Advances in Neural Information Processing Systems 14 (NIPS 2001)
The papers below appear in Advances in Neural Information Processing Systems 14 edited by T.G. Dietterich and S. Becker and Z. Ghahramani.They are proceedings from the conference, "Neural Information Processing Systems 2001."
- Modeling Temporal Structure in Classical Conditioning Aaron C. Courville, David S. Touretzky
- Motivated Reinforcement Learning Peter Dayan
- Probabilistic principles in unsupervised learning of visual structure: human data and a model Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Nathan Intrator
- Fragment Completion in Humans and Machines David Jacobs, Bas Rokers, Archisman Rudra, Zili Liu
- Natural Language Grammar Induction Using a Constituent-Context Model Dan Klein, Christopher D. Manning
- The Emergence of Multiple Movement Units in the Presence of Noise and Feedback Delay Michael Kositsky, Andrew G. Barto
- A Rational Analysis of Cognitive Control in a Speeded Discrimination Task Michael C. Mozer, Michael D. Colagrosso, David E. Huber
- A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing S. Narayanan, Daniel Jurafsky
- Grammar Transfer in a Second Order Recurrent Neural Network Michiro Negishi, Stephen J. Hanson
- Generalizable Relational Binding from Coarse-coded Distributed Representations Randall C. O'Reilly, R. S. Busby
- A Model of the Phonological Loop: Generalization and Binding Randall C. O'Reilly, R. Soto
- Grammatical Bigrams Mark A. Paskin
- Causal Categorization with Bayes Nets Bob Rehder
- Constructing Distributed Representations Using Additive Clustering Wheeler Ruml
- Reinforcement Learning and Time Perception -- a Model of Animal Experiments Jonathan L. Shapiro, J. Wearden
- A Quantitative Model of Counterfactual Reasoning Daniel Yarlett, Michael Ramscar
- Bayesian morphometry of hippocampal cells suggests same-cell somatodendritic repulsion Giorgio A. Ascoli, Alexei V. Samsonovich
- Modularity in the motor system: decomposition of muscle patterns as combinations of time-varying synergies A. D'avella, M. C. Tresch
- Receptive field structure of flow detectors for heading perception J. A. Beintema, M. Lappe, Alexander C. Berg
- Classifying Single Trial EEG: Towards Brain Computer Interfacing Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller
- Orientational and Geometric Determinants of Place and Head-direction Neil Burgess, Tom Hartley
- Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway Gal Chechik, Amir Globerson, M. J. Anderson, E. D. Young, Israel Nelken, Naftali Tishby
- A Maximum-Likelihood Approach to Modeling Multisensory Enhancement H. Colonius, A. Diederich
- ACh, Uncertainty, and Cortical Inference Peter Dayan, Angela J. Yu
- Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field O. Donchin, Reza Shadmehr
- Exact differential equation population dynamics for integrate-and-fire neurons Julian Eggert, Berthold Bäuml
- Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex Yun Gao, Michael J. Black, Elie Bienenstock, Shy Shoham, John P. Donoghue
- A theory of neural integration in the head-direction system Richard Hahnloser, Xiaohui Xie, H. S. Seung
- 3 state neurons for contextual processing Ádám Kepecs, S. Raghavachari
- Associative memory in realistic neuronal networks Peter E. Latham
- Self-regulation Mechanism of Temporally Asymmetric Hebbian Plasticity N. Matsumoto, M. Okada
- Information-Geometric Decomposition in Spike Analysis Hiroyuki Nakahara, Shun-ichi Amari
- Eye movements and the maturation of cortical orientation selectivity Antonino Casile, Michele Rucci
- Characterizing Neural Gain Control using Spike-triggered Covariance Odelia Schwartz, E.J. Chichilnisky, Eero P. Simoncelli
- Correlation Codes in Neuronal Populations Maoz Shamir, Haim Sompolinsky
- Why Neuronal Dynamics Should Control Synaptic Learning Rules Jesper Tegnér, Ádám Kepecs
- Effective Size of Receptive Fields of Inferior Temporal Visual Cortex Neurons in Natural Scenes Thomas P. Trappenberg, Edmund T. Rolls, Simon M. Stringer
- Activity Driven Adaptive Stochastic Resonance Gregor Wenning, Klaus Obermayer
- Spike timing and the coding of naturalistic sounds in a central auditory area of songbirds B. D. Wright, Kamal Sen, William Bialek, A. J. Doupe
- Neural Implementation of Bayesian Inference in Population Codes Si Wu, Shun-ichi Amari
- Generating velocity tuning by asymmetric recurrent connections Xiaohui Xie, Martin A. Giese
- Sampling Techniques for Kernel Methods Dimitris Achlioptas, Frank Mcsherry, Bernhard Schölkopf
- Geometrical Singularities in the Neuromanifold of Multilayer Perceptrons Shun-ichi Amari, Hyeyoung Park, Tomoko Ozeki
- The Noisy Euclidean Traveling Salesman Problem and Learning Mikio L. Braun, Joachim M. Buhmann
- On the Generalization Ability of On-Line Learning Algorithms Nicolò Cesa-bianchi, Alex Conconi, Claudio Gentile
- On Kernel-Target Alignment Nello Cristianini, John Shawe-Taylor, André Elisseeff, Jaz S. Kandola
- PAC Generalization Bounds for Co-training Sanjoy Dasgupta, Michael L. Littman, David A. McAllester
- Analysis of Sparse Bayesian Learning Anita C. Faul, Michael E. Tipping
- Algorithmic Luckiness Ralf Herbrich, Robert C. Williamson
- Distribution of Mutual Information Marcus Hutter
- Information Geometrical Framework for Analyzing Belief Propagation Decoder Shiro Ikeda, Toshiyuki Tanaka, Shun-ichi Amari
- Novel iteration schemes for the Cluster Variation Method Hilbert J. Kappen, Wim Wiegerinck
- Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms Roni Khardon, Dan Roth, Rocco A. Servedio
- Small-World Phenomena and the Dynamics of Information Jon M. Kleinberg
- Kernel Machines and Boolean Functions Adam Kowalczyk, Alex J. Smola, Robert C. Williamson
- Boosting and Maximum Likelihood for Exponential Models Guy Lebanon, John D. Lafferty
- Means, Correlations and Bounds Martijn Leisink, Bert Kappen
- A Variational Approach to Learning Curves Dörthe Malzahn, Manfred Opper
- Entropy and Inference, Revisited Ilya Nemenman, F. Shafee, William Bialek
- Asymptotic Universality for Learning Curves of Support Vector Machines Manfred Opper, Robert Urbanczik
- On the Convergence of Leveraging Gunnar Rätsch, Sebastian Mika, Manfred K. K. Warmuth
- Scaling Laws and Local Minima in Hebbian ICA Magnus Rattray, Gleb Basalyga
- Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks M. Schmitt
- On the Concentration of Spectral Properties John Shawe-Taylor, Nello Cristianini, Jaz S. Kandola
- Gaussian Process Regression with Mismatched Models Peter Sollich
- Information-Geometrical Significance of Sparsity in Gallager Codes Toshiyuki Tanaka, Shiro Ikeda, Shun-ichi Amari
- Fast Parameter Estimation Using Green's Functions K. Wong, F. Li
- Generalization Performance of Some Learning Problems in Hilbert Functional Spaces T. Zhang
- Semi-supervised MarginBoost Florence D'alché-buc, Yves Grandvalet, Christophe Ambroise
- Rao-Blackwellised Particle Filtering via Data Augmentation Christophe Andrieu, Nando D. Freitas, Arnaud Doucet
- Thin Junction Trees Francis R. Bach, Michael I. Jordan
- The Infinite Hidden Markov Model Matthew J. Beal, Zoubin Ghahramani, Carl E. Rasmussen
- Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering Mikhail Belkin, Partha Niyogi
- Duality, Geometry, and Support Vector Regression J. Bi, Kristin P. Bennett
- Latent Dirichlet Allocation David M. Blei, Andrew Y. Ng, Michael I. Jordan
- Incorporating Invariances in Non-Linear Support Vector Machines Olivier Chapelle, Bernhard Schölkopf
- A Generalization of Principal Components Analysis to the Exponential Family Michael Collins, S. Dasgupta, Robert E. Schapire
- Convolution Kernels for Natural Language Michael Collins, Nigel Duffy
- A Parallel Mixture of SVMs for Very Large Scale Problems Ronan Collobert, Samy Bengio, Yoshua Bengio
- Pranking with Ranking Koby Crammer, Yoram Singer
- Spectral Kernel Methods for Clustering Nello Cristianini, John Shawe-Taylor, Jaz S. Kandola
- TAP Gibbs Free Energy, Belief Propagation and Sparsity Lehel Csató, Manfred Opper, Ole Winther
- Adaptive Nearest Neighbor Classification Using Support Vector Machines Carlotta Domeniconi, Dimitrios Gunopulos
- Learning from Infinite Data in Finite Time Pedro Domingos, Geoff Hulten
- A kernel method for multi-labelled classification André Elisseeff, Jason Weston
- Approximate Dynamic Programming via Linear Programming Daniela Farias, Benjamin V. Roy
- Adaptive Sparseness Using Jeffreys Prior Mário Figueiredo
- Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM Shai Fine, Katya Scheinberg
- KLD-Sampling: Adaptive Particle Filters Dieter Fox
- Fast, Large-Scale Transformation-Invariant Clustering Brendan J. Frey, Nebojsa Jojic
- Product Analysis: Learning to Model Observations as Products of Hidden Variables Brendan J. Frey, Anitha Kannan, Nebojsa Jojic
- Very loopy belief propagation for unwrapping phase images Brendan J. Frey, Ralf Koetter, Nemanja Petrovic
- Discriminative Direction for Kernel Classifiers Polina Golland
- Escaping the Convex Hull with Extrapolated Vector Machines Patrick Haffner
- Kernel Feature Spaces and Nonlinear Blind Souce Separation Stefan Harmeling, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller
- The Method of Quantum Clustering David Horn, Assaf Gottlieb
- Active Information Retrieval Tommi Jaakkola, Hava T. Siegelmann
- Online Learning with Kernels Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
- A Dynamic HMM for On-line Segmentation of Sequential Data Jens Kohlmorgen, Steven Lemm
- Minimax Probability Machine Gert Lanckriet, Laurent E. Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan
- (Not) Bounding the True Error John Langford, Rich Caruana
- An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games Michael L. Littman, Michael J. Kearns, Satinder P. Singh
- Quantizing Density Estimators Peter Meinicke, Helge Ritter
- Linear-time inference in Hierarchical HMMs Kevin P. Murphy, Mark A. Paskin
- On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes Andrew Y. Ng, Michael I. Jordan
- On Spectral Clustering: Analysis and an algorithm Andrew Y. Ng, Michael I. Jordan, Yair Weiss
- Learning Hierarchical Structures with Linear Relational Embedding Alberto Paccanaro, Geoffrey E. Hinton
- Matching Free Trees with Replicator Equations Marcello Pelillo
- MIME: Mutual Information Minimization and Entropy Maximization for Bayesian Belief Propagation Anand Rangarajan, Alan L. Yuille
- Infinite Mixtures of Gaussian Process Experts Carl E. Rasmussen, Zoubin Ghahramani
- Global Coordination of Local Linear Models Sam T. Roweis, Lawrence K. Saul, Geoffrey E. Hinton
- Multiplicative Updates for Classification by Mixture Models Lawrence K. Saul, Daniel D. Lee
- Covariance Kernels from Bayesian Generative Models Matthias Seeger
- Probabilistic Abstraction Hierarchies Eran Segal, Daphne Koller, Dirk Ormoneit
- Dynamic Time-Alignment Kernel in Support Vector Machine Hiroshi Shimodaira, Ken-ichi Noma, Mitsuru Nakai, Shigeki Sagayama
- Agglomerative Multivariate Information Bottleneck Noam Slonim, Nir Friedman, Naftali Tishby
- Bayesian time series classification Peter Sykacek, Stephen J. Roberts
- Partially labeled classification with Markov random walks Martin Szummer, Tommi Jaakkola
- The Unified Propagation and Scaling Algorithm Yee W. Teh, Max Welling
- Risk Sensitive Particle Filters Sebastian Thrun, John Langford, Vandi Verma
- Learning Discriminative Feature Transforms to Low Dimensions in Low Dimentions Kari Torkkola
- A New Discriminative Kernel From Probabilistic Models Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller
- K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms Pascal Vincent, Yoshua Bengio
- Multi Dimensional ICA to Separate Correlated Sources Roland Vollgraf, Klaus Obermayer
- Tree-based reparameterization for approximate inference on loopy graphs Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky
- Learning Lateral Interactions for Feature Binding and Sensory Segmentation Heiko Wersing
- Products of Gaussians Christopher Williams, Felix V. Agakov, Stephen N. Felderhof
- Iterative Double Clustering for Unsupervised and Semi-Supervised Learning Ran El-Yaniv, Oren Souroujon
- The Concave-Convex Procedure (CCCP) Alan L. Yuille, Anand Rangarajan
- Reducing multiclass to binary by coupling probability estimates B. Zadrozny
- Blind Source Separation via Multinode Sparse Representation Michael Zibulevsky, Pavel Kisilev, Yehoshua Y. Zeevi, Barak A. Pearlmutter
- Spectral Relaxation for K-means Clustering Hongyuan Zha, Xiaofeng He, Chris Ding, Ming Gu, Horst D. Simon
- A General Greedy Approximation Algorithm with Applications T. Zhang
- EM-DD: An Improved Multiple-Instance Learning Technique Qi Zhang, Sally A. Goldman
- Kernel Logistic Regression and the Import Vector Machine Ji Zhu, Trevor Hastie
- Citcuits for VLSI Implementation of Temporally Asymmetric Hebbian Learning A. Bofill, D. P. Thompson, Alan F. Murray
- Stochastic Mixed-Signal VLSI Architecture for High-Dimensional Kernel Machines Roman Genov, Gert Cauwenberghs
- Orientation-Selective aVLSI Spiking Neurons Shih-Chii Liu, Jörg Kramer, Giacomo Indiveri, Tobi Delbrück, Rodney J. Douglas
- An Efficient Clustering Algorithm Using Stochastic Association Model and Its Implementation Using Nanostructures Takashi Morie, Tomohiro Matsuura, Makoto Nagata, Atsushi Iwata
- Learning Spike-Based Correlations and Conditional Probabilities in Silicon Aaron P. Shon, David Hsu, Chris Diorio
- Analog Soft-Pattern-Matching Classifier using Floating-Gate MOS Technology Toshihiko Yamasaki, Tadashi Shibata
- Intransitive Likelihood-Ratio Classifiers Jeff Bilmes, Gang Ji, Marina Meila
- Relative Density Nets: A New Way to Combine Backpropagation with HMM's Andrew D. Brown, Geoffrey E. Hinton
- A Sequence Kernel and its Application to Speaker Recognition William M. Campbell
- ALGONQUIN - Learning Dynamic Noise Models From Noisy Speech for Robust Speech Recognition Brendan J. Frey, Trausti T. Kristjansson, Li Deng, Alex Acero
- Audio-Visual Sound Separation Via Hidden Markov Models John R. Hershey, Michael Casey
- Estimating the Reliability of ICA Projections Frank C. Meinecke, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller
- Speech Recognition with Missing Data using Recurrent Neural Nets S. Parveen, P. Green
- Speech Recognition using SVMs N. Smith, Mark Gales
- Sequential Noise Compensation by Sequential Monte Carlo Method K. Yao, S. Nakamura
- A Neural Oscillator Model of Auditory Selective Attention Stuart N. Wrigley, Guy J. Brown
- Perceptual Metamers in Stereoscopic Vision B. T. Backus
- The g Factor: Relating Distributions on Features to Distributions on Images James M. Coughlan, Alan L. Yuille
- Categorization by Learning and Combining Object Parts Bernd Heisele, Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso Poggio
- Modeling the Modulatory Effect of Attention on Human Spatial Vision Laurent Itti, Jochen Braun, Christof Koch
- Grouping and dimensionality reduction by locally linear embedding Marzia Polito, Pietro Perona
- Learning Body Pose via Specialized Maps Rómer Rosales, Stan Sclaroff
- A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-Depth Silvio P. Sabatini, Fabio Solari, Giulia Andreani, Chiara Bartolozzi, Giacomo M. Bisio
- The Fidelity of Local Ordinal Encoding Javid Sadr, Sayan Mukherjee, Keith Thoresz, Pawan Sinha
- Unsupervised Learning of Human Motion Models Yang Song, Luis Goncalves, Pietro Perona
- Transform-invariant Image Decomposition with Similarity Templates Chris Stauffer, Erik Miller, Kinh Tieu
- Contextual Modulation of Target Saliency Antonio Torralba
- Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade Paul Viola, Michael Jones
- A Rotation and Translation Invariant Discrete Saliency Network Lance R. Williams, John W. Zweck
- Grouping with Bias Stella X. Yu, Jianbo Shi
- Switch Packet Arbitration via Queue-Learning Timothy X. Brown
- Model Based Population Tracking and Automatic Detection of Distribution Changes Igor V. Cadez, P. S. Bradley
- Bayesian Predictive Profiles With Applications to Retail Transaction Data Igor V. Cadez, Padhraic Smyth
- Tempo tracking and rhythm quantization by sequential Monte Carlo Ali Taylan Cemgil, Bert Kappen
- Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference Nicolas Chapados, Yoshua Bengio, Pascal Vincent, Joumana Ghosn, Charles Dugas, Ichiro Takeuchi, Linyan Meng
- Improvisation and Learning Judy A. Franklin
- Using Vocabulary Knowledge in Bayesian Multinomial Estimation Thomas L. Griffiths, Joshua B. Tenenbaum
- Cobot: A Social Reinforcement Learning Agent Charles Lee Isbell Jr., Christian R. Shelton
- Optimising Synchronisation Times for Mobile Devices Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, Michael J. Taylor
- Prodding the ROC Curve: Constrained Optimization of Classifier Performance Michael C. Mozer, Robert Dodier, Michael D. Colagrosso, Cesar Guerra-Salcedo, Richard Wolniewicz
- Hyperbolic Self-Organizing Maps for Semantic Navigation Jorg Ontrup, Helge Ritter
- Learning a Gaussian Process Prior for Automatically Generating Music Playlists John C. Platt, Christopher J. C. Burges, Steven Swenson, Christopher Weare, Alice Zheng
- A Bayesian Network for Real-Time Musical Accompaniment Christopher Raphael
- The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank Matthew Richardson, Pedro Domingos
- Active Learning in the Drug Discovery Process Manfred K. K. Warmuth, Gunnar Rätsch, Michael Mathieson, Jun Liao, Christian Lemmen
- Face Recognition Using Kernel Methods Ming-Hsuan Yang
- Active Portfolio-Management based on Error Correction Neural Networks Hans-Georg Zimmermann, Ralph Neuneier, Ralph Grothmann
- Reinforcement Learning with Long Short-Term Memory Bram Bakker
- Playing is believing: The role of beliefs in multi-agent learning Yu-Han Chang, Leslie Pack Kaelbling
- Batch Value Function Approximation via Support Vectors Thomas G. Dietterich, Xin Wang
- Convergence of Optimistic and Incremental Q-Learning Eyal Even-dar, Yishay Mansour
- Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning Evan Greensmith, Peter L. Bartlett, Jonathan Baxter
- Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement Learning Gregory Z. Grudic, Lyle H. Ungar
- Multiagent Planning with Factored MDPs Carlos Guestrin, Daphne Koller, Ronald Parr
- A Natural Policy Gradient Sham M. Kakade
- Incremental A* S. Koenig, M. Likhachev
- Model-Free Least-Squares Policy Iteration Michail G. Lagoudakis, Ronald Parr
- Predictive Representations of State Michael L. Littman, Richard S. Sutton
- The Steering Approach for Multi-Criteria Reinforcement Learning Shie Mannor, Nahum Shimkin
- Efficient Resources Allocation for Markov Decision Processes Rémi Munos
- Direct value-approximation for factored MDPs Dale Schuurmans, Relu Patrascu
- Stabilizing Value Function Approximation with the BFBP Algorithm Xin Wang, Thomas G. Dietterich