NeurIPS(NIPS) 2005 论文列表
Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, NIPS 2005, December 5-8, 2005, Vancouver, British Columbia, Canada].
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On the Convergence of Eigenspaces in Kernel Principal Component Analysis.
Cyclic Equilibria in Markov Games.
A Hierarchical Compositional System for Rapid Object Detection.
A Computational Model of Eye Movements during Object Class Detection.
Analysis of Spectral Kernel Design based Semi-supervised Learning.
A Domain Decomposition Method for Fast Manifold Learning.
Learning Influence among Interacting Markov Chains.
Modeling Neuronal Interactivity using Dynamic Bayesian Networks.
Learning Multiple Related Tasks using Latent Independent Component Analysis.
Separation of Music Signals by Harmonic Structure Modeling.
The Role of Top-down and Bottom-up Processes in Guiding Eye Movements during Visual Search.
Augmented Rescorla-Wagner and Maximum Likelihood Estimation.
Extracting Dynamical Structure Embedded in Neural Activity.
Soft Clustering on Graphs.
Modeling Neural Population Spiking Activity with Gibbs Distributions.
Message passing for task redistribution on sparse graphs.
Comparing the Effects of Different Weight Distributions on Finding Sparse Representations.
Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care.
Neural mechanisms of contrast dependent receptive field size in V1.
Active Bidirectional Coupling in a Cochlear Chip.
Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games.
Analyzing Auditory Neurons by Learning Distance Functions.
Distance Metric Learning for Large Margin Nearest Neighbor Classification.
Variational Bayesian Stochastic Complexity of Mixture Models.
A Bayes Rule for Density Matrices.
Group and Topic Discovery from Relations and Their Attributes.
Gaussian Process Dynamical Models.
Recovery of Jointly Sparse Signals from Few Random Projections.
Estimating the wrong Markov random field: Benefits in the computation-limited setting.
Multiple Instance Boosting for Object Detection.
Consistency of one-class SVM and related algorithms.
Kernels for gene regulatory regions.
Goal-Based Imitation as Probabilistic Inference over Graphical Models.
Generalization error bounds for classifiers trained with interdependent data.
Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares.
Affine Structure From Sound.
Structured Prediction via the Extragradient Method.
Temporally changing synaptic plasticity.
Silicon growth cones map silicon retina.
Sequence and Tree Kernels with Statistical Feature Mining.
Temporal Abstraction in Temporal-difference Networks.
Active Learning for Misspecified Models.
Describing Visual Scenes using Transformed Dirichlet Processes.
Sensory Adaptation within a Bayesian Framework for Perception.
Prediction and Change Detection.
A General and Efficient Multiple Kernel Learning Algorithm.
Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface.
Sparse Gaussian Processes using Pseudo-inputs.
Conditional Visual Tracking in Kernel Space.
Selecting Landmark Points for Sparse Manifold Learning.
Learning Shared Latent Structure for Image Synthesis and Robotic Imitation.
Fast Gaussian Process Regression using KD-Trees.
AER Building Blocks for Multi-Layer Multi-Chip Neuromorphic Vision Systems.
Learning Minimum Volume Sets.
A Bayesian Framework for Tilt Perception and Confidence.
The Information-Form Data Association Filter.
Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation.
On the Accuracy of Bounded Rationality: How Far from Optimal Is Fast and Frugal?.
An aVLSI Cricket Ear Model.
Identifying Distributed Object Representations in Human Extrastriate Visual Cortex.
Learning Depth from Single Monocular Images.
Logic and MRF Circuitry for Labeling Occluding and Thinline Visual Contours.
Dynamic Social Network Analysis using Latent Space Models.
Visual Encoding with Jittering Eyes.
TD(0) Leads to Better Policies than Approximate Value Iteration.
Generalization to Unseen Cases.
Cue Integration for Figure/Ground Labeling.
Preconditioner Approximations for Probabilistic Graphical Models.
Estimation of Intrinsic Dimensionality Using High-Rate Vector Quantization.
Off-policy Learning with Options and Recognizers.
Scaling Laws in Natural Scenes and the Inference of 3D Shape.
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects.
Neuronal Fiber Delineation in Area of Edema from Diffusion Weighted MRI.
Nonparametric inference of prior probabilities from Bayes-optimal behavior.
Variational EM Algorithms for Non-Gaussian Latent Variable Models.
Spiking Inputs to a Winner-take-all Network.
Bayesian model learning in human visual perception.
An Approximate Inference Approach for the PCA Reconstruction Error.
Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction.
How fast to work: Response vigor, motivation and tonic dopamine.
Divergences, surrogate loss functions and experimental design.
A Bayesian Spatial Scan Statistic.
Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity.
Optimal cue selection strategy.
Q-Clustering.
An Analog Visual Pre-Processing Processor Employing Cyclic Line Access in Only-Nearest-Neighbor-Interconnects Architecture.
Stimulus Evoked Independent Factor Analysis of MEG Data with Large Background Activity.
Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators.
Nested sampling for Potts models.
Gaussian Processes for Multiuser Detection in CDMA receivers.
Rate Distortion Codes in Sensor Networks.
Top-Down Control of Visual Attention: A Rational Account.
Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms.
Context as Filtering.
Consensus Propagation.
Unbiased Estimator of Shape Parameter for Spiking Irregularities under Changing Environments.
An Alternative Infinite Mixture Of Gaussian Process Experts.
Online Discovery and Learning of Predictive State Representations.
An exploration-exploitation model based on norepinepherine and dopamine activity.
Modeling Memory Transfer and Saving in Cerebellar Motor Learning.
Noise and the two-thirds power Law.
Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions.
Principles of real-time computing with feedback applied to cortical microcircuit models.
Ideal Observers for Detecting Motion: Correspondence Noise.
Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations.
Efficient Unsupervised Learning for Localization and Detection in Object Categories.
Asymptotics of Gaussian Regularized Least Squares.
Location-based activity recognition.
Radial Basis Function Network for Multi-task Learning.
From Lasso regression to Feature vector machine.
Dynamical Synapses Give Rise to a Power-Law Distribution of Neuronal Avalanches.
A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity.
CMOL CrossNets: Possible Neuromorphic Nanoelectronic Circuits.
Dual-Tree Fast Gauss Transforms.
Off-Road Obstacle Avoidance through End-to-End Learning.
A PAC-Bayes approach to the Set Covering Machine.
Fusion of Similarity Data in Clustering.
Fixing two weaknesses of the Spectral Method.
Rodeo: Sparse Nonparametric Regression in High Dimensions.
Assessing Approximations for Gaussian Process Classification.
Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery.
Generalization in Clustering with Unobserved Features.
Inference with Minimal Communication: a Decision-Theoretic Variational Approach.
Measuring Shared Information and Coordinated Activity in Neuronal Networks.
Robust Fisher Discriminant Analysis.
Benchmarking Non-Parametric Statistical Tests.
A matching pursuit approach to sparse Gaussian process regression.
Is Early Vision Optimized for Extracting Higher-order Dependencies?
Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification.
From Batch to Transductive Online Learning.
Worst-Case Bounds for Gaussian Process Models.
Generalization Error Bounds for Aggregation by Mirror Descent with Averaging.
Integrate-and-Fire models with adaptation are good enough.
Using epitomes to model genetic diversity: Rational design of HIV vaccines.
Walk-Sum Interpretation and Analysis of Gaussian Belief Propagation.
A Probabilistic Approach for Optimizing Spectral Clustering.
Representing Part-Whole Relationships in Recurrent Neural Networks.
Efficient Estimation of OOMs.
Bayesian Surprise Attracts Human Attention.
Learning Cue-Invariant Visual Responses.
Non-iterative Estimation with Perturbed Gaussian Markov Processes.
Response Analysis of Neuronal Population with Synaptic Depression.
Inferring Motor Programs from Images of Handwritten Digits.
Tensor Subspace Analysis.
Laplacian Score for Feature Selection.
Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs.
Computing the Solution Path for the Regularized Support Vector Regression.
Infinite latent feature models and the Indian buffet process.
A Probabilistic Interpretation of SVMs with an Application to Unbalanced Classification.
Interpolating between types and tokens by estimating power-law generators.
Metric Learning by Collapsing Classes.
Query by Committee Made Real.
Bayesian Sets.
Fast biped walking with a reflexive controller and real-time policy searching.
Products of Edge-perts.
Large-Scale Multiclass Transduction.
A Connectionist Model for Constructive Modal Reasoning.
Learning Rankings via Convex Hull Separation.
Statistical Convergence of Kernel CCA.
Mixture Modeling by Affinity Propagation.
Fast Krylov Methods for N-Body Learning.
Pattern Recognition from One Example by Chopping.
Robust design of biological experiments.
Two view learning: SVM-2K, Theory and Practice.
Learning to Control an Octopus Arm with Gaussian Process Temporal Difference Methods.
Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps.
Searching for Character Models.
Correcting sample selection bias in maximum entropy density estimation.
Optimizing spatio-temporal filters for improving Brain-Computer Interfacing.
A Theoretical Analysis of Robust Coding over Noisy Overcomplete Channels.
Transfer learning for text classification.
An Application of Markov Random Fields to Range Sensing.
Generalized Nonnegative Matrix Approximations with Bregman Divergences.
Beyond Gaussian Processes: On the Distributions of Infinite Networks.
Data-Driven Online to Batch Conversions.
The Forgetron: A Kernel-Based Perceptron on a Fixed Budget.
Norepinephrine and Neural Interrupts.
Coarse sample complexity bounds for active learning.
Efficient estimation of hidden state dynamics from spike trains.
Learning from Data of Variable Quality.
Size Regularized Cut for Data Clustering.
Layered Dynamic Textures.
Improved risk tail bounds for on-line algorithms.
Gradient Flow Independent Component Analysis in Micropower VLSI.
Faster Rates in Regression via Active Learning.
Subsequence Kernels for Relation Extraction.
Active Learning For Identifying Function Threshold Boundaries.
Saliency Based on Information Maximization.
Correlated Topic Models.
From Weighted Classification to Policy Search.
Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction.
Convex Neural Networks.
Non-Local Manifold Parzen Windows.
The Curse of Highly Variable Functions for Local Kernel Machines.
Bayesian models of human action understanding.
On Local Rewards and Scaling Distributed Reinforcement Learning.
Learning Topology with the Generative Gaussian Graph and the EM Algorithm.
Learning in Silicon: Timing is Everything.
Combining Graph Laplacians for Semi-Supervised Learning.
A Cortically-Plausible Inverse Problem Solving Method Applied to Recognizing Static and Kinematic 3D Objects.
Fast Information Value for Graphical Models.
Large scale networks fingerprinting and visualization using the k-core decomposition.
Margin Semi-Supervised Learning for Structured Variables.
Large-scale biophysical parameter estimation in single neurons via constrained linear regression.
Kernelized Infomax Clustering.
Policy-Gradient Methods for Planning.
Learning vehicular dynamics, with application to modeling helicopters.
