi, Tong Zhang
- Planning for Markov Decision Processes with Sparse Stochasticity Maxim Likhachev, Sebastian Thrun, Geoffrey J. Gordon
- Expectation Consistent Free Energies for Approximate Inference Manfred Opper, Ole Winther
- Using Machine Learning to Break Visual Human Interaction Proofs (HIPs) Kumar Chellapilla, Patrice Simard
- Making Latin Manuscripts Searchable using gHMM's Jaety Edwards, Yee Teh, Roger Bock, Michael Maire, Grace Vesom, David Forsyth
- Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning Jerry Zhu, Jaz Kandola, Zoubin Ghahramani, John Lafferty
- Sparse Coding of Natural Images Using an Overcomplete Set of Limited Capacity Units Eizaburo Doi, Michael Lewicki
- Optimal Information Decoding from Neuronal Populations with Specific Stimulus Selectivity Marcelo Montemurro, Stefano Panzeri
- Implicit Wiener Series for Higher-Order Image Analysis Matthias Franz, Bernhard Schölkopf
- Following Curved Regularized Optimization Solution Paths Saharon Rosset
- Learning, Regularization and Ill-Posed Inverse Problems Lorenzo Rosasco, Andrea Caponnetto, Ernesto Vito, Francesca Odone, Umberto Giovannini
- Density Level Detection is Classification Ingo Steinwart, Don Hush, Clint Scovel
- Learning Syntactic Patterns for Automatic Hypernym Discovery Rion Snow, Daniel Jurafsky, Andrew Ng
- Joint Tracking of Pose, Expression, and Texture using Conditionally Gaussian Filters Tim Marks, J. Roddey, Javier Movellan, John Hershey
- Multiple Alignment of Continuous Time Series Jennifer Listgarten, Radford Neal, Sam Roweis, Andrew Emili
- Hierarchical Clustering of a Mixture Model Jacob Goldberger, Sam Roweis
- Hierarchical Bayesian Inference in Networks of Spiking Neurons Rajesh PN Rao
- Efficient Kernel Discriminant Analysis via QR Decomposition Tao Xiong, Jieping Ye, Qi Li, Ravi Janardan, Vladimir Cherkassky
- Semi-parametric Exponential Family PCA Sajama Sajama, Alon Orlitsky
- Self-Tuning Spectral Clustering Lihi Zelnik-manor, Pietro Perona
- The Entire Regularization Path for the Support Vector Machine Saharon Rosset, Robert Tibshirani, Ji Zhu, Trevor Hastie
- The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space Robert Jenssen, Deniz Erdogmus, Jose Principe, Torbjørn Eltoft
- Experts in a Markov Decision Process Eyal Even-dar, Sham M. Kakade, Yishay Mansour
- Neighbourhood Components Analysis Jacob Goldberger, Geoffrey E. Hinton, Sam Roweis, Russ R. Salakhutdinov
- Learning Gaussian Process Kernels via Hierarchical Bayes Anton Schwaighofer, Volker Tresp, Kai Yu
- Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis Tobias Blaschke, Laurenz Wiskott
- Solitaire: Man Versus Machine Xiang Yan, Persi Diaconis, Paat Rusmevichientong, Benjamin Roy
- A Machine Learning Approach to Conjoint Analysis Olivier Chapelle, Zaïd Harchaoui
- Intrinsically Motivated Reinforcement Learning Nuttapong Chentanez, Andrew Barto, Satinder Singh
- Joint Probabilistic Curve Clustering and Alignment Scott Gaffney, Padhraic Smyth
- Triangle Fixing Algorithms for the Metric Nearness Problem Suvrit Sra, Joel Tropp, Inderjit Dhillon
- Economic Properties of Social Networks Sham M. Kakade, Michael Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri
- Beat Tracking the Graphical Model Way Dustin Lang, Nando Freitas
- Large-Scale Prediction of Disulphide Bond Connectivity Jianlin Cheng, Alessandro Vullo, Pierre Baldi
- An Application of Boosting to Graph Classification Taku Kudo, Eisaku Maeda, Yuji Matsumoto
- Multi-agent Cooperation in Diverse Population Games K. Wong, S. Lim, Z. Gao
- Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution Hyun Park, Te Lee
- Dependent Gaussian Processes Phillip Boyle, Marcus Frean
- Synchronization of neural networks by mutual learning and its application to cryptography Einat Klein, Rachel Mislovaty, Ido Kanter, Andreas Ruttor, Wolfgang Kinzel
- Semigroup Kernels on Finite Sets Marco Cuturi, Jean-philippe Vert
- A Hidden Markov Model for de Novo Peptide Sequencing Bernd Fischer, Volker Roth, Jonas Grossmann, Sacha Baginsky, Wilhelm Gruissem, Franz Roos, Peter Widmayer, Joachim Buhmann
- Instance-Based Relevance Feedback for Image Retrieval Giorgio Gia\-cin\-to, Fabio Roli
- Learning Preferences for Multiclass Problems Fabio Aiolli, Alessandro Sperduti
- Mass Meta-analysis in Talairach Space Finn Nielsen
- Result Analysis of the NIPS 2003 Feature Selection Challenge Isabelle Guyon, Steve Gunn, Asa Ben-Hur, Gideon Dror
- Sub-Microwatt Analog VLSI Support Vector Machine for Pattern Classification and Sequence Estimation Shantanu Chakrabartty, Gert Cauwenberghs
- Maximum Margin Clustering Linli Xu, James Neufeld, Bryce Larson, Dale Schuurmans
- On the Adaptive Properties of Decision Trees Clayton Scott, Robert Nowak
- Kernel Methods for Implicit Surface Modeling Joachim Giesen, Simon Spalinger, Bernhard Schölkopf
- Neural Network Computation by In Vitro Transcriptional Circuits Jongmin Kim, John Hopfield, Erik Winfree
- Similarity and Discrimination in Classical Conditioning: A Latent Variable Account Aaron C. Courville, Nathaniel Daw, David Touretzky
- Boosting on Manifolds: Adaptive Regularization of Base Classifiers Ligen Wang, Balázs Kégl
- Surface Reconstruction using Learned Shape Models Jan Solem, Fredrik Kahl
- Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity Sander Bohte, Michael C. Mozer
- Maximum Likelihood Estimation of Intrinsic Dimension Elizaveta Levina, Peter Bickel
- Distributed Information Regularization on Graphs Adrian Corduneanu, Tommi Jaakkola
- The Convergence of Contrastive Divergences Alan L. Yuille
- Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning Liam Paninski
- Incremental Algorithms for Hierarchical Classification Nicolò Cesa-bianchi, Claudio Gentile, Andrea Tironi, Luca Zaniboni
- On Semi-Supervised Classification Balaji Krishnapuram, David Williams, Ya Xue, Lawrence Carin, Mário Figueiredo, Alexander Hartemink
- Joint MRI Bias Removal Using Entropy Minimization Across Images Erik Learned-miller, Parvez Ahammad
- Chemosensory Processing in a Spiking Model of the Olfactory Bulb: Chemotopic Convergence and Center Surround Inhibition Baranidharan Raman, Ricardo Gutierrez-osuna
- Using Random Forests in the Structured Language Model Peng Xu, Frederick Jelinek
- Exploration-Exploitation Tradeoffs for Experts Algorithms in Reactive Environments Daniela Farias, Nimrod Megiddo
- Unsupervised Variational Bayesian Learning of Nonlinear Models Antti Honkela, Harri Valpola
- VDCBPI: an Approximate Scalable Algorithm for Large POMDPs Pascal Poupart, Craig Boutilier
- A Generalized Bradley-Terry Model: From Group Competition to Individual Skill Tzu-kuo Huang, Chih-jen Lin, Ruby Weng
- Rate- and Phase-coded Autoassociative Memory Máté Lengyel, Peter Dayan
- Constraining a Bayesian Model of Human Visual Speed Perception Alan A. Stocker, Eero Simoncelli
- Efficient Kernel Machines Using the Improved Fast Gauss Transform Changjiang Yang, Ramani Duraiswami, Larry S. Davis
- Responding to Modalities with Different Latencies Fredrik Bissmarck, Hiroyuki Nakahara, Kenji Doya, Okihide Hikosaka
- Two-Dimensional Linear Discriminant Analysis Jieping Ye, Ravi Janardan, Qi Li
- Assignment of Multiplicative Mixtures in Natural Images Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan
- Convergence and No-Regret in Multiagent Learning Michael Bowling
- Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters Evan Smith, Michael Lewicki
- Active Learning for Anomaly and Rare-Category Detection Dan Pelleg, Andrew Moore
- A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound Dori Peleg, Ron Meir
- Semi-supervised Learning with Penalized Probabilistic Clustering Zhengdong Lu, Todd Leen
- A Direct Formulation for Sparse PCA Using Semidefinite Programming Alexandre D'aspremont, Laurent Ghaoui, Michael Jordan, Gert Lanckriet
- Stable adaptive control with online learning H. Kim, Andrew Ng
- The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters Alan L. Yuille
- Contextual Models for Object Detection Using Boosted Random Fields Antonio Torralba, Kevin P. Murphy, William Freeman
- An Auditory Paradigm for Brain-Computer Interfaces N. Hill, Thomas Lal, Karin Bierig, Niels Birbaumer, Bernhard Schölkopf
- Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms Nicolò Cesa-bianchi, Claudio Gentile, Luca Zaniboni
- Markov Networks for Detecting Overalpping Elements in Sequence Data Mark Craven, Joseph Bockhorst
- Co-Validation: Using Model Disagreement on Unlabeled Data to Validate Classification Algorithms Omid Madani, David Pennock, Gary Flake
- Co-Training and Expansion: Towards Bridging Theory and Practice Maria-florina Balcan, Avrim Blum, Ke Yang
- Probabilistic Inference of Alternative Splicing Events in Microarray Data Ofer Shai, Brendan J. Frey, Quaid Morris, Qun Pan, Christine Misquitta, Benjamin Blencowe
- Semi-supervised Learning by Entropy Minimization Yves Grandvalet, Yoshua Bengio
- Detecting Significant Multidimensional Spatial Clusters Daniel Neill, Andrew Moore, Francisco Pereira, Tom M. Mitchell
- Message Errors in Belief Propagation Alexander Ihler, John Fisher, Alan Willsky
- Methods Towards Invasive Human Brain Computer Interfaces Thomas Lal, Thilo Hinterberger, Guido Widman, Michael Schröder, N. Hill, Wolfgang Rosenstiel, Christian Elger, Niels Birbaumer, Bernhard Schölkopf
- A Three Tiered Approach for Articulated Object Action Modeling and Recognition Le Lu, Gregory Hager, Laurent Younes
- Temporal-Difference Networks Richard S. Sutton, Brian Tanner
- Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits Wolfgang Maass, Robert Legenstein, Nils Bertschinger
- The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data Oliver Williams, Andrew Blake, Roberto Cipolla
- Generalization Error and Algorithmic Convergence of Median Boosting Balázs Kégl
- Supervised Graph Inference Jean-philippe Vert, Yoshihiro Yamanishi
- Confidence Intervals for the Area Under the ROC Curve Corinna Cortes, Mehryar Mohri
- Maximising Sensitivity in a Spiking Network Anthony Bell, Lucas Parra
- Probabilistic Computation in Spiking Populations Richard Zemel, Rama Natarajan, Peter Dayan, Quentin Huys
- Theory of localized synfire chain: characteristic propagation speed of stable spike pattern Kosuke Hamaguchi, Masato Okada, Kazuyuki Aihara
- Semi-supervised Learning on Directed Graphs Dengyong Zhou, Thomas Hofmann, Bernhard Schölkopf
- Class-size Independent Generalization Analsysis of Some Discriminative Multi-Category Classification Tong Zhang
- ℓ₀-norm Minimization for Basis Selection David Wipf, Bhaskar Rao
- The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees Ofer Dekel, Shai Shalev-shwartz, Yoram Singer
- Modelling Uncertainty in the Game of Go David Stern, Thore Graepel, David MacKay
- Spike Sorting: Bayesian Clustering of Non-Stationary Data Aharon Bar-hillel, Adam Spiro, Eran Stark
- Harmonising Chorales by Probabilistic Inference Moray Allan, Christopher Williams
- Nearly Tight Bounds for the Continuum-Armed Bandit Problem Robert Kleinberg
- Mistake Bounds for Maximum Entropy Discrimination Philip Long, Xinyu Wu
- A Harmonic Excitation State-Space Approach to Blind Separation of Speech Rasmus Olsson, Lars K. Hansen
- Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection Koji Tsuda, Gunnar Rätsch, Manfred K. K. Warmuth
- Blind One-microphone Speech Separation: A Spectral Learning Approach Francis Bach, Michael Jordan
- Resolving Perceptual Aliasing In The Presence Of Noisy Sensors Guy Shani, Ronen Brafman
- Kernels for Multi--task Learning Charles Micchelli, Massimiliano Pontil
- Variational Minimax Estimation of Discrete Distributions under KL Loss Liam Paninski
- Online Bounds for Bayesian Algorithms Sham M. Kakade, Andrew Ng
- Analysis of a greedy active learning strategy Sanjoy Dasgupta
- A Cost-Shaping LP for Bellman Error Minimization with Performance Guarantees Daniela Farias, Benjamin Roy
- Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks Joris M. Mooij, Hilbert Kappen
- Bayesian inference in spiking neurons Sophie Deneve
- Computing regularization paths for learning multiple kernels Francis Bach, Romain Thibaux, Michael Jordan
- Saliency-Driven Image Acuity Modulation on a Reconfigurable Array of Spiking Silicon Neurons R. Vogelstein, Udayan Mallik, Eugenio Culurciello, Gert Cauwenberghs, Ralph Etienne-Cummings
- Common-Frame Model for Object Recognition Pierre Moreels, Pietro Perona
- Brain Inspired Reinforcement Learning Françcois Rivest, Yoshua Bengio, John Kalaska
- Semi-supervised Learning via Gaussian Processes Neil Lawrence, Michael Jordan
- Parallel Support Vector Machines: The Cascade SVM Hans Graf, Eric Cosatto, Léon Bottou, Igor Dourdanovic, Vladimir Vapnik
- Sampling Methods for Unsupervised Learning Rob Fergus, Andrew Zisserman, Pietro Perona
- Limits of Spectral Clustering Ulrike Luxburg, Olivier Bousquet, Mikhail Belkin
- Using the Equivalent Kernel to Understand Gaussian Process Regression Peter Sollich, Christopher Williams
- Newscast EM Wojtek Kowalczyk, Nikos Vlassis
- Dynamic Bayesian Networks for Brain-Computer Interfaces Pradeep Shenoy, Rajesh PN Rao
- Edge of Chaos Computation in Mixed-Mode VLSI - A Hard Liquid Felix Schürmann, Karlheinz Meier, Johannes Schemmel
- Breaking SVM Complexity with Cross-Training Léon Bottou, Jason Weston, Gökhan Bakir
- Linear Multilayer Independent Component Analysis for Large Natural Scenes Yoshitatsu Matsuda, Kazunori Yamaguchi
- Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale Haidong Wang, Eran Segal, Asa Ben-Hur, Daphne Koller, Douglas Brutlag
- Proximity Graphs for Clustering and Manifold Learning Richard Zemel, Miguel Carreira-Perpiñán
- Fast Rates to Bayes for Kernel Machines Ingo Steinwart, Clint Scovel
- Discriminant Saliency for Visual Recognition from Cluttered Scenes Dashan Gao, Nuno Vasconcelos
- Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation Yuanqing Lin, Daniel Lee
- Algebraic Set Kernels with Application to Inference Over Local Image Representations Amnon Shashua, Tamir Hazan
- The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces Dragomir Anguelov, Praveen Srinivasan, Hoi-cheung Pang, Daphne Koller, Sebastian Thrun, James Davis
- Maximum-Margin Matrix Factorization Nathan Srebro, Jason Rennie, Tommi Jaakkola
- Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization Fei Sha, Lawrence Saul
- Efficient Out-of-Sample Extension of Dominant-Set Clusters Massimiliano Pavan, Marcello Pelillo
- Instance-Specific Bayesian Model Averaging for Classification Shyam Visweswaran, Gregory Cooper
- Hierarchical Eigensolver for Transition Matrices in Spectral Methods Chakra Chennubhotla, Allan Jepson
- Exponentiated Gradient Algorithms for Large-margin Structured Classification Peter Bartlett, Michael Collins, Ben Taskar, David McAllester
- A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie
- Semi-Markov Conditional Random Fields for Information Extraction Sunita Sarawagi, William W. Cohen
- Adaptive Manifold Learning Jing Wang, Zhenyue Zhang, Hongyuan Zha
- Euclidean Embedding of Co-Occurrence Data Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby
- Heuristics for Ordering Cue Search in Decision Making Peter Todd, Anja Dieckmann
- Coarticulation in Markov Decision Processes Khashayar Rohanimanesh, Robert Platt, Sridhar Mahadevan, Roderic Grupen
- Integrating Topics and Syntax Thomas Griffiths, Mark Steyvers, David Blei, Joshua Tenenbaum
- Object Classification from a Single Example Utilizing Class Relevance Metrics Michael Fink
- A Large Deviation Bound for the Area Under the ROC Curve Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth
- Adaptive Discriminative Generative Model and Its Applications Ruei-sung Lin, David Ross, Jongwoo Lim, Ming-Hsuan Yang
- Optimal sub-graphical models Mukund Narasimhan, Jeff A. Bilmes
- Modeling Conversational Dynamics as a Mixed-Memory Markov Process Tanzeem Choudhury, Sumit Basu
- Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices Nathan Srebro, Noga Alon, Tommi Jaakkola
- Incremental Learning for Visual Tracking Jongwoo Lim, David Ross, Ruei-sung Lin, Ming-Hsuan Yang
- Face Detection --- Efficient and Rank Deficient Wolf Kienzle, Matthias Franz, Bernhard Schölkopf, Gökhan Bakir
- A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaadia, Yoram Singer
- On-Chip Compensation of Device-Mismatch Effects in Analog VLSI Neural Networks Miguel Figueroa, Seth Bridges, Chris Diorio
- At the Edge of Chaos: Real-time Computations and Self-Organized Criticality in Recurrent Neural Networks Nils Bertschinger, Thomas Natschläger, Robert Legenstein
- Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM Juan Coz, Gustavo Bayón, Jorge Díez, Oscar Luaces, Antonio Bahamonde, Carlos Sañudo
- Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes Yee Teh, Michael Jordan, Matthew Beal, David Blei
- Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model Taro Toyoizumi, Jean-pascal Pfister, Kazuyuki Aihara, Wulfram Gerstner
- Approximately Efficient Online Mechanism Design David C. Parkes, Dimah Yanovsky, Satinder Singh
- Comparing Beliefs, Surveys, and Random Walks Erik Aurell, Uri Gordon, Scott Kirkpatrick
- Theories of Access Consciousness Michael Colagrosso, Michael C. Mozer
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