mtybilly/Instruct2DS huggingface dataset:
Joint Tracking of Pose, Expression, and Texture using Conditionally Gaussian Filters
Authors: J. C. Roddey, Javier R. Movellan, John R. Hershey Tim K. Marks
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We present a generative model and stochastic filtering algorithm for si-           multaneous tracking of 3D position and orientation, non-rigid motion,           object texture, and background texture using a single camera. We show           that the solution to this problem is formally equivalent to stochastic fil-           tering of conditionally Gaussian processes, a problem for which well           known approaches exist [3, 8]. We propose an approach based on Monte           Carlo sampling of the nonlinear component of the process (object mo-           tion) and exact filtering of the object and background textures given the           sampled motion. The smoothness of image sequences in time and space           is exploited by using Laplace's method to generate proposal distributions           for importance sampling [7]. The resulting inference algorithm encom-           passes both optic flow and template-based tracking as special cases, and           elucidates the conditions under which these methods are optimal. We           demonstrate an application of the system to 3D non-rigid face tracking.
Hierarchical Clustering of a Mixture Model
Authors: Sam T. Roweis Jacob Goldberger
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: In this paper we propose an efficient algorithm for reducing a large         mixture of Gaussians into a smaller mixture while still preserv-         ing the component structure of the original model; this is achieved         by clustering (grouping) the components. The method minimizes         a new, easily computed distance measure between two Gaussian         mixtures that can be motivated from a suitable stochastic model         and the iterations of the algorithm use only the model parameters,         avoiding the need for explicit resampling of datapoints. We demon-         strate the method by performing hierarchical clustering of scenery         images and handwritten digits.
Hierarchical Bayesian Inference in Networks of Spiking Neurons
Authors: Rajesh P. Rao
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: There is growing evidence from psychophysical and neurophysiological          studies that the brain utilizes Bayesian principles for inference and de-          cision making. An important open question is how Bayesian inference          for arbitrary graphical models can be implemented in networks of spik-          ing neurons. In this paper, we show that recurrent networks of noisy          integrate-and-fire neurons can perform approximate Bayesian inference          for dynamic and hierarchical graphical models. The membrane potential          dynamics of neurons is used to implement belief propagation in the log          domain. The spiking probability of a neuron is shown to approximate the          posterior probability of the preferred state encoded by the neuron, given          past inputs. We illustrate the model using two examples: (1) a motion de-          tection network in which the spiking probability of a direction-selective          neuron becomes proportional to the posterior probability of motion in          a preferred direction, and (2) a two-level hierarchical network that pro-          duces attentional effects similar to those observed in visual cortical areas          V2 and V4. The hierarchical model offers a new Bayesian interpretation          of attentional modulation in V2 and V4.
Semi-parametric Exponential Family PCA
Authors: Alon Orlitsky Sajama Sajama
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We present a semi-parametric latent variable model based technique for            density modelling, dimensionality reduction and visualization. Unlike            previous methods, we estimate the latent distribution non-parametrically            which enables us to model data generated by an underlying low dimen-            sional, multimodal distribution. In addition, we allow the components            of latent variable models to be drawn from the exponential family which            makes the method suitable for special data types, for example binary or            count data. Simulations on real valued, binary and count data show fa-            vorable comparison to other related schemes both in terms of separating            different populations and generalization to unseen samples.
Self-Tuning Spectral Clustering
Authors: Pietro Perona Lihi Zelnik-manor
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi-scale data, (iii) Cluster- ing with irregular background clutter, and, (iv) Finding automatically the number of groups. We ﬁrst propose that a ‘local’ scale should be used to compute the afﬁnity between each pair of points. This local scaling leads to better clustering especially when the data includes multiple scales and when the clusters are placed within a cluttered background. We further suggest exploiting the structure of the eigenvectors to infer automatically the number of groups. This leads to a new algorithm in which the ﬁnal randomly initialized k-means stage is eliminated.
The Entire Regularization Path for the Support Vector Machine
Authors: Robert Tibshirani, Ji Zhu, Trevor J. Hastie Saharon Rosset
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: In this paper we argue that the choice of the SVM cost parameter can be          critical. We then derive an algorithm that can fit the entire path of SVM          solutions for every value of the cost parameter, with essentially the same          computational cost as fitting one SVM model.
Neighbourhood Components Analysis
Authors: Geoffrey E. Hinton, Sam T. Roweis, Ruslan Salakhutdinov Jacob Goldberger
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: In this paper we propose a novel method for learning a Mahalanobis              distance measure to be used in the KNN classification algorithm. The              algorithm directly maximizes a stochastic variant of the leave-one-out              KNN score on the training set. It can also learn a low-dimensional lin-              ear embedding of labeled data that can be used for data visualization              and fast classification. Unlike other methods, our classification model              is non-parametric, making no assumptions about the shape of the class              distributions or the boundaries between them. The performance of the              method is demonstrated on several data sets, both for metric learning and              linear dimensionality reduction.
Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis
Authors: Laurenz Wiskott Tobias Blaschke
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the origi- nal source signal from some unknown nonlinear transformations of the sources using only the independence assumption. Integrating the ob- jectives of statistical independence and temporal slowness removes this indeterminacy leading to a new method for nonlinear blind source sepa- ration. The principle of temporal slowness is adopted from slow feature analysis, an unsupervised method to extract slowly varying features from a given observed vectorial signal. The performance of the algorithm is demonstrated on nonlinearly mixed speech data.
Joint Probabilistic Curve Clustering and Alignment
Authors: Padhraic Smyth Scott J. Gaffney
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Clustering and prediction of sets of curves is an important problem in          many areas of science and engineering. It is often the case that curves          tend to be misaligned from each other in a continuous manner, either in          space (across the measurements) or in time. We develop a probabilistic          framework that allows for joint clustering and continuous alignment of          sets of curves in curve space (as opposed to a fixed-dimensional feature-          vector space). The proposed methodology integrates new probabilistic          alignment models with model-based curve clustering algorithms. The          probabilistic approach allows for the derivation of consistent EM learn-          ing algorithms for the joint clustering-alignment problem. Experimental          results are shown for alignment of human growth data, and joint cluster-          ing and alignment of gene expression time-course data.
Economic Properties of Social Networks
Authors: Michael Kearns, Luis E. Ortiz, Robin Pemantle, Siddharth Suri Sham M. Kakade
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We examine the marriage of recent probabilistic generative models           for social networks with classical frameworks from mathematical eco-           nomics. We are particularly interested in how the statistical structure of           such networks influences global economic quantities such as price vari-           ation. Our findings are a mixture of formal analysis, simulation, and           experiments on an international trade data set from the United Nations.
Beat Tracking the Graphical Model Way
Authors: Nando D. Freitas Dustin Lang
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We present a graphical model for beat tracking in recorded music. Using          a probabilistic graphical model allows us to incorporate local information          and global smoothness constraints in a principled manner. We evaluate          our model on a set of varied and difficult examples, and achieve impres-          sive results. By using a fast dual-tree algorithm for graphical model in-          ference, our system runs in less time than the duration of the music being          processed.
Large-Scale Prediction of Disulphide Bond Connectivity
Authors: Alessandro Vullo, Pierre F. Baldi Jianlin Cheng
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: The formation of disulphide bridges among cysteines is an important fea-          ture of protein structures. Here we develop new methods for the predic-          tion of disulphide bond connectivity. We first build a large curated data          set of proteins containing disulphide bridges and then use 2-Dimensional          Recursive Neural Networks to predict bonding probabilities between cys-          teine pairs. These probabilities in turn lead to a weighted graph matching          problem that can be addressed efficiently. We show how the method con-          sistently achieves better results than previous approaches on the same          validation data. In addition, the method can easily cope with chains with          arbitrary numbers of bonded cysteines. Therefore, it overcomes one of          the major limitations of previous approaches restricting predictions to          chains containing no more than 10 oxidized cysteines. The method can          be applied both to situations where the bonded state of each cysteine is          known or unknown, in which case bonded state can be predicted with          85% precision and 90% recall. The method also yields an estimate for          the total number of disulphide bridges in each chain.
Multi-agent Cooperation in Diverse Population Games
Authors: S. W. Lim, Z. Gao K. Wong
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We consider multi-agent systems whose agents compete for resources by striving to be in the minority group. The agents adapt to the environment by reinforcement learning of the preferences of the policies they hold. Diversity of preferences of policies is introduced by adding random bi- ases to the initial cumulative payoffs of their policies. We explain and provide evidence that agent cooperation becomes increasingly important when diversity increases. Analyses of these mechanisms yield excellent agreement with simulations over nine decades of data.
Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution
Authors: Te W. Lee Hyun J. Park
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Capturing dependencies in images in an unsupervised manner is  important for many image processing applications. We propose a  new method for capturing nonlinear dependencies in images of  natural scenes. This method is an extension of the linear Independent  Component Analysis (ICA) method by building a hierarchical model  based on ICA and mixture of Laplacian distribution. The model  parameters are learned via an EM algorithm and it can accurately  capture variance correlation and other high order structures in a  simple manner. We visualize the learned variance structure and  demonstrate applications to image segmentation and denoising.
Dependent Gaussian Processes
Authors: Marcus Frean Phillip Boyle
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Gaussian processes are usually parameterised in terms of their covari-          ance functions. However, this makes it difficult to deal with multiple          outputs, because ensuring that the covariance matrix is positive definite          is problematic. An alternative formulation is to treat Gaussian processes          as white noise sources convolved with smoothing kernels, and to param-          eterise the kernel instead. Using this, we extend Gaussian processes to          handle multiple, coupled outputs.
Synchronization of neural networks by mutual learning and its application to cryptography
Authors: Rachel Mislovaty, Ido Kanter, Andreas Ruttor, Wolfgang Kinzel Einat Klein
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Two neural networks that are trained on their mutual output synchronize          to an identical time dependant weight vector. This novel phenomenon          can be used for creation of a secure cryptographic secret-key using a          public channel. Several models for this cryptographic system have been          suggested, and have been tested for their security under different sophis-          ticated attack strategies. The most promising models are networks that          involve chaos synchronization. The synchronization process of mutual          learning is described analytically using statistical physics methods.
Semigroup Kernels on Finite Sets
Authors: Jean-philippe Vert Marco Cuturi
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Complex objects can often be conveniently represented by finite sets of          simpler components, such as images by sets of patches or texts by bags          of words. We study the class of positive definite (p.d.) kernels for two          such objects that can be expressed as a function of the merger of their          respective sets of components. We prove a general integral representa-          tion of such kernels and present two particular examples. One of them          leads to a kernel for sets of points living in a space endowed itself with a          positive definite kernel. We provide experimental results on a benchmark          experiment of handwritten digits image classification which illustrate the          validity of the approach.
A Hidden Markov Model for de Novo Peptide Sequencing
Authors: Volker Roth, Jonas Grossmann, Sacha Baginsky, Wilhelm Gruissem, Franz Roos, Peter Widmayer, Joachim M. Buhmann Bernd Fischer
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: De novo Sequencing of peptides is a challenging task in proteome re-          search. While there exist reliable DNA-sequencing methods, the high-          throughput 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 signif-          icantly outperforms standard approaches in matching quality.
Instance-Based Relevance Feedback for Image Retrieval
Authors: Fabio Roli Giorgio Gia\-cin\-to
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: High  retrieval  precision  in  content-based  image  retrieval  can  be           attained  by  adopting  relevance  feedback  mechanisms.  These           mechanisms require that the user judges the quality of the results of           the  query  by  marking  all  the  retrieved  images  as  being  either           relevant or not. Then, the search engine exploits this information to           adapt  the  search  to  better  meet  user's  needs.  At  present,  the  vast           majority  of  proposed  relevance  feedback  mechanisms  are           formulated in terms of search model that has to be optimized. Such           an  optimization  involves  the  modification  of  some  search           parameters so that the nearest neighbor of the query vector contains           the  largest  number  of  relevant  images.  In  this  paper,  a  different           approach  to  relevance  feedback  is  proposed.  After  the  user           provides the first feedback, following retrievals are not based on k-          nn  search,  but  on  the  computation  of  a  relevance  score  for  each           image of the database. This score is computed as a function of two           distances, namely the distance from the nearest non-relevant image           and  the  distance  from  the  nearest  relevant  one.  Images  are  then           ranked  according  to  this  score  and  the  top  k  images  are  displayed.           Reported  results  on  three  image  data  sets  show  that  the  proposed           mechanism  outperforms  other  state-of-the-art  relevance  feedback           mechanisms.
Learning Preferences for Multiclass Problems
Authors: Alessandro Sperduti Fabio Aiolli
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Many interesting multiclass problems can be cast in the general frame-          work of label ranking defined on a given set of classes. The evaluation          for such a ranking is generally given in terms of the number of violated          order constraints between classes. In this paper, we propose the Prefer-          ence Learning Model as a unifying framework to model and solve a large          class of multiclass problems in a large margin perspective. In addition,          an original kernel-based method is proposed and evaluated on a ranking          dataset with state-of-the-art results.
Maximum Margin Clustering
Authors: James Neufeld, Bryce Larson, Dale Schuurmans Linli Xu
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We propose a new method for clustering based on finding maximum mar-          gin hyperplanes through data. By reformulating the problem in terms          of the implied equivalence relation matrix, we can pose the problem as          a convex integer program. Although this still yields a difficult com-          putational problem, the hard-clustering constraints can be relaxed to a          soft-clustering formulation which can be feasibly solved with a semidef-          inite program. Since our clustering technique only depends on the data          through the kernel matrix, we can easily achieve nonlinear clusterings in          the same manner as spectral clustering. Experimental results show that          our maximum margin clustering technique often obtains more accurate          results than conventional clustering methods. The real benefit of our ap-          proach, however, is that it leads naturally to a semi-supervised training          method for support vector machines. By maximizing the margin simul-          taneously on labeled and unlabeled training data, we achieve state of the          art performance by using a single, integrated learning principle.
On the Adaptive Properties of Decision Trees
Authors: Robert Nowak Clayton Scott
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Decision trees are surprisingly adaptive in three important respects: They          automatically (1) adapt to favorable conditions near the Bayes decision          boundary; (2) focus on data distributed on lower dimensional manifolds;          (3) reject irrelevant features. In this paper we examine a decision tree          based on dyadic splits that adapts to each of these conditions to achieve          minimax optimal rates of convergence. The proposed classifier is the          first known to achieve these optimal rates while being practical and im-          plementable.
Similarity and Discrimination in Classical Conditioning: A Latent Variable Account
Authors: Nathaniel D. Daw, David S. Touretzky Aaron C. Courville
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We propose a probabilistic, generative account of configural learning          phenomena in classical conditioning. Configural learning experiments          probe how animals discriminate and generalize between patterns of si-          multaneously presented stimuli (such as tones and lights) that are dif-          ferentially predictive of reinforcement. Previous models of these issues          have been successful more on a phenomenological than an explanatory          level: they reproduce experimental findings but, lacking formal founda-          tions, provide scant basis for understanding why animals behave as they          do. We present a theory that clarifies seemingly arbitrary aspects of pre-          vious models while also capturing a broader set of data. Key patterns          of data, e.g. concerning animals' readiness to distinguish patterns with          varying degrees of overlap, are shown to follow from statistical inference.
Surface Reconstruction using Learned Shape Models
Authors: Fredrik Kahl Jan E. Solem
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We consider the problem of geometrical surface reconstruction from one          or several images using learned shape models. While humans can effort-          lessly retrieve 3D shape information, this inverse problem has turned out          to be difficult to perform automatically. We introduce a framework based          on level set surface reconstruction and shape models for achieving this          goal. Through this merging, we obtain an efficient and robust method for          reconstructing surfaces of an object category of interest.          The shape model includes surface cues such as point, curve and silhou-          ette features. Based on ideas from Active Shape Models, we show how          both the geometry and the appearance of these features can be modelled          consistently in a multi-view context. The complete surface is obtained by          evolving a level set driven by a PDE, which tries to fit the surface to the          inferred 3D features. In addition, an a priori 3D surface model is used to          regularize the solution, in particular, where surface features are sparse.          Experiments are demonstrated on a database of real face images.
Maximum Likelihood Estimation of Intrinsic Dimension
Authors: Peter J. Bickel Elizaveta Levina
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We propose a new method for estimating intrinsic dimension of a dataset derived by applying the principle of maximum likelihood to the distances between close neighbors. We derive the estimator by a Poisson process approximation, assess its bias and variance theo- retically and by simulations, and apply it to a number of simulated and real datasets. We also show it has the best overall performance compared with two other intrinsic dimension estimators.
Distributed Information Regularization on Graphs
Authors: Tommi S. Jaakkola Adrian Corduneanu
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: We provide a principle for semi-supervised learning based on optimizing          the rate of communicating labels for unlabeled points with side informa-          tion. The side information is expressed in terms of identities of sets of          points or regions with the purpose of biasing the labels in each region          to be the same. The resulting regularization objective is convex, has a          unique solution, and the solution can be found with a pair of local prop-          agation operations on graphs induced by the regions. We analyze the          properties of the algorithm and demonstrate its performance on docu-          ment classification tasks.
The Convergence of Contrastive Divergences
Authors: Alan L. Yuille
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: This paper analyses the Contrastive Divergence algorithm for learning          statistical parameters. We relate the algorithm to the stochastic approxi-          mation literature. This enables us to specify conditions under which the          algorithm is guaranteed to converge to the optimal solution (with proba-          bility 1). This includes necessary and sufficient conditions for the solu-          tion to be unbiased.
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning
Authors: Liam Paninski
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Log-concavity is an important property in the context of optimization,          Laplace approximation, and sampling; Bayesian methods based on Gaus-          sian process priors have become quite popular recently for classification,          regression, density estimation, and point process intensity estimation.          Here we prove that the predictive densities corresponding to each of these          applications are log-concave, given any observed data. We also prove          that the likelihood is log-concave in the hyperparameters controlling the          mean function of the Gaussian prior in the density and point process in-          tensity estimation cases, and the mean, covariance, and observation noise          parameters in the classification and regression cases; this result leads to          a useful parameterization of these hyperparameters, indicating a suitably          large class of priors for which the corresponding maximum a posteriori          problem is log-concave.
Joint MRI Bias Removal Using Entropy Minimization Across Images
Authors: Parvez Ahammad Erik G. Learned-miller
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: The correction of bias in magnetic resonance images is an important          problem in medical image processing. Most previous approaches have          used a maximum likelihood method to increase the likelihood of the pix-          els in a single image by adaptively estimating a correction to the unknown          image bias field. The pixel likelihoods are defined either in terms of a          pre-existing tissue model, or non-parametrically in terms of the image's          own pixel values. In both cases, the specific location of a pixel in the im-          age is not used to calculate the likelihoods. We suggest a new approach          in which we simultaneously eliminate the bias from a set of images of          the same anatomy, but from different patients. We use the statistics from          the same location across different images, rather than within an image, to          eliminate bias fields from all of the images simultaneously. The method          builds a "multi-resolution" non-parametric tissue model conditioned on          image location while eliminating the bias fields associated with the orig-          inal image set. We present experiments on both synthetic and real MR          data sets, and present comparisons with other methods.
Chemosensory Processing in a Spiking Model of the Olfactory Bulb: Chemotopic Convergence and Center Surround Inhibition
Authors: Ricardo Gutierrez-osuna Baranidharan Raman
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: This paper presents a neuromorphic model of two olfactory signal-          processing primitives: chemotopic convergence of olfactory           receptor neurons, and center on-off surround lateral inhibition in           the olfactory bulb. A self-organizing model of receptor           convergence onto glomeruli is used to generate a spatially           organized map, an olfactory image. This map serves as input to a           lattice of spiking neurons with lateral connections. The dynamics           of this recurrent network transforms the initial olfactory image into           a spatio-temporal pattern that evolves and stabilizes into odor- and           intensity-coding attractors. The model is validated using           experimental data from an array of temperature-modulated gas           sensors.  Our results are consistent with recent neurobiological           findings on the antennal lobe of the honeybee and the locust.
Using Random Forests in the Structured Language Model
Authors: Frederick Jelinek Peng Xu
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: In this paper, we explore the use of Random Forests (RFs) in the struc- tured language model (SLM), which uses rich syntactic information in predicting the next word based on words already seen. The goal in this work is to construct RFs by randomly growing Decision Trees (DTs) us- ing syntactic information and investigate the performance of the SLM modeled by the RFs in automatic speech recognition. RFs, which were originally developed as classiﬁers, are a combination of decision tree classiﬁers. Each tree is grown based on random training data sampled independently and with the same distribution for all trees in the forest, and a random selection of possible questions at each node of the decision tree. Our approach extends the original idea of RFs to deal with the data sparseness problem encountered in language modeling. RFs have been studied in the context of n-gram language modeling and have been shown to generalize well to unseen data. We show in this paper that RFs using syntactic information can also achieve better performance in both perplexity (PPL) and word error rate (WER) in a large vocabulary speech recognition system, compared to a baseline that uses Kneser-Ney smoothing.
Exploration-Exploitation Tradeoffs for Experts Algorithms in Reactive Environments
Authors: Nimrod Megiddo Daniela D. Farias
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: A reactive environment is one that responds to the actions of an agent rather than           evolving obliviously. In reactive environments, experts algorithms must balance           exploration and exploitation of experts more carefully than in oblivious ones. In           addition, a more subtle definition of a learnable value of an expert is required. A           general exploration-exploitation experts method is presented along with a proper           definition of value. The method is shown to asymptotically perform as well as           the best available expert. Several variants are analyzed from the viewpoint of the           exploration-exploitation tradeoff, including explore-then-exploit, polynomially           vanishing exploration, constant-frequency exploration, and constant-size explo-           ration phases. Complexity and performance bounds are proven.
Unsupervised Variational Bayesian Learning of Nonlinear Models
Authors: Harri Valpola Antti Honkela
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: In this paper we present a framework for using multi-layer per- ceptron (MLP) networks in nonlinear generative models trained by variational Bayesian learning. The nonlinearity is handled by linearizing it using a Gauss–Hermite quadrature at the hidden neu- rons. This yields an accurate approximation for cases of large pos- terior variance. The method can be used to derive nonlinear coun- terparts for linear algorithms such as factor analysis, independent component/factor analysis and state-space models. This is demon- strated with a nonlinear factor analysis experiment in which even 20 sources can be estimated from a real world speech data set.
A Generalized Bradley-Terry Model: From Group Competition to Individual Skill
Authors: Chih-jen Lin, Ruby C. Weng Tzu-kuo Huang
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

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.
Rate- and Phase-coded Autoassociative Memory
Authors: Peter Dayan Máté Lengyel
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Areas of the brain involved in various forms of memory exhibit patterns           of neural activity quite unlike those in canonical computational models.           We show how to use well-founded Bayesian probabilistic autoassociative           recall to derive biologically reasonable neuronal dynamics in recurrently           coupled models, together with appropriate values for parameters such as           the membrane time constant and inhibition. We explicitly treat two cases.           One arises from a standard Hebbian learning rule, and involves activity           patterns that are coded by graded firing rates. The other arises from a           spike timing dependent learning rule, and involves patterns coded by the           phase of spike times relative to a coherent local field potential oscillation.           Our model offers a new and more complete understanding of how neural           dynamics may support autoassociation.
Efficient Kernel Machines Using the Improved Fast Gauss Transform
Authors: Ramani Duraiswami, Larry S. Davis Changjiang Yang
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: The computation and memory required for kernel machines with N train-          ing samples is at least O(N 2). Such a complexity is significant even for          moderate size problems and is prohibitive for large datasets. We present          an approximation technique based on the improved fast Gauss transform          to reduce the computation to O(N ). We also give an error bound for the          approximation, and provide experimental results on the UCI datasets.
Responding to Modalities with Different Latencies
Authors: Hiroyuki Nakahara, Kenji Doya, Okihide Hikosaka Fredrik Bissmarck
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Motor control depends on sensory feedback in multiple modalities with different latencies. In this paper we consider within the framework of re- inforcement learning how different sensory modalities can be combined and selected for real-time, optimal movement control. We propose an actor-critic architecture with multiple modules, whose output are com- bined using a softmax function. We tested our architecture in a simu- lation of a sequential reaching task. Reaching was initially guided by visual feedback with a long latency. Our learning scheme allowed the agent to utilize the somatosensory feedback with shorter latency when the hand is near the experienced trajectory. In simulations with different latencies for visual and somatosensory feedback, we found that the agent depended more on feedback with shorter latency.
Two-Dimensional Linear Discriminant Analysis
Authors: Ravi Janardan, Qi Li Jieping Ye
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Linear Discriminant Analysis (LDA) is a well-known scheme for feature extraction and dimension reduction. It has been used widely in many ap- plications involving high-dimensional data, such as face recognition and image retrieval. An intrinsic limitation of classical LDA is the so-called singularity problem, that is, it fails when all scatter matrices are singu- lar. A well-known approach to deal with the singularity problem is to apply an intermediate dimension reduction stage using Principal Com- ponent Analysis (PCA) before LDA. The algorithm, called PCA+LDA, is used widely in face recognition. However, PCA+LDA has high costs in time and space, due to the need for an eigen-decomposition involving the scatter matrices. In this paper, we propose a novel LDA algorithm, namely 2DLDA, which stands for 2-Dimensional Linear Discriminant Analysis. 2DLDA over- comes the singularity problem implicitly, while achieving efﬁciency. The key difference between 2DLDA and classical LDA lies in the model for data representation. Classical LDA works with vectorized representa- tions of data, while the 2DLDA algorithm works with data in matrix representation. To further reduce the dimension by 2DLDA, the combi- nation of 2DLDA and classical LDA, namely 2DLDA+LDA, is studied, where LDA is preceded by 2DLDA. The proposed algorithms are ap- plied on face recognition and compared with PCA+LDA. Experiments show that 2DLDA and 2DLDA+LDA achieve competitive recognition accuracy, while being much more efﬁcient.
Convergence and No-Regret in Multiagent Learning
Authors: Michael Bowling
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Learning in a multiagent system is a challenging problem due to two key          factors. First, if other agents are simultaneously learning then the envi-          ronment is no longer stationary, thus undermining convergence guaran-          tees. Second, learning is often susceptible to deception, where the other          agents may be able to exploit a learner's particular dynamics. In the          worst case, this could result in poorer performance than if the agent was          not learning at all. These challenges are identifiable in the two most com-          mon evaluation criteria for multiagent learning algorithms: convergence          and regret. Algorithms focusing on convergence or regret in isolation          are numerous. In this paper, we seek to address both criteria in a single          algorithm by introducing GIGA-WoLF, a learning algorithm for normal-          form games. We prove the algorithm guarantees at most zero average          regret, while demonstrating the algorithm converges in many situations          of self-play. We prove convergence in a limited setting and give empir-          ical results in a wider variety of situations. These results also suggest          a third new learning criterion combining convergence and regret, which          we call negative non-convergence regret (NNR).
Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters
Authors: Michael S. Lewicki Evan C. Smith
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: The representation of acoustic signals at the cochlear nerve must serve a           wide range of auditory tasks that require exquisite sensitivity in both time           and frequency. Lewicki (2002) demonstrated that many of the filtering           properties of the cochlea could be explained in terms of efficient coding           of natural sounds. This model, however, did not account for properties           such as phase-locking or how sound could be encoded in terms of action           potentials. Here, we extend this theoretical approach with algorithm for           learning efficient auditory codes using a spiking population code. Here,           we propose an algorithm for learning efficient auditory codes using a           theoretical model for coding sound in terms of spikes. In this model,           each spike encodes the precise time position and magnitude of a local-           ized, time varying kernel function. By adapting the kernel functions to           the statistics natural sounds, we show that, compared to conventional           signal representations, the spike code achieves far greater coding effi-           ciency. Furthermore, the inferred kernels show both striking similarities           to measured cochlear filters and a similar bandwidth versus frequency           dependence.
Active Learning for Anomaly and Rare-Category Detection
Authors: Andrew W. Moore Dan Pelleg
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

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 useful-          ness of anomalies is categorized subjectively by the user. We make two          additional assumptions. First, there exist extremely few useful anoma-          lies 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 cat-          egorize. 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 mod-          els. 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.
A Feature Selection Algorithm Based on the Global Minimization of a Generalization Error Bound
Authors: Ron Meir Dori Peleg
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

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 opti-          mization 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 effi-          cient solvers are available. Highly competitive numerical results on both          artificial and real-world data sets are reported.
Semi-supervised Learning with Penalized Probabilistic Clustering
Authors: Todd K. Leen Zhengdong Lu
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: While clustering is usually an unsupervised operation, there are circum-          stances in which we believe (with varying degrees of certainty) that items          A and B should be assigned to the same cluster, while items A and C          should not. We would like such pairwise relations to influence cluster          assignments of out-of-sample data in a manner consistent with the prior          knowledge expressed in the training set. Our starting point is proba-          bilistic clustering based on Gaussian mixture models (GMM) of the data          distribution. We express clustering preferences in the prior distribution          over assignments of data points to clusters. This prior penalizes cluster          assignments according to the degree with which they violate the prefer-          ences. We fit the model parameters with EM. Experiments on a variety          of data sets show that PPC can consistently improve clustering results.
A Direct Formulation for Sparse PCA Using Semidefinite Programming
Authors: Laurent E. Ghaoui, Michael I. Jordan, Gert R. Lanckriet Alexandre D'aspremont
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

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 modifica-          tion 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.
Stable adaptive control with online learning
Authors: Andrew Y. Ng H. J. Kim
Published in: Advances in Neural Information Processing Systems 17  (NIPS 2004) (Main Conference Track), 2004

Abstract: Learning algorithms have enjoyed numerous successes in robotic control          tasks. In problems with time-varying dynamics, online learning methods          have also proved to be a powerful tool for automatically tracking and/or          adapting to the changing circumstances. However, for safety-critical ap-          plications such as airplane flight, the adoption of these algorithms has          been significantly hampered by their lack of safety, such as "stability,"          guarantees. Rather than trying to show difficult, a priori, stability guar-          antees for specific learning methods, in this paper we propose a method          for "monitoring" the controllers suggested by the learning algorithm on-          line, and rejecting controllers leading to instability. We prove that even if          an arbitrary online learning method is used with our algorithm to control          a linear dynamical system, the resulting system is stable.