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Semantic ScholarNIPS 2005: Vancouver, British Columbia, Canada
Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, NIPS 2005, December 5-8, 2005, Vancouver, British Columbia, Canada]. 2005- Pieter Abbeel, Varun Ganapathi, Andrew Y. Ng:
Learning vehicular dynamics, with application to modeling helicopters. 1-8 - Misha Ahrens, Quentin J. M. Huys, Liam Paninski:
Large-scale biophysical parameter estimation in single neurons via constrained linear regression. 25-32 - Yasemin Altun, David A. McAllester, Mikhail Belkin:
Margin Semi-Supervised Learning for Structured Variables. 33-40 - J. Ignacio Alvarez-Hamelin, Luca Dall'Asta, Alain Barrat, Alessandro Vespignani:
Large scale networks fingerprinting and visualization using the k-core decomposition. 41-50 - David Arathorn:
A Cortically-Plausible Inverse Problem Solving Method Applied to Recognizing Static and Kinematic 3D Objects. 59-66 - Andreas Argyriou, Mark Herbster, Massimiliano Pontil:
Combining Graph Laplacians for Semi-Supervised Learning. 67-74 - J. Andrew Bagnell, Andrew Y. Ng:
On Local Rewards and Scaling Distributed Reinforcement Learning. 91-98 - Chris L. Baker, Joshua B. Tenenbaum, Rebecca Saxe:
Bayesian models of human action understanding. 99-106 - Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux:
The Curse of Highly Variable Functions for Local Kernel Machines. 107-114 - Yoshua Bengio, Nicolas Le Roux, Pascal Vincent, Olivier Delalleau, Patrice Marcotte:
Convex Neural Networks. 123-130 - Gilles Blanchard, Masashi Sugiyama, Motoaki Kawanabe, Vladimir G. Spokoiny, Klaus-Robert Müller:
Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction. 131-138 - Brent Bryan, Jeff G. Schneider, Robert Nichol, Christopher J. Miller, Christopher R. Genovese, Larry A. Wasserman:
Active Learning For Identifying Function Threshold Boundaries. 163-170 - Rui M. Castro, Rebecca Willett, Robert D. Nowak:
Faster Rates in Regression via Active Learning. 179-186 - Abdullah Celik, Milutin Stanacevic, Gert Cauwenberghs:
Gradient Flow Independent Component Analysis in Micropower VLSI. 187-194 - Márton Danóczy, Richard H. R. Hahnloser:
Efficient estimation of hidden state dynamics from spike trains. 227-234 - Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
The Forgetron: A Kernel-Based Perceptron on a Fixed Budget. 259-266 - Ricky Der, Daniel Lee:
Beyond Gaussian Processes: On the Distributions of Infinite Networks. 275-282 - Inderjit S. Dhillon, Suvrit Sra:
Generalized Nonnegative Matrix Approximations with Bregman Divergences. 283-290 - Eizaburo Doi, Doru-Cristian Balcan, Michael S. Lewicki:
A Theoretical Analysis of Robust Coding over Noisy Overcomplete Channels. 307-314 - Guido Dornhege, Benjamin Blankertz, Matthias Krauledat, Florian Losch, Gabriel Curio, Klaus-Robert Müller:
Optimizing spatio-temporal filters for improving Brain-Computer Interfacing. 315-322 - Miroslav Dudík, Robert E. Schapire, Steven J. Phillips:
Correcting sample selection bias in maximum entropy density estimation. 323-330 - Austin I. Eliazar, Ronald Parr:
Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps. 339-346 - Yaakov Engel, Peter Szabó, Dmitry Volkinshtein:
Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods. 347-354 - Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák:
Two view learning: SVM-2K, Theory and Practice. 355-362 - Patrick Flaherty, Michael I. Jordan, Adam P. Arkin:
Robust design of biological experiments. 363-370 - Nando de Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang:
Fast Krylov Methods for N-Body Learning. 251-258 - Glenn Fung, Rómer Rosales, Balaji Krishnapuram:
Learning Rankings via Convex Hull Separation. 395-402 - Artur S. d'Avila Garcez, Luís C. Lamb, Dov M. Gabbay:
A Connectionist Model for Constructive Modal Reasoning. 403-410 - Thomas Gärtner, Quoc V. Le, Simon Burton, Alexander J. Smola, S. V. N. Vishwanathan:
Large-Scale Multiclass Transduction. 411-418 - Tao Geng, Bernd Porr, Florentin Wörgötter:
Fast biped walking with a reflexive controller and real-time policy searching. 427-434 - Sharon Goldwater, Thomas L. Griffiths, Mark Johnson:
Interpolating between types and tokens by estimating power-law generators. 459-466 - Yves Grandvalet, Johnny Mariéthoz, Samy Bengio:
A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification. 467-474 - Thomas L. Griffiths, Zoubin Ghahramani:
Infinite latent feature models and the Indian buffet process. 475-482 - Lacey Gunter, Ji Zhu:
Computing the Solution Path for the Regularized Support Vector Regression. 483-490 - Firas Hamze, Nando de Freitas:
Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs. 491-498 - Wentao Huang, Licheng Jiao, Tan Shan, Maoguo Gong:
Response Analysis of Neuronal Population with Synaptic Depression. 523-530 - Yunsong Huang, B. Keith Jenkins:
Non-iterative Estimation with Perturbed Gaussian Markov Processes. 531-538 - Viren Jain, Valentin P. Zhigulin, H. Sebastian Seung:
Representing Part-Whole Relationships in Recurrent Neural Networks. 563-570 - Rong Jin, Chris H. Q. Ding, Feng Kang:
A Probabilistic Approach for Optimizing Spectral Clustering. 571-578 - Jason K. Johnson, Dmitry M. Malioutov, Alan S. Willsky:
Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation. 579-586 - Nebojsa Jojic, Vladimir Jojic, Brendan J. Frey, Christopher Meek, David Heckerman:
Using epitomes to model genetic diversity: Rational design of HIV vaccines. 587-594 - Renaud Jolivet, Alexander Rauch, Hans-Rudolf Lüscher, Wulfram Gerstner:
Integrate-and-Fire models with adaptation are good enough. 595-602 - Anatoli Juditsky, Alexander V. Nazin, Alexandre B. Tsybakov, Nicolas Vayatis:
Generalization Error Bounds for Aggregation by Mirror Descent with Averaging. 603-610 - Sham M. Kakade, Matthias W. Seeger, Dean P. Foster:
Worst-Case Bounds for Gaussian Process Models. 619-626 - Ashish Kapoor, Yuan (Alan) Qi, Hyungil Ahn, Rosalind W. Picard:
Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification. 627-634 - Yan Karklin, Michael S. Lewicki:
Is Early Vision Optimized for Extracting Higher-order Dependencies? 635-642 - S. Sathiya Keerthi, Wei Chu:
A matching pursuit approach to sparse Gaussian process regression. 643-650 - Mikaela Keller, Samy Bengio, Siew Yeung Wong:
Benchmarking Non-Parametric Statistical Tests. 651-658 - Kristina Lisa Klinkner, Cosma Rohilla Shalizi, Marcelo Camperi:
Measuring Shared Information and Coordinated Activity in Neuronal Networks. 667-674 - O. Patrick Kreidl, Alan S. Willsky:
Inference with Minimal Communication: a Decision-Theoretic Variational Approach. 675-682 - Jeremy Kubica, Joseph Masiero, Andrew W. Moore, Robert Jedicke, Andrew J. Connolly:
Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery. 691-698 - Malte Kuss, Carl Edward Rasmussen:
Assessing Approximations for Gaussian Process Classification. 699-706 - John D. Lafferty, Larry A. Wasserman:
Rodeo: Sparse Nonparametric Regression in High Dimensions. 707-714 - François Laviolette, Mario Marchand, Mohak Shah:
A PAC-Bayes approach to the Set Covering Machine. 731-738 - Yann LeCun, Urs Muller, Jan Ben, Eric Cosatto, Beat Flepp:
Off-Road Obstacle Avoidance through End-to-End Learning. 739-746 - Jung Hoon Lee, Xiaolong Ma, Konstantin Likharev:
CMOL CrossNets: Possible Neuromorphic Nanoelectronic Circuits. 755-762 - Robert A. Legenstein, Wolfgang Maass:
A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity. 763-770 - Anna Levina, J. Michael Herrmann, Theo Geisel:
Dynamical Synapses Give Rise to a Power-Law Distribution of Neuronal Avalanches. 771-778 - Nicolas Loeff, Himanshu Arora, Alexander Sorokin, David A. Forsyth:
Efficient Unsupervised Learning for Localization and Detection in Object Categories. 811-818 - Aurelie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire:
Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. 819-826 - Wolfgang Maass, Prashant Joshi, Eduardo D. Sontag:
Principles of real-time computing with feedback applied to cortical microcircuit models. 835-842 - Sridhar Mahadevan, Mauro Maggioni:
Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions. 843-850 - Naoki Masuda, Shun-ichi Amari:
Modeling Memory Transfer and Saving in Cerebellar Motor Learning. 859-866 - Samuel McClure, Mark S. Gilzenrat, Jonathan D. Cohen:
An exploration-exploitation model based on norepinepherine and dopamine activity. 867-874 - Peter McCracken, Michael H. Bowling:
Online Discovery and Learning of Predictive State Representations. 875-882 - Keiji Miura, Masato Okada, Shun-ichi Amari:
Unbiased Estimator of Shape Parameter for Spiking Irregularities under Changing Environments. 891-898 - Baback Moghaddam, Yair Weiss, Shai Avidan:
Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms. 915-922 - Michael Mozer, Michael Shettel, Shaun Vecera:
Top-Down Control of Visual Attention: A Rational Account. 923-930 - Juan José Murillo-Fuentes, Sebastian Caro, Fernando Pérez-Cruz:
Gaussian Processes for Multiuser Detection in CDMA receivers. 939-946 - Iain Murray, David J. C. MacKay, Zoubin Ghahramani, John Skilling:
Nested sampling for Potts models. 947-954 - Boaz Nadler, Stéphane Lafon, Ronald R. Coifman, Ioannis G. Kevrekidis:
Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators. 955-962 - Srikantan S. Nagarajan, Hagai Attias, Kenneth E. Hild II, Kensuke Sekihara:
Stimulus Evoked Independent Factor Analysis of MEG Data with Large Background Activity. 963-970 - Amir Navot, Lavi Shpigelman, Naftali Tishby, Eilon Vaadia:
Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity. 996-1002 - XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan:
Divergences, surrogate loss functions and experimental design. 1011-1018