nips nips2004 knowledge-graph by maker-knowledge-mining
1 nips-2004-A Cost-Shaping LP for Bellman Error Minimization with Performance Guarantees
Author: Daniela D. Farias, Benjamin V. Roy
Abstract: We introduce a new algorithm based on linear programming that approximates the differential value function of an average-cost Markov decision process via a linear combination of pre-selected basis functions. The algorithm carries out a form of cost shaping and minimizes a version of Bellman error. We establish an error bound that scales gracefully with the number of states without imposing the (strong) Lyapunov condition required by its counterpart in [6]. We propose a path-following method that automates selection of important algorithm parameters which represent counterparts to the “state-relevance weights” studied in [6]. 1
2 nips-2004-A Direct Formulation for Sparse PCA Using Semidefinite Programming
Author: Alexandre D'aspremont, Laurent E. Ghaoui, Michael I. Jordan, Gert R. Lanckriet
Abstract: We examine the problem of approximating, in the Frobenius-norm sense, a positive, semidefinite symmetric matrix by a rank-one matrix, with an upper bound on the cardinality of its eigenvector. The problem arises in the decomposition of a covariance matrix into sparse factors, and has wide applications ranging from biology to finance. We use a modification of the classical variational representation of the largest eigenvalue of a symmetric matrix, where cardinality is constrained, and derive a semidefinite programming based relaxation for our problem. 1
Author: Dori Peleg, Ron Meir
Abstract: A novel linear feature selection algorithm is presented based on the global minimization of a data-dependent generalization error bound. Feature selection and scaling algorithms often lead to non-convex optimization problems, which in many previous approaches were addressed through gradient descent procedures that can only guarantee convergence to a local minimum. We propose an alternative approach, whereby the global solution of the non-convex optimization problem is derived via an equivalent optimization problem. Moreover, the convex optimization task is reduced to a conic quadratic programming problem for which efficient solvers are available. Highly competitive numerical results on both artificial and real-world data sets are reported. 1
4 nips-2004-A Generalized Bradley-Terry Model: From Group Competition to Individual Skill
Author: Tzu-kuo Huang, Chih-jen Lin, Ruby C. Weng
Abstract: The Bradley-Terry model for paired comparison has been popular in many areas. We propose a generalized version in which paired individual comparisons are extended to paired team comparisons. We introduce a simple algorithm with convergence proofs to solve the model and obtain individual skill. A useful application to multi-class probability estimates using error-correcting codes is demonstrated. 1
5 nips-2004-A Harmonic Excitation State-Space Approach to Blind Separation of Speech
Author: Rasmus K. Olsson, Lars K. Hansen
Abstract: We discuss an identification framework for noisy speech mixtures. A block-based generative model is formulated that explicitly incorporates the time-varying harmonic plus noise (H+N) model for a number of latent sources observed through noisy convolutive mixtures. All parameters including the pitches of the source signals, the amplitudes and phases of the sources, the mixing filters and the noise statistics are estimated by maximum likelihood, using an EM-algorithm. Exact averaging over the hidden sources is obtained using the Kalman smoother. We show that pitch estimation and source separation can be performed simultaneously. The pitch estimates are compared to laryngograph (EGG) measurements. Artificial and real room mixtures are used to demonstrate the viability of the approach. Intelligible speech signals are re-synthesized from the estimated H+N models.
6 nips-2004-A Hidden Markov Model for de Novo Peptide Sequencing
Author: Bernd Fischer, Volker Roth, Jonas Grossmann, Sacha Baginsky, Wilhelm Gruissem, Franz Roos, Peter Widmayer, Joachim M. Buhmann
Abstract: De novo Sequencing of peptides is a challenging task in proteome research. While there exist reliable DNA-sequencing methods, the highthroughput de novo sequencing of proteins by mass spectrometry is still an open problem. Current approaches suffer from a lack in precision to detect mass peaks in the spectrograms. In this paper we present a novel method for de novo peptide sequencing based on a hidden Markov model. Experiments effectively demonstrate that this new method significantly outperforms standard approaches in matching quality. 1
7 nips-2004-A Large Deviation Bound for the Area Under the ROC Curve
Author: Shivani Agarwal, Thore Graepel, Ralf Herbrich, Dan Roth
Abstract: The area under the ROC curve (AUC) has been advocated as an evaluation criterion for the bipartite ranking problem. We study large deviation properties of the AUC; in particular, we derive a distribution-free large deviation bound for the AUC which serves to bound the expected accuracy of a ranking function in terms of its empirical AUC on an independent test sequence. A comparison of our result with a corresponding large deviation result for the classification error rate suggests that the test sample size required to obtain an -accurate estimate of the expected accuracy of a ranking function with δ-confidence is larger than that required to obtain an -accurate estimate of the expected error rate of a classification function with the same confidence. A simple application of the union bound allows the large deviation bound to be extended to learned ranking functions chosen from finite function classes. 1
8 nips-2004-A Machine Learning Approach to Conjoint Analysis
Author: Olivier Chapelle, Za\
Abstract: Choice-based conjoint analysis builds models of consumer preferences over products with answers gathered in questionnaires. Our main goal is to bring tools from the machine learning community to solve this problem more efficiently. Thus, we propose two algorithms to quickly and accurately estimate consumer preferences. 1
9 nips-2004-A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning
Author: Saharon Rosset, Ji Zhu, Hui Zou, Trevor J. Hastie
Abstract: We consider the situation in semi-supervised learning, where the “label sampling” mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of moments for estimating this stochastic dependence using the unlabeled data. This is potentially useful for two distinct purposes: a. As an input to a supervised learning procedure which can be used to “de-bias” its results using labeled data only and b. As a potentially interesting learning task in itself. We present several examples to illustrate the practical usefulness of our method.
10 nips-2004-A Probabilistic Model for Online Document Clustering with Application to Novelty Detection
Author: Jian Zhang, Zoubin Ghahramani, Yiming Yang
Abstract: In this paper we propose a probabilistic model for online document clustering. We use non-parametric Dirichlet process prior to model the growing number of clusters, and use a prior of general English language model as the base distribution to handle the generation of novel clusters. Furthermore, cluster uncertainty is modeled with a Bayesian Dirichletmultinomial distribution. We use empirical Bayes method to estimate hyperparameters based on a historical dataset. Our probabilistic model is applied to the novelty detection task in Topic Detection and Tracking (TDT) and compared with existing approaches in the literature. 1
11 nips-2004-A Second Order Cone programming Formulation for Classifying Missing Data
Author: Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alex J. Smola
Abstract: We propose a convex optimization based strategy to deal with uncertainty in the observations of a classification problem. We assume that instead of a sample (xi , yi ) a distribution over (xi , yi ) is specified. In particular, we derive a robust formulation when the distribution is given by a normal distribution. It leads to Second Order Cone Programming formulation. Our method is applied to the problem of missing data, where it outperforms direct imputation. 1
12 nips-2004-A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities
Author: Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaadia, Yoram Singer
Abstract: We devise and experiment with a dynamical kernel-based system for tracking hand movements from neural activity. The state of the system corresponds to the hand location, velocity, and acceleration, while the system’s input are the instantaneous spike rates. The system’s state dynamics is defined as a combination of a linear mapping from the previous estimated state and a kernel-based mapping tailored for modeling neural activities. In contrast to generative models, the activity-to-state mapping is learned using discriminative methods by minimizing a noise-robust loss function. We use this approach to predict hand trajectories on the basis of neural activity in motor cortex of behaving monkeys and find that the proposed approach is more accurate than both a static approach based on support vector regression and the Kalman filter. 1
13 nips-2004-A Three Tiered Approach for Articulated Object Action Modeling and Recognition
Author: Le Lu, Gregory D. Hager, Laurent Younes
Abstract: Visual action recognition is an important problem in computer vision. In this paper, we propose a new method to probabilistically model and recognize actions of articulated objects, such as hand or body gestures, in image sequences. Our method consists of three levels of representation. At the low level, we first extract a feature vector invariant to scale and in-plane rotation by using the Fourier transform of a circular spatial histogram. Then, spectral partitioning [20] is utilized to obtain an initial clustering; this clustering is then refined using a temporal smoothness constraint. Gaussian mixture model (GMM) based clustering and density estimation in the subspace of linear discriminant analysis (LDA) are then applied to thousands of image feature vectors to obtain an intermediate level representation. Finally, at the high level we build a temporal multiresolution histogram model for each action by aggregating the clustering weights of sampled images belonging to that action. We discuss how this high level representation can be extended to achieve temporal scaling invariance and to include Bi-gram or Multi-gram transition information. Both image clustering and action recognition/segmentation results are given to show the validity of our three tiered representation.
14 nips-2004-A Topographic Support Vector Machine: Classification Using Local Label Configurations
Author: Johannes Mohr, Klaus Obermayer
Abstract: The standard approach to the classification of objects is to consider the examples as independent and identically distributed (iid). In many real world settings, however, this assumption is not valid, because a topographical relationship exists between the objects. In this contribution we consider the special case of image segmentation, where the objects are pixels and where the underlying topography is a 2D regular rectangular grid. We introduce a classification method which not only uses measured vectorial feature information but also the label configuration within a topographic neighborhood. Due to the resulting dependence between the labels of neighboring pixels, a collective classification of a set of pixels becomes necessary. We propose a new method called ’Topographic Support Vector Machine’ (TSVM), which is based on a topographic kernel and a self-consistent solution to the label assignment shown to be equivalent to a recurrent neural network. The performance of the algorithm is compared to a conventional SVM on a cell image segmentation task. 1
15 nips-2004-Active Learning for Anomaly and Rare-Category Detection
Author: Dan Pelleg, Andrew W. Moore
Abstract: We introduce a novel active-learning scenario in which a user wants to work with a learning algorithm to identify useful anomalies. These are distinguished from the traditional statistical definition of anomalies as outliers or merely ill-modeled points. Our distinction is that the usefulness of anomalies is categorized subjectively by the user. We make two additional assumptions. First, there exist extremely few useful anomalies to be hunted down within a massive dataset. Second, both useful and useless anomalies may sometimes exist within tiny classes of similar anomalies. The challenge is thus to identify “rare category” records in an unlabeled noisy set with help (in the form of class labels) from a human expert who has a small budget of datapoints that they are prepared to categorize. We propose a technique to meet this challenge, which assumes a mixture model fit to the data, but otherwise makes no assumptions on the particular form of the mixture components. This property promises wide applicability in real-life scenarios and for various statistical models. We give an overview of several alternative methods, highlighting their strengths and weaknesses, and conclude with a detailed empirical analysis. We show that our method can quickly zoom in on an anomaly set containing a few tens of points in a dataset of hundreds of thousands. 1
16 nips-2004-Adaptive Discriminative Generative Model and Its Applications
Author: Ruei-sung Lin, David A. Ross, Jongwoo Lim, Ming-Hsuan Yang
Abstract: This paper presents an adaptive discriminative generative model that generalizes the conventional Fisher Linear Discriminant algorithm and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the target from the background. We present a computationally efficient algorithm to constantly update this discriminative model as time progresses. While most tracking algorithms operate on the premise that the object appearance or ambient lighting condition does not significantly change as time progresses, our method adapts a discriminative generative model to reflect appearance variation of the target and background, thereby facilitating the tracking task in ever-changing environments. Numerous experiments show that our method is able to learn a discriminative generative model for tracking target objects undergoing large pose and lighting changes.
17 nips-2004-Adaptive Manifold Learning
Author: Jing Wang, Zhenyue Zhang, Hongyuan Zha
Abstract: Recently, there have been several advances in the machine learning and pattern recognition communities for developing manifold learning algorithms to construct nonlinear low-dimensional manifolds from sample data points embedded in high-dimensional spaces. In this paper, we develop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the neighborhood sizes; and 2) better fitting the local geometric structure to account for the variations in the curvature of the manifold and its interplay with the sampling density of the data set. We also illustrate the effectiveness of our methods on some synthetic data sets. 1
18 nips-2004-Algebraic Set Kernels with Application to Inference Over Local Image Representations
Author: Amnon Shashua, Tamir Hazan
Abstract: This paper presents a general family of algebraic positive definite similarity functions over spaces of matrices with varying column rank. The columns can represent local regions in an image (whereby images have varying number of local parts), images of an image sequence, motion trajectories in a multibody motion, and so forth. The family of set kernels we derive is based on a group invariant tensor product lifting with parameters that can be naturally tuned to provide a cook-book of sorts covering the possible ”wish lists” from similarity measures over sets of varying cardinality. We highlight the strengths of our approach by demonstrating the set kernels for visual recognition of pedestrians using local parts representations. 1
19 nips-2004-An Application of Boosting to Graph Classification
Author: Taku Kudo, Eisaku Maeda, Yuji Matsumoto
Abstract: This paper presents an application of Boosting for classifying labeled graphs, general structures for modeling a number of real-world data, such as chemical compounds, natural language texts, and bio sequences. The proposal consists of i) decision stumps that use subgraph as features, and ii) a Boosting algorithm in which subgraph-based decision stumps are used as weak learners. We also discuss the relation between our algorithm and SVMs with convolution kernels. Two experiments using natural language data and chemical compounds show that our method achieves comparable or even better performance than SVMs with convolution kernels as well as improves the testing efficiency. 1
20 nips-2004-An Auditory Paradigm for Brain-Computer Interfaces
Author: N. J. Hill, Thomas N. Lal, Karin Bierig, Niels Birbaumer, Bernhard Schölkopf
Abstract: Motivated by the particular problems involved in communicating with “locked-in” paralysed patients, we aim to develop a braincomputer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged eventrelated potentials, we show that an untrained user’s EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI. 1
21 nips-2004-An Information Maximization Model of Eye Movements
22 nips-2004-An Investigation of Practical Approximate Nearest Neighbor Algorithms
23 nips-2004-Analysis of a greedy active learning strategy
24 nips-2004-Approximately Efficient Online Mechanism Design
25 nips-2004-Assignment of Multiplicative Mixtures in Natural Images
27 nips-2004-Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation
28 nips-2004-Bayesian inference in spiking neurons
29 nips-2004-Beat Tracking the Graphical Model Way
30 nips-2004-Binet-Cauchy Kernels
31 nips-2004-Blind One-microphone Speech Separation: A Spectral Learning Approach
32 nips-2004-Boosting on Manifolds: Adaptive Regularization of Base Classifiers
33 nips-2004-Brain Inspired Reinforcement Learning
34 nips-2004-Breaking SVM Complexity with Cross-Training
37 nips-2004-Co-Training and Expansion: Towards Bridging Theory and Practice
39 nips-2004-Coarticulation in Markov Decision Processes
40 nips-2004-Common-Frame Model for Object Recognition
41 nips-2004-Comparing Beliefs, Surveys, and Random Walks
42 nips-2004-Computing regularization paths for learning multiple kernels
43 nips-2004-Conditional Models of Identity Uncertainty with Application to Noun Coreference
44 nips-2004-Conditional Random Fields for Object Recognition
45 nips-2004-Confidence Intervals for the Area Under the ROC Curve
46 nips-2004-Constraining a Bayesian Model of Human Visual Speed Perception
47 nips-2004-Contextual Models for Object Detection Using Boosted Random Fields
48 nips-2004-Convergence and No-Regret in Multiagent Learning
49 nips-2004-Density Level Detection is Classification
50 nips-2004-Dependent Gaussian Processes
51 nips-2004-Detecting Significant Multidimensional Spatial Clusters
52 nips-2004-Discrete profile alignment via constrained information bottleneck
53 nips-2004-Discriminant Saliency for Visual Recognition from Cluttered Scenes
54 nips-2004-Distributed Information Regularization on Graphs
55 nips-2004-Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation
56 nips-2004-Dynamic Bayesian Networks for Brain-Computer Interfaces
57 nips-2004-Economic Properties of Social Networks
58 nips-2004-Edge of Chaos Computation in Mixed-Mode VLSI - A Hard Liquid
59 nips-2004-Efficient Kernel Discriminant Analysis via QR Decomposition
60 nips-2004-Efficient Kernel Machines Using the Improved Fast Gauss Transform
61 nips-2004-Efficient Out-of-Sample Extension of Dominant-Set Clusters
62 nips-2004-Euclidean Embedding of Co-Occurrence Data
63 nips-2004-Expectation Consistent Free Energies for Approximate Inference
64 nips-2004-Experts in a Markov Decision Process
65 nips-2004-Exploration-Exploitation Tradeoffs for Experts Algorithms in Reactive Environments
66 nips-2004-Exponential Family Harmoniums with an Application to Information Retrieval
67 nips-2004-Exponentiated Gradient Algorithms for Large-margin Structured Classification
68 nips-2004-Face Detection --- Efficient and Rank Deficient
69 nips-2004-Fast Rates to Bayes for Kernel Machines
70 nips-2004-Following Curved Regularized Optimization Solution Paths
71 nips-2004-Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices
72 nips-2004-Generalization Error and Algorithmic Convergence of Median Boosting
73 nips-2004-Generative Affine Localisation and Tracking
74 nips-2004-Harmonising Chorales by Probabilistic Inference
75 nips-2004-Heuristics for Ordering Cue Search in Decision Making
76 nips-2004-Hierarchical Bayesian Inference in Networks of Spiking Neurons
77 nips-2004-Hierarchical Clustering of a Mixture Model
78 nips-2004-Hierarchical Distributed Representations for Statistical Language Modeling
79 nips-2004-Hierarchical Eigensolver for Transition Matrices in Spectral Methods
80 nips-2004-Identifying Protein-Protein Interaction Sites on a Genome-Wide Scale
81 nips-2004-Implicit Wiener Series for Higher-Order Image Analysis
82 nips-2004-Incremental Algorithms for Hierarchical Classification
83 nips-2004-Incremental Learning for Visual Tracking
84 nips-2004-Inference, Attention, and Decision in a Bayesian Neural Architecture
85 nips-2004-Instance-Based Relevance Feedback for Image Retrieval
86 nips-2004-Instance-Specific Bayesian Model Averaging for Classification
87 nips-2004-Integrating Topics and Syntax
88 nips-2004-Intrinsically Motivated Reinforcement Learning
89 nips-2004-Joint MRI Bias Removal Using Entropy Minimization Across Images
90 nips-2004-Joint Probabilistic Curve Clustering and Alignment
91 nips-2004-Joint Tracking of Pose, Expression, and Texture using Conditionally Gaussian Filters
92 nips-2004-Kernel Methods for Implicit Surface Modeling
93 nips-2004-Kernel Projection Machine: a New Tool for Pattern Recognition
94 nips-2004-Kernels for Multi--task Learning
95 nips-2004-Large-Scale Prediction of Disulphide Bond Connectivity
96 nips-2004-Learning, Regularization and Ill-Posed Inverse Problems
97 nips-2004-Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters
98 nips-2004-Learning Gaussian Process Kernels via Hierarchical Bayes
99 nips-2004-Learning Hyper-Features for Visual Identification
100 nips-2004-Learning Preferences for Multiclass Problems
101 nips-2004-Learning Syntactic Patterns for Automatic Hypernym Discovery
102 nips-2004-Learning first-order Markov models for control
103 nips-2004-Limits of Spectral Clustering
104 nips-2004-Linear Multilayer Independent Component Analysis for Large Natural Scenes
105 nips-2004-Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning
106 nips-2004-Machine Learning Applied to Perception: Decision Images for Gender Classification
107 nips-2004-Making Latin Manuscripts Searchable using gHMM's
108 nips-2004-Markov Networks for Detecting Overalpping Elements in Sequence Data
109 nips-2004-Mass Meta-analysis in Talairach Space
110 nips-2004-Matrix Exponential Gradient Updates for On-line Learning and Bregman Projection
111 nips-2004-Maximal Margin Labeling for Multi-Topic Text Categorization
112 nips-2004-Maximising Sensitivity in a Spiking Network
113 nips-2004-Maximum-Margin Matrix Factorization
114 nips-2004-Maximum Likelihood Estimation of Intrinsic Dimension
115 nips-2004-Maximum Margin Clustering
116 nips-2004-Message Errors in Belief Propagation
117 nips-2004-Methods Towards Invasive Human Brain Computer Interfaces
119 nips-2004-Mistake Bounds for Maximum Entropy Discrimination
120 nips-2004-Modeling Conversational Dynamics as a Mixed-Memory Markov Process
121 nips-2004-Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution
122 nips-2004-Modelling Uncertainty in the Game of Go
123 nips-2004-Multi-agent Cooperation in Diverse Population Games
124 nips-2004-Multiple Alignment of Continuous Time Series
125 nips-2004-Multiple Relational Embedding
126 nips-2004-Nearly Tight Bounds for the Continuum-Armed Bandit Problem
127 nips-2004-Neighbourhood Components Analysis
128 nips-2004-Neural Network Computation by In Vitro Transcriptional Circuits
129 nips-2004-New Criteria and a New Algorithm for Learning in Multi-Agent Systems
131 nips-2004-Non-Local Manifold Tangent Learning
133 nips-2004-Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning
134 nips-2004-Object Classification from a Single Example Utilizing Class Relevance Metrics
135 nips-2004-On-Chip Compensation of Device-Mismatch Effects in Analog VLSI Neural Networks
136 nips-2004-On Semi-Supervised Classification
137 nips-2004-On the Adaptive Properties of Decision Trees
138 nips-2004-Online Bounds for Bayesian Algorithms
140 nips-2004-Optimal Information Decoding from Neuronal Populations with Specific Stimulus Selectivity
141 nips-2004-Optimal sub-graphical models
142 nips-2004-Outlier Detection with One-class Kernel Fisher Discriminants
143 nips-2004-PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data
144 nips-2004-Parallel Support Vector Machines: The Cascade SVM
145 nips-2004-Parametric Embedding for Class Visualization
146 nips-2004-Pictorial Structures for Molecular Modeling: Interpreting Density Maps
147 nips-2004-Planning for Markov Decision Processes with Sparse Stochasticity
148 nips-2004-Probabilistic Computation in Spiking Populations
149 nips-2004-Probabilistic Inference of Alternative Splicing Events in Microarray Data
150 nips-2004-Proximity Graphs for Clustering and Manifold Learning
151 nips-2004-Rate- and Phase-coded Autoassociative Memory
152 nips-2004-Real-Time Pitch Determination of One or More Voices by Nonnegative Matrix Factorization
154 nips-2004-Resolving Perceptual Aliasing In The Presence Of Noisy Sensors
155 nips-2004-Responding to Modalities with Different Latencies
156 nips-2004-Result Analysis of the NIPS 2003 Feature Selection Challenge
158 nips-2004-Sampling Methods for Unsupervised Learning
159 nips-2004-Schema Learning: Experience-Based Construction of Predictive Action Models
160 nips-2004-Seeing through water
161 nips-2004-Self-Tuning Spectral Clustering
162 nips-2004-Semi-Markov Conditional Random Fields for Information Extraction
163 nips-2004-Semi-parametric Exponential Family PCA
164 nips-2004-Semi-supervised Learning by Entropy Minimization
165 nips-2004-Semi-supervised Learning on Directed Graphs
166 nips-2004-Semi-supervised Learning via Gaussian Processes
167 nips-2004-Semi-supervised Learning with Penalized Probabilistic Clustering
168 nips-2004-Semigroup Kernels on Finite Sets
169 nips-2004-Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes
170 nips-2004-Similarity and Discrimination in Classical Conditioning: A Latent Variable Account
171 nips-2004-Solitaire: Man Versus Machine
172 nips-2004-Sparse Coding of Natural Images Using an Overcomplete Set of Limited Capacity Units
174 nips-2004-Spike Sorting: Bayesian Clustering of Non-Stationary Data
175 nips-2004-Stable adaptive control with online learning
177 nips-2004-Supervised Graph Inference
178 nips-2004-Support Vector Classification with Input Data Uncertainty
179 nips-2004-Surface Reconstruction using Learned Shape Models
180 nips-2004-Synchronization of neural networks by mutual learning and its application to cryptography
181 nips-2004-Synergies between Intrinsic and Synaptic Plasticity in Individual Model Neurons
182 nips-2004-Synergistic Face Detection and Pose Estimation with Energy-Based Models
183 nips-2004-Temporal-Difference Networks
185 nips-2004-The Convergence of Contrastive Divergences
186 nips-2004-The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces
187 nips-2004-The Entire Regularization Path for the Support Vector Machine
188 nips-2004-The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space
189 nips-2004-The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees
190 nips-2004-The Rescorla-Wagner Algorithm and Maximum Likelihood Estimation of Causal Parameters
191 nips-2004-The Variational Ising Classifier (VIC) Algorithm for Coherently Contaminated Data
192 nips-2004-The power of feature clustering: An application to object detection
193 nips-2004-Theories of Access Consciousness
195 nips-2004-Trait Selection for Assessing Beef Meat Quality Using Non-linear SVM
196 nips-2004-Triangle Fixing Algorithms for the Metric Nearness Problem
197 nips-2004-Two-Dimensional Linear Discriminant Analysis
198 nips-2004-Unsupervised Variational Bayesian Learning of Nonlinear Models
199 nips-2004-Using Machine Learning to Break Visual Human Interaction Proofs (HIPs)
200 nips-2004-Using Random Forests in the Structured Language Model
201 nips-2004-Using the Equivalent Kernel to Understand Gaussian Process Regression
202 nips-2004-VDCBPI: an Approximate Scalable Algorithm for Large POMDPs
203 nips-2004-Validity Estimates for Loopy Belief Propagation on Binary Real-world Networks
204 nips-2004-Variational Minimax Estimation of Discrete Distributions under KL Loss
205 nips-2004-Who's In the Picture
206 nips-2004-Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms
207 nips-2004-ℓ₀-norm Minimization for Basis Selection