NIPS 2008 - PDF Preprint Papers

NIPS 2008

Teaser Browsing | Click Here

Title Browsing | Click Here

Wall of Fame | Most Viewed NIPS-2008 Paper

click to vote

NIPS
2008

305views Information Technology» more NIPS 2008»

Supervised Dictionary Learning

14 years 1 months ago

Download www.di.ens.fr

It is now well established that sparse signal models are well suited for restoration tasks and can be effectively learned from audio, image, and video data. Recent research has be...

Julien Mairal, Francis Bach, Jean Ponce, Guillermo...

claim paper

Read More »

Disclaimer and Copyright Notice

Sciweavers respects the rights of all copyright holders and in this regard, authors are only allowed to share a link to their preprint paper on their own website. Every contribution is associated with a desciptive image. It is the sole responsibility of the authors to ensure that their posted image is not copyright infringing. This service is compliant with IEEE copyright.

Related Conferences

Id	Views	Title	Status

1	305	Supervised Dictionary Learning	claim
2	285	Natural Image Denoising with Convolutional Networks	claim
3	280	On Bootstrapping the ROC Curve	claim
4	271	Goal-directed decision making in prefrontal cortex: a computational framework	claim
5	261	Learning a discriminative hidden part model for human action recognition	claim
6	252	An Homotopy Algorithm for the Lasso with Online Observations	claim
7	232	Sparse Signal Recovery Using Markov Random Fields	claim
8	230	Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform	claim
9	228	MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features	claim
10	218	Bayesian Exponential Family PCA	claim
11	215	Unlabeled data: Now it helps, now it doesn't	claim
12	215	Learning Hybrid Models for Image Annotation with Partially Labeled Data	claim
13	212	Multiscale Random Fields with Application to Contour Grouping	claim
14	211	Multi-Level Active Prediction of Useful Image Annotations for Recognition	claim
15	210	Non-stationary dynamic Bayesian networks	claim
16	210	Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes	claim
17	208	Nonrigid Structure from Motion in Trajectory Space	claim
18	207	DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification	claim
19	205	Dimensionality Reduction for Data in Multiple Feature Representations	claim
20	203	Automatic online tuning for fast Gaussian summation	claim
21	201	Using Bayesian Dynamical Systems for Motion Template Libraries	claim
22	199	Overlaying classifiers: a practical approach for optimal ranking	claim
23	195	Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images	claim
24	194	Supervised Exponential Family Principal Component Analysis via Convex Optimization	claim
25	194	Recursive Segmentation and Recognition Templates for 2D Parsing	claim
26	193	Breaking Audio CAPTCHAs	claim
27	190	Generative and Discriminative Learning with Unknown Labeling Bias	claim
28	189	A "Shape Aware" Model for semi-supervised Learning of Objects and its Context	claim
29	188	Bayesian Kernel Shaping for Learning Control	claim
30	188	Kernel Measures of Independence for non-iid Data	claim
31	185	Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data	claim
32	184	The Mondrian Process	claim
33	183	ICA based on a Smooth Estimation of the Differential Entropy	claim
34	183	Hebbian Learning of Bayes Optimal Decisions	claim
35	180	Adaptive Template Matching with Shift-Invariant Semi-NMF	claim
36	179	Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks	claim
37	175	Local Gaussian Process Regression for Real Time Online Model Learning	claim
38	174	A mixture model for the evolution of gene expression in non-homogeneous datasets	claim
39	173	On the asymptotic equivalence between differential Hebbian and temporal difference learning using a local third factor	claim
40	173	Adapting to a Market Shock: Optimal Sequential Market-Making	claim
41	173	Kernelized Sorting	claim
42	171	Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes	claim
43	171	Bayesian Experimental Design of Magnetic Resonance Imaging Sequences	claim
44	171	MDPs with Non-Deterministic Policies	claim
45	170	Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance	claim
46	170	Cascaded Classification Models: Combining Models for Holistic Scene Understanding	claim
47	167	Characteristic Kernels on Groups and Semigroups	claim
48	167	Spectral Hashing	claim
49	166	Asynchronous Distributed Learning of Topic Models	claim
50	166	An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis	claim
51	165	Regularized Policy Iteration	claim
52	165	Clustering via LP-based Stabilities	claim
53	164	Estimating Robust Query Models with Convex Optimization	claim
54	163	Spectral Clustering with Perturbed Data	claim
55	163	Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree	claim
56	163	Learning Bounded Treewidth Bayesian Networks	claim
57	162	Nonlinear causal discovery with additive noise models	claim
58	162	Analyzing human feature learning as nonparametric Bayesian inference	claim
59	162	Nonparametric Bayesian Learning of Switching Linear Dynamical Systems	claim
60	161	Deep Learning with Kernel Regularization for Visual Recognition	claim
61	161	Estimating vector fields using sparse basis field expansions	claim
62	161	Partially Observed Maximum Entropy Discrimination Markov Networks	claim
63	161	Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation	claim
64	161	Stochastic Relational Models for Large-scale Dyadic Data using MCMC	claim
65	160	Interpreting the neural code with Formal Concept Analysis	claim
66	159	Clustered Multi-Task Learning: A Convex Formulation	claim
67	159	Policy Search for Motor Primitives in Robotics	claim
68	159	Structured ranking learning using cumulative distribution networks	claim
69	158	Mind the Duality Gap: Logarithmic regret algorithms for online optimization	claim
70	157	Fast Computation of Posterior Mode in Multi-Level Hierarchical Models	claim
71	157	The Infinite Factorial Hidden Markov Model	claim
72	156	Online Metric Learning and Fast Similarity Search	claim
73	154	Logistic Normal Priors for Unsupervised Probabilistic Grammar Induction	claim
74	154	Characterizing neural dependencies with copula models	claim
75	151	An ideal observer model of infant object perception	claim
76	150	Fast Rates for Regularized Objectives	claim
77	150	Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds	claim
78	150	Human Active Learning	claim
79	149	Optimization on a Budget: A Reinforcement Learning Approach	claim
80	149	Exact Convex Confidence-Weighted Learning	claim
81	149	Theory of matching pursuit	claim
82	149	Scalable Algorithms for String Kernels with Inexact Matching	claim
83	148	Regularized Learning with Networks of Features	claim
84	148	Syntactic Topic Models	claim
85	148	The Infinite Hierarchical Factor Regression Model	claim
86	148	On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing	claim
87	148	Grouping Contours Via a Related Image	claim
88	148	Global Ranking Using Continuous Conditional Random Fields	claim
89	148	Improving on Expectation Propagation	claim
90	148	Kernel Change-point Analysis	claim
91	144	Non-parametric Regression Between Manifolds	claim
92	144	Predicting the Geometry of Metal Binding Sites from Protein Sequence	claim
93	143	Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization	claim
94	143	An Online Algorithm for Maximizing Submodular Functions	claim
95	143	Integrating Locally Learned Causal Structures with Overlapping Variables	claim
96	143	One sketch for all: Theory and Application of Conditional Random Sampling	claim
97	142	Bayesian Synchronous Grammar Induction	claim
98	142	Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM	claim
99	142	Mixed Membership Stochastic Blockmodels	claim
100	141	Sparse probabilistic projections	claim
101	141	A Scalable Hierarchical Distributed Language Model	claim
102	140	Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation	claim
103	140	Diffeomorphic Dimensionality Reduction	claim
104	140	Hierarchical Fisher Kernels for Longitudinal Data	claim
105	140	Skill Characterization Based on Betweenness	claim
106	140	From Online to Batch Learning with Cutoff-Averaging	claim
107	140	Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG	claim
108	139	A spatially varying two-sample recombinant coalescent, with applications to HIV escape response	claim
109	138	Learning Taxonomies by Dependence Maximization	claim
110	138	Modeling the effects of memory on human online sentence processing with particle filters	claim
111	138	Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning	claim
112	138	An Extended Level Method for Efficient Multiple Kernel Learning	claim
113	138	Posterior Consistency of the Silverman g-prior in Bayesian Model Choice	claim
114	137	Optimal Response Initiation: Why Recent Experience Matters	claim
115	136	Clusters and Coarse Partitions in LP Relaxations	claim
116	136	A rational model of preference learning and choice prediction by children	claim
117	135	High-dimensional support union recovery in multivariate regression	claim
118	135	An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering	claim
119	135	The Recurrent Temporal Restricted Boltzmann Machine	claim
120	134	QUIC-SVD: Fast SVD Using Cosine Trees	claim
121	133	Sparse Online Learning via Truncated Gradient	claim
122	133	Robust Kernel Principal Component Analysis	claim
123	133	Fast Prediction on a Tree	claim
124	133	Online Prediction on Large Diameter Graphs	claim
125	133	Privacy-preserving logistic regression	claim
126	132	Learning with Consistency between Inductive Functions and Kernels	claim
127	132	Multi-label Multiple Kernel Learning	claim
128	132	Bayesian Model of Behaviour in Economic Games	claim
129	132	The Gaussian Process Density Sampler	claim
130	131	Unsupervised Learning of Visual Sense Models for Polysemous Words	claim
131	131	Generative versus discriminative training of RBMs for classification of fMRI images	claim
132	131	Implicit Mixtures of Restricted Boltzmann Machines	claim
133	131	Bounds on marginal probability distributions	claim
134	130	Fitted Q-iteration by Advantage Weighted Regression	claim
135	130	Dynamic visual attention: searching for coding length increments	claim
136	130	Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation	claim
137	130	Mortal Multi-Armed Bandits	claim
138	129	The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction	claim
139	129	Efficient Sampling for Gaussian Process Inference using Control Variables	claim
140	129	Support Vector Machines with a Reject Option	claim
141	129	Measures of Clustering Quality: A Working Set of Axioms for Clustering	claim
142	129	Structure Learning in Human Sequential Decision-Making	claim
143	129	Improved Moves for Truncated Convex Models	claim
144	128	Linear Classification and Selective Sampling Under Low Noise Conditions	claim
145	127	Sparse Convolved Gaussian Processes for Multi-output Regression	claim
146	127	On the Generalization Ability of Online Strongly Convex Programming Algorithms	claim
147	127	Efficient Inference in Phylogenetic InDel Trees	claim
148	127	Robust Regression and Lasso	claim
149	127	Deflation Methods for Sparse PCA	claim
150	127	Psychiatry: Insights into depression through normative decision-making models	claim
151	127	Reducing statistical dependencies in natural signals using radial Gaussianization	claim
152	126	Inferring rankings under constrained sensing	claim
153	126	On the Reliability of Clustering Stability in the Large Sample Regime	claim
154	126	Multi-task Gaussian Process Learning of Robot Inverse Dynamics	claim
155	125	Multi-resolution Exploration in Continuous Spaces	claim
156	125	An interior-point stochastic approximation method and an L1-regularized delta rule	claim
157	124	On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization	claim
158	124	Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning	claim
159	124	Domain Adaptation with Multiple Sources	claim
160	123	Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method	claim
161	122	Differentiable Sparse Coding	claim
162	121	Predictive Indexing for Fast Search	claim
163	121	Look Ma, No Hands: Analyzing the Monotonic Feature Abstraction for Text Classification	claim
164	121	Spike Feature Extraction Using Informative Samples	claim
165	119	Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text	claim
166	119	Online Optimization in X-Armed Bandits	claim
167	118	Dependent Dirichlet Process Spike Sorting	claim
168	118	Relative Margin Machines	claim
169	118	Regularized Co-Clustering with Dual Supervision	claim
170	118	Bounding Performance Loss in Approximate MDP Homomorphisms	claim
171	118	Self-organization using synaptic plasticity	claim
172	118	Transfer Learning by Distribution Matching for Targeted Advertising	claim
173	117	Continuously-adaptive discretization for message-passing algorithms	claim
174	116	Algorithms for Infinitely Many-Armed Bandits	claim
175	116	On the Efficient Minimization of Classification Calibrated Surrogates	claim
176	116	Model selection and velocity estimation using novel priors for motion patterns	claim
177	116	Particle Filter-based Policy Gradient in POMDPs	claim
178	116	Simple Local Models for Complex Dynamical Systems	claim
179	115	Localized Sliced Inverse Regression	claim
180	115	Influence of graph construction on graph-based clustering measures	claim
181	114	MAS: a multiplicative approximation scheme for probabilistic inference	claim
182	112	Tighter Bounds for Structured Estimation	claim
183	112	Unifying the Sensory and Motor Components of Sensorimotor Adaptation	claim
184	111	Shape-Based Object Localization for Descriptive Classification	claim
185	111	Load and Attentional Bayes	claim
186	111	Correlated Bigram LSA for Unsupervised Language Model Adaptation	claim
187	110	Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms	claim
188	109	Biasing Approximate Dynamic Programming with a Lower Discount Factor	claim
189	108	Evaluating probabilities under high-dimensional latent variable models	claim
190	108	Near-minimax recursive density estimation on the binary hypercube	claim
191	105	Resolution Limits of Sparse Coding in High Dimensions	claim
192	105	Convergence and Rate of Convergence of a Manifold-Based Dimension Reduction Algorithm	claim
193	101	Tracking Changing Stimuli in Continuous Attractor Neural Networks	claim
194	101	Probabilistic detection of short events, with application to critical care monitoring	claim
195	98	Near-optimal Regret Bounds for Reinforcement Learning	claim
196	96	Multi-Agent Filtering with Infinitely Nested Beliefs	claim
197	96	Empirical performance maximization for linear rank statistics	claim
198	96	Bayesian Network Score Approximation using a Metagraph Kernel	claim
199	94	Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing	claim
200	89	Rademacher Complexity Bounds for Non-I.I.D. Processes	claim

Sciweavers

Explore & Download

Productivity Tools

Document Tools

Image Tools

Sciweavers