NeurIPS(NIPS) 2007 论文列表Advances in Neural Information Processing Systems 19, Proceedings of the Twentieth Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 4-7, 2006. |
A Probabilistic Algorithm Integrating Source Localization and Noise Suppression of MEG and EEG data.
Unsupervised Learning of a Probabilistic Grammar for Object Detection and Parsing.
Multi-Instance Multi-Label Learning with Application to Scene Classification.
Learning with Hypergraphs: Clustering, Classification, and Embedding.
MLLE: Modified Locally Linear Embedding Using Multiple Weights.
Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms.
Simplifying Mixture Models through Function Approximation.
Doubly Stochastic Normalization for Spectral Clustering.
Nonnegative Sparse PCA.
Stochastic Relational Models for Discriminative Link Prediction.
Optimal Change-Detection and Spiking Neurons.
The Robustness-Performance Tradeoff in Markov Decision Processes.
A Local Learning Approach for Clustering.
A Scalable Machine Learning Approach to Go.
Particle Filtering for Nonparametric Bayesian Matrix Factorization.
Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization.
A Switched Gaussian Process for Estimating Disparity and Segmentation in Binocular Stereo.
Graph Laplacian Regularization for Large-Scale Semidefinite Programming.
Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension.
Attentional Processing on a Spike-Based VLSI Neural Network.
High-Dimensional Graphical Model Selection Using ℓ1-Regularized Logistic Regression.
Temporal Coding using the Response Properties of Spiking Neurons.
Fast Computation of Graph Kernels.
Comparative Gene Prediction using Conditional Random Fields.
Online Clustering of Moving Hyperplanes.
A Complexity-Distortion Approach to Joint Pattern Alignment.
Generalized Maximum Margin Clustering and Unsupervised Kernel Learning.
Scalable Discriminative Learning for Natural Language Parsing and Translation.
Large-Scale Sparsified Manifold Regularization.
Learning Motion Style Synthesis from Perceptual Observations.
Large Margin Component Analysis.
Logistic Regression for Single Trial EEG Classification.
Linearly-solvable Markov decision problems.
Towards a general independent subspace analysis.
A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation.
Modeling Human Motion Using Binary Latent Variables.
Mixture Regression for Covariate Shift.
Learning Structural Equation Models for fMRI.
Amos J. Storkey Enrico Simonotto Heather Whalley Stephen M. Lawrie Lawrence Murray David J. McGonigle
An Oracle Inequality for Clipped Regularized Risk Minimizers.
Learning Dense 3D Correspondence.
Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space.
Mutagenetic tree Fisher kernel improves prediction of HIV drug resistance from viral genotype.
A recipe for optimizing a time-histogram.
Chained Boosting.
Recursive ICA.
Convex Repeated Games and Fenchel Duality.
Nonlinear physically-based models for decoding motor-cortical population activity.
Large Margin Hidden Markov Models for Automatic Speech Recognition.
Information Bottleneck for Non Co-Occurrence Data.
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods.
Fast Iterative Kernel PCA.
Theory and Dynamics of Perceptual Bistability.
Robotic Grasping of Novel Objects.
Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation.
Shifting, One-Inclusion Mistake Bounds and Tight Multiclass Expected Risk Bounds.
Learning annotated hierarchies from relational data.
Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees.
Natural Actor-Critic for Road Traffic Optimisation.
Large Scale Hidden Semi-Markov SVMs.
Boosting Structured Prediction for Imitation Learning.
Learning to be Bayesian without Supervision.
Efficient Learning of Sparse Representations with an Energy-Based Model.
Learning to parse images of articulated bodies.
Stability of $K$-Means Clustering.
Unsupervised Regression with Applications to Nonlinear System Identification.
Inferring Network Structure from Co-Occurrences.
Parameter Expanded Variational Bayesian Methods.
Bayesian Image Super-resolution, Continued.
Game Theoretic Algorithms for Protein-DNA binding.
Bayesian Model Scoring in Markov Random Fields.
Handling Advertisements of Unknown Quality in Search Advertising.
The Neurodynamics of Belief Propagation on Binary Markov Random Fields.
Blind source separation for over-determined delayed mixtures.
Temporal dynamics of information content carried by neurons in the primary visual cortex.
A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments.
On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts.
Fundamental Limitations of Spectral Clustering.
Non-rigid point set registration: Coherent Point Drift.
Multi-Robot Negotiation: Approximating the Set of Subgame Perfect Equilibria in General-Sum Stochastic Games.
Context Effects in Category Learning: An Investigation of Four Probabilistic Models.
Fast Discriminative Visual Codebooks using Randomized Clustering Forests.
Modeling Dyadic Data with Binary Latent Factors.
Part-based Probabilistic Point Matching using Equivalence Constraints.
Isotonic Conditional Random Fields and Local Sentiment Flow.
An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments.
Statistical Modeling of Images with Fields of Gaussian Scale Mixtures.
Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning.
Dynamic Foreground/Background Extraction from Images and Videos using Random Patches.
Attribute-efficient learning of decision lists and linear threshold functions under unconcentrated distributions.
Analysis of Contour Motions.
Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure.
Emergence of conjunctive visual features by quadratic independent component analysis.
Learnability and the doubling dimension.
Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space.
Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data.
Ordinal Regression by Extended Binary Classification.
Real-time adaptive information-theoretic optimization of neurophysiology experiments.
Speakers optimize information density through syntactic reduction.
Blind Motion Deblurring Using Image Statistics.
Uncertainty, phase and oscillatory hippocampal recall.
Aggregating Classification Accuracy across Time: Application to Single Trial EEG.
Efficient Structure Learning of Markov Networks using L1-Regularization.
A Bayesian Approach to Diffusion Models of Decision-Making and Response Time.
Efficient sparse coding algorithms.
Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields.
Modelling transcriptional regulation using Gaussian Processes.
Inducing Metric Violations in Human Similarity Judgements.
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier.
Accelerated Variational Dirichlet Process Mixtures.
Reducing Calibration Time For Brain-Computer Interfaces: A Clustering Approach.
Multiple timescales and uncertainty in motor adaptation.
Causal inference in sensorimotor integration.
Gaussian and Wishart Hyperkernels.
Predicting spike times from subthreshold dynamics of a neuron.
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons.
An Information Theoretic Framework for Eukaryotic Gradient Sensing.
Hierarchical Dirichlet Processes with Random Effects.
A Nonparametric Approach to Bottom-Up Visual Saliency.
Combining causal and similarity-based reasoning.
An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models.
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems.
Clustering appearance and shape by learning jigsaws.
A Humanlike Predictor of Facial Attractiveness.
Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models.
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm.
Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models.
In-Network PCA and Anomaly Detection.
Sparse Representation for Signal Classification.
Correcting Sample Selection Bias by Unlabeled Data.
Single Channel Speech Separation Using Factorial Dynamics.
Geometric entropy minimization (GEM) for anomaly detection and localization.
Prediction on a Graph with a Perceptron.
TrueSkillTM: A Bayesian Skill Rating System.
Manifold Denoising.
Stratification Learning: Detecting Mixed Density and Dimensionality in High Dimensional Point Clouds.
Graph-Based Visual Saliency.
Adaptive Spatial Filters with predefined Region of Interest for EEG based Brain-Computer-Interfaces.
Training Conditional Random Fields for Maximum Labelwise Accuracy.
Learning Nonparametric Models for Probabilistic Imitation.
A Kernel Method for the Two-Sample-Problem.
Approximate Correspondences in High Dimensions.
Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis.
No-regret Algorithms for Online Convex Programs.
Near-Uniform Sampling of Combinatorial Spaces Using XOR Constraints.
Approximate inference using planar graph decomposition.
Data Integration for Classification Problems Employing Gaussian Process Priors.
Bayesian Policy Gradient Algorithms.
A PAC-Bayes Risk Bound for General Loss Functions.
iLSTD: Eligibility Traces and Convergence Analysis.
Distributed Inference in Dynamical Systems.
Multiple Instance Learning for Computer Aided Diagnosis.
Image Retrieval and Classification Using Local Distance Functions.
Multi-dynamic Bayesian Networks.
Clustering Under Prior Knowledge with Application to Image Segmentation.
PG-means: learning the number of clusters in data.
A Small World Threshold for Economic Network Formation.
Optimal Single-Class Classification Strategies.
Using Combinatorial Optimization within Max-Product Belief Propagation.
Learning to Traverse Image Manifolds.
A Theory of Retinal Population Coding.
Support Vector Machines on a Budget.
Differential Entropic Clustering of Multivariate Gaussians.
Kernels on Structured Objects Through Nested Histograms.
Learning from Multiple Sources.
Balanced Graph Matching.
On Transductive Regression.
Recursive Attribute Factoring.
Relational Learning with Gaussian Processes.
Map-Reduce for Machine Learning on Multicore.
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions.
Bayesian Ensemble Learning.
Context dependent amplification of both rate and event-correlation in a VLSI network of spiking neurons.
implicit Online Learning with Kernels.
Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model.
Max-margin classification of incomplete data.
Automated Hierarchy Discovery for Planning in Partially Observable Environments.
Branch and Bound for Semi-Supervised Support Vector Machines.
Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation.
Conditional mean field.
Learning to Rank with Nonsmooth Cost Functions.
Denoising and Dimension Reduction in Feature Space.
Similarity by Composition.
Detecting Humans via Their Pose.
Dirichlet-Enhanced Spam Filtering based on Biased Samples.
Greedy Layer-Wise Training of Deep Networks.
An Approach to Bounded Rationality.
Analysis of Representations for Domain Adaptation.
Convergence of Laplacian Eigenmaps.
Temporal and Cross-Subject Probabilistic Models for fMRI Prediction Tasks.
A selective attention multi--chip system with dynamic synapses and spiking neurons.
AdaBoost is Consistent.
Sample Complexity of Policy Search with Known Dynamics.
A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems.
Unified Inference for Variational Bayesian Linear Gaussian State-Space Models.
Subordinate class recognition using relational object models.
Active learning for misspecified generalized linear models.
Efficient Methods for Privacy Preserving Face Detection.
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning.
Multi-Task Feature Learning.
Sparse Kernel Orthonormalized PLS for feature extraction in large data sets.
Learning on Graph with Laplacian Regularization.
Online Classification for Complex Problems Using Simultaneous Projections.
Tighter PAC-Bayes Bounds.
An Application of Reinforcement Learning to Aerobatic Helicopter Flight.