mtybilly/Instruct2DS huggingface dataset:
Combining ICA and Top-Down Attention for Robust Speech Recognition
Authors: Soo-Young Lee Un-Min Bae
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  present  an  algorithm  which  compensates  for  the  mismatches  between characteristics of real-world problems and assumptions of  independent  component analysis algorithm.  To  provide additional  information  to  the  ICA  network,  we  incorporate top-down  selec(cid:173) tive  attention.  An  MLP  classifier is  added to the separated signal  channel  and  the  error  of  the  classifier  is  backpropagated  to  the  ICA  network.  This backpropagation process results  in  estimation  of expected ICA  output  signal for  the top-down  attention.  Then,  the unmixing matrix is  retrained according to a  new  cost function  representing the backpropagated error as well  as independence.  It  modifies the density of recovered signals to the density appropriate  for  classification.  For noisy speech  signal recorded in real environ(cid:173) ments,  the  algorithm  improved  the  recognition  performance  and  showed robustness against parametric changes.
A Comparison of Image Processing Techniques for Visual Speech Recognition Applications
Authors: Terrence J. Sejnowski, Javier R. Movellan Michael S. Gray
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  examine  eight  different  techniques  for  developing  visual  rep(cid:173) resentations  in  machine  vision  tasks.  In  particular  we  compare  different  versions  of  principal  component  and  independent  com(cid:173) ponent  analysis  in  combination  with  stepwise  regression  methods  for  variable selection.  We  found  that local methods,  based on the  statistics of image patches, consistently outperformed global meth(cid:173) ods based on the statistics of entire images.  This result is consistent  with  previous  work  on  emotion  and facial  expression  recognition.  In  addition, the use of a stepwise regression technique for  selecting  variables and regions of interest substantially boosted performance.
Algebraic Information Geometry for Learning Machines with Singularities
Authors: Sumio Watanabe
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Algebraic  geometry is  essential  to learning theory.  In  hierarchical  learning  machines  such  as  layered  neural  networks  and  gaussian  mixtures,  the  asymptotic normality does not hold, since Fisher in(cid:173) formation matrices are singular.  In this paper , the rigorous asymp(cid:173) totic form of the stochastic complexity is  clarified  based on resolu(cid:173) tion of singularities and two  different  problems  are studied.  (1)  If  the  prior  is  positive,  then the  stochastic  complexity is  far smaller  than BIO,  resulting in the smaller generalization error than regular  statistical models, even when the true distribution is  not contained  in  the  parametric  model.  nate  free  and  equal  to  zero  at  singularities,  is  employed  then the  stochastic  complexity  has  the  same  form  as  BIO.  It  is  useful  for  model selection,  but not  for  generalization. 
(2)  If Jeffreys'  prior,  which  is  coordi(cid:173)
The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity
Authors: Thomas Hofmann David A. Cohn
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  describe  a joint probabilistic  model  for  modeling  the  contents  and  inter-connectivity  of document collections such  as  sets of web pages  or  research  paper archives.  The  model  is  based  on  a  probabilistic  factor  decomposition  and  allows  identifying  principal  topics  of the  collection  as well as  authoritative documents within those topics.  Furthermore, the  relationships between topics is mapped out in order to build a predictive  model of link content.  Among the many applications of this approach are  information retrieval and search, topic identification, query disambigua(cid:173) tion, focused web crawling, web authoring, and bibliometric analysis.
The Use of Classifiers in Sequential Inference
Authors: Dan Roth Vasin Punyakanok
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  study  the  problem  of combining  the  outcomes  of several  different  classifiers in a way that provides a coherent inference that satisfies some  constraints.  In particular, we develop two general approaches for an im(cid:173) portant subproblem - identifying phrase structure.  The first is  a Marko(cid:173) vian approach that extends standard HMMs to allow the use of a rich ob(cid:173) servation  structure  and  of general  classifiers  to  model  state-observation  dependencies.  The second is  an extension of constraint satisfaction for(cid:173) malisms.  We  develop efficient combination algorithms under both mod(cid:173) els and study them experimentally in the context of shallow parsing.
Finding the Key to a Synapse
Authors: Wolfgang Maass Thomas Natschläger
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Experimental data have shown that synapses are heterogeneous: different  synapses respond with different sequences of amplitudes of postsynaptic  responses to the same spike train.  Neither the role of synaptic dynamics  itself nor the  role  of the  heterogeneity of synaptic  dynamics  for  com(cid:173) putations in neural circuits is  well  understood.  We present in this article  methods that make it feasible to compute for a given synapse with known  synaptic parameters the spike train that is optimally fitted  to the synapse,  for example in the  sense that it produces the largest sum of postsynap(cid:173) tic responses.  To  our surprise we find  that most of these optimally fitted  spike  trains  match  common firing  patterns  of specific  types  of neurons  that are discussed in the literature.
The Unscented Particle Filter
Authors: Arnaud Doucet, Nando de Freitas, Eric A. Wan Rudolph van der Merwe
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: In this paper, we  propose a  new  particle filter  based on sequential  importance sampling.  The algorithm uses  a  bank of unscented fil(cid:173) ters to obtain the importance proposal distribution.  This proposal  has  two  very  "nice"  properties.  Firstly,  it  makes  efficient  use  of  the  latest  available  information  and,  secondly,  it  can  have  heavy  tails.  As  a  result,  we  find  that  the  algorithm  outperforms  stan(cid:173) dard  particle filtering  and  other  nonlinear  filtering  methods  very  substantially.  This  experimental finding  is  in  agreement  with the  theoretical  convergence  proof for  the  algorithm.  The  algorithm  also includes resampling and (possibly) Markov chain Monte Carlo  (MCMC)  steps.
Algorithms for Non-negative Matrix Factorization
Authors: H. Sebastian Seung Daniel D. Lee
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Non-negative matrix factorization (NMF) has previously been shown to 
be a useful decomposition for multivariate data. Two different multi- 
plicative algorithms for NMF are analyzed. They differ only slightly in 
the multiplicative factor used in the update rules. One algorithm can be 
shown to minimize the conventional least squares error while the other 
minimizes the generalized Kullback-Leibler divergence. The monotonic 
convergence of both algorithms can be proven using an auxiliary func- 
tion analogous to that used for proving convergence of the Expectation- 
Maximization algorithm. The algorithms can also be interpreted as diag- 
onally rescaled gradient descent, where the rescaling factor is optimally 
chosen to ensure convergence.
Divisive and Subtractive Mask Effects: Linking Psychophysics and Biophysics
Authors: Christof Koch Barbara Zenger
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We describe an analogy between psychophysically measured effects  in  contrast  masking,  and  the  behavior  of a  simple  integrate-and(cid:173) fire  neuron  that  receives  time-modulated  inhibition.  In  the  psy(cid:173) chophysical experiments, we tested observers ability to discriminate  contrasts of peripheral  Gabor patches  in  the  presence  of collinear  Gabor flankers.  The data reveal a complex interaction pattern that  we  account  for  by  assuming  that  flankers  provide  divisive  inhibi(cid:173) tion  to  the  target  unit  for  low  target  contrasts,  but  provide  sub(cid:173) tractive  inhibition  to  the  target  unit  for  higher  target  contrasts.  A similar switch from divisive to subtractive inhibition is  observed  in  an integrate-and-fire unit that receives  inhibition  modulated in  time such that the cell spends part of the time in  a high-inhibition  state  and  part  of  the  time  in  a  low-inhibition  state.  The  simi(cid:173) larity  between  the effects  suggests  that  one  may  cause  the  other.  The biophysical model makes testable predictions for  physiological  single-cell  recordings.
Factored Semi-Tied Covariance Matrices
Authors: Mark J. F. Gales
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: A new  form  of covariance modelling for Gaussian mixture models and  hidden Markov models is  presented.  This is  an  extension to  an  efficient  form of covariance modelling used in  speech recognition, semi-tied co(cid:173) variance matrices.  In the standard form of semi-tied covariance matrices  the covariance matrix is  decomposed into a highly shared decorrelating  transform and a component-specific diagonal covariance matrix. The use  of a factored decorrelating transform is presented in this paper. This fac(cid:173) toring effectively increases the number of possible transforms without in(cid:173) creasing the number of free parameters. Maximum likelihood estimation  schemes for all the model parameters are presented including the compo(cid:173) nent/transform assignment,  transform and component parameters.  This  new  model  form  is  evaluated on  a large vocabulary  speech  recognition  task.  It is  shown that using this  factored  form of covariance modelling  reduces the word error rate.
Noise Suppression Based on Neurophysiologically-motivated SNR Estimation for Robust Speech Recognition
Authors: Michael Kleinschmidt, Birger Kollmeier Jürgen Tchorz
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: A  novel  noise  suppression  scheme  for  speech  signals  is  proposed  which  is  based  on  a  neurophysiologically-motivated estimation  of  the  local  signal-to-noise  ratio  (SNR)  in  different  frequency  chan(cid:173) nels.  For  SNR-estimation,  the  input  signal  is  transformed  into  so-called  Amplitude Modulation Spectrograms  (AMS),  which rep(cid:173) resent both spectral and temporal characteristics of the respective  analysis  frame,  and  which  imitate  the  representation  of modula(cid:173) tion  frequencies  in  higher  stages  of the  mammalian  auditory sys(cid:173) tem.  A neural network is  used to analyse AMS  patterns generated  from  noisy  speech  and  estimates  the  local  SNR.  Noise  suppres(cid:173) sion  is  achieved  by  attenuating  frequency  channels  according  to  their SNR. The noise suppression algorithm is evaluated in speaker(cid:173) independent  digit  recognition  experiments  and  compared to noise  suppression by Spectral Subtraction.
Dendritic Compartmentalization Could Underlie Competition and Attentional Biasing of Simultaneous Visual Stimuli
Authors: Bartlett W. Mel Kevin A. Archie
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Neurons in area V4 have relatively large receptive fields  (RFs), so multi(cid:173) ple visual features  are simultaneously "seen" by these cells.  Recordings  from  single  V 4  neurons  suggest  that  simultaneously  presented  stimuli  compete  to  set  the  output  firing  rate,  and  that  attention  acts  to  isolate  individual  features  by  biasing  the  competition in  favor  of the  attended  object.  We propose that both stimulus competition and attentional bias(cid:173) ing arise from the spatial segregation of afferent synapses onto different  regions of the excitable dendritic tree of V 4 neurons. The pattern of feed(cid:173) forward, stimulus-driven inputs follows from  a Hebbian rule:  excitatory  afferents  with  similar RFs  tend  to  group  together on  the  dendritic  tree,  avoiding randomly located inhibitory inputs with similar RFs.  The same  principle  guides  the  formation  of inputs  that  mediate  attentional  mod(cid:173) ulation.  Using  both  biophysically  detailed  compartmental  models  and  simplified models of computation in single neurons, we demonstrate that  such an architecture could account for the response properties and atten(cid:173) tional modulation of V 4 neurons.  Our results  suggest an important role  for nonlinear dendritic conductances in extrastriate cortical processing.
Smart Vision Chip Fabricated Using Three Dimensional Integration Technology
Authors: M. Nakagawa, Kang Wook Lee, Tomonori Nakamura, Yuusuke Yamada, Ki Tae Park, Mitsumasa Koyanagi Hiroyuki Kurino
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: The  smart  VISIOn  chip  has  a  large  potential  for  application  in  general  purpose  high  speed  image  processing  systems.  In  order  to  fabricate  smart  vision  chips  including  photo  detector  compactly,  we  have  proposed  the  application  of  three  dimensional  LSI  technology  for  smart  vision  chips.  Three  dimensional  technology  has  great  potential  to  realize  new  neuromorphic  systems  inspired  by not only  the biological function but also  the  biological structure.  In this paper, we describe our three dimensional LSI technology for  neuromorphic circuits and the design of smart vision chips .
An Analysis of Convex Relaxations for MAP Estimation
Authors: Vladimir Kolmogorov, Philip Torr Pawan Mudigonda
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: The problem of obtaining the maximum a posteriori estimate of a general discrete random field (i.e. a random field defined using a finite and discrete set of labels) is known to be N P-hard. However, due to its central importance in many applications, several approximate algorithms have been proposed in the literature. In this paper, we present an analysis of three such algorithms based on convex relaxations: (i) L P - S: the linear programming (L P) relaxation proposed by Schlesinger [20] for a special case and independently in [4, 12, 23] for the general case; (ii) Q P - R L: the quadratic programming (Q P) relaxation by Ravikumar and Lafferty [18]; and (iii) S O C P - M S: the second order cone programming (S O C P) relaxation first proposed by Muramatsu and Suzuki [16] for two label problems and later extended in [14] for a general label set. We show that the S O C P - M S and the Q P - R L relaxations are equivalent. Furthermore, we prove that despite the flexibility in the form of the constraints/objective function offered by Q P and S O C P, the L P - S relaxation strictly dominates (i.e. provides a better approximation than) Q P - R L and S O C P - M S. We generalize these results by defining a large class of S O C P (and equivalent Q P) relaxations which is dominated by the L P - S relaxation. Based on these results we propose some novel S O C P relaxations which strictly dominate the previous approaches.
Random Features for Large-Scale Kernel Machines
Authors: Benjamin Recht Ali Rahimi
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user speciﬁed shift- invariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classiﬁcation and regression tasks linear machine learning al- gorithms applied to these features outperform state-of-the-art large-scale kernel machines.
Compressed Regression
Authors: Larry Wasserman, John D. Lafferty Shuheng Zhou
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Recent research has studied the role of sparsity in high dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models from sparse data. In this paper we study a variant of this problem where the original $n$ input variables are compressed by a random linear transformation to $m \ll n$ examples in $p$ dimensions, and establish conditions under which a sparse linear model can be successfully recovered from the compressed data. A primary motivation for this compression procedure is to anonymize the data and preserve privacy by revealing little information about the original data. We characterize the number of random projections that are required for $\ell_1$-regularized compressed regression to identify the nonzero coefficients in the true model with probability approaching one, a property called ``sparsistence.'' In addition, we show that $\ell_1$-regularized compressed regression asymptotically predicts as well as an oracle linear model, a property called ``persistence.'' Finally, we characterize the privacy properties of the compression procedure in information-theoretic terms, establishing upper bounds on the rate of information communicated between the compressed and uncompressed data that decay to zero.
Predictive Matrix-Variate t Models
Authors: Kai Yu, Yihong Gong Shenghuo Zhu
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn from a matrix-variate t distribution and suggest a matrix-variate t model (MVTM) to predict those missing elements. We show that MVTM generalizes a range of known probabilistic models, and automatically performs model selection to encourage sparse predictive models. Due to the non-conjugacy of its prior, it is difficult to make predictions by computing the mode or mean of the posterior distribution. We suggest an optimization method that sequentially minimizes a convex upper-bound of the log-likelihood, which is very efficient and scalable. The experiments on a toy data and EachMovie dataset show a good predictive accuracy of the model.
Loop Series and Bethe Variational Bounds in Attractive Graphical Models
Authors: Erik B. Sudderth, Martin J. Wainwright Alan S. Willsky
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Variational methods are frequently used to approximate or bound the partition or likelihood function of a Markov random field. Methods based on mean field theory are guaranteed to provide lower bounds, whereas certain types of convex relaxations provide upper bounds. In general, loopy belief propagation (BP) provides (often accurate) approximations, but not bounds. We prove that for a class of attractive binary models, the value specified by any fixed point of loopy BP always provides a lower bound on the true likelihood. Empirically, this bound is much better than the naive mean field bound, and requires no further work than running BP. We establish these lower bounds using a loop series expansion due to Chertkov and Chernyak, which we show can be derived as a consequence of the tree reparameterization characterization of BP fixed points.
Stable Dual Dynamic Programming
Authors: Michael Bowling, Dale Schuurmans, Daniel J. Lizotte Tao Wang
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Recently, we have introduced a novel approach to dynamic programming and re- inforcement learning that is based on maintaining explicit representations of sta- tionary distributions instead of value functions. In this paper, we investigate the convergence properties of these dual algorithms both theoretically and empirically, and show how they can be scaled up by incorporating function approximation.
FilterBoost: Regression and Classification on Large Datasets
Authors: Robert E. Schapire Joseph K. Bradley
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We study boosting in the ﬁltering setting, where the booster draws examples from an oracle instead of using a ﬁxed training set and so may train efﬁciently on very large datasets. Our algorithm, which is based on a logistic regression technique proposed by Collins, Schapire, & Singer, requires fewer assumptions to achieve bounds equivalent to or better than previous work. Moreover, we give the ﬁrst proof that the algorithm of Collins et al. is a strong PAC learner, albeit within the ﬁltering setting. Our proofs demonstrate the algorithm’s strong theoretical proper- ties for both classiﬁcation and conditional probability estimation, and we validate these results through extensive experiments. Empirically, our algorithm proves more robust to noise and overﬁtting than batch boosters in conditional probability estimation and proves competitive in classiﬁcation.
Unsupervised Feature Selection for Accurate Recommendation of High-Dimensional Image Data
Authors: Djemel Ziou, Nizar Bouguila Sabri Boutemedjet
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Content-based image suggestion (CBIS) targets the recommendation of products based on user preferences on the visual content of images. In this paper, we mo- tivate both feature selection and model order identiﬁcation as two key issues for a successful CBIS. We propose a generative model in which the visual features and users are clustered into separate classes. We identify the number of both user and image classes with the simultaneous selection of relevant visual features us- ing the message length approach. The goal is to ensure an accurate prediction of ratings for multidimensional non-Gaussian and continuous image descriptors. Experiments on a collected data have demonstrated the merits of our approach.
Efficient Principled Learning of Thin Junction Trees
Authors: Carlos Guestrin Anton Chechetka
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We present the first truly polynomial algorithm for learning the structure of bounded-treewidth junction trees -- an attractive subclass of probabilistic graphical models that permits both the compact representation of probability distributions and efficient exact inference. For a constant treewidth, our algorithm has polynomial time and sample complexity, and provides strong theoretical guarantees in terms of $KL$ divergence from the true distribution. We also present a lazy extension of our approach that leads to very significant speed ups in practice, and demonstrate the viability of our method empirically, on several real world datasets. One of our key new theoretical insights is a method for bounding the conditional mutual information of arbitrarily large sets of random variables with only a polynomial number of mutual information computations on fixed-size subsets of variables, when the underlying distribution can be approximated by a bounded treewidth junction tree.
Regret Minimization in Games with Incomplete Information
Authors: Michael Johanson, Michael Bowling, Carmelo Piccione Martin Zinkevich
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Extensive games are a powerful model of multiagent decision-making scenarios with incomplete information. Finding a Nash equilibrium for very large instances of these games has received a great deal of recent attention. In this paper, we describe a new technique for solving large games based on regret minimization. In particular, we introduce the notion of counterfactual regret, which exploits the degree of incomplete information in an extensive game. We show how minimizing counterfactual regret minimizes overall regret, and therefore in self-play can be used to compute a Nash equilibrium. We demonstrate this technique in the domain of poker, showing we can solve abstractions of limit Texas Hold’em with as many as 1012 states, two orders of magnitude larger than previous methods.
A Bayesian Model of Conditioned Perception
Authors: Eero P. Simoncelli Alan Stocker
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We propose an extended probabilistic model for human perception. We argue that in many circumstances, human observers simultaneously evaluate sensory evidence under different hypotheses regarding the underlying physical process that might have generated the sensory information. Within this context, inference can be optimal if the observer weighs each hypothesis according to the correct belief in that hypothesis. But if the observer commits to a particular hypothesis, the belief in that hypothesis is converted into subjective certainty, and subsequent perceptual behavior is suboptimal, conditioned only on the chosen hypothesis. We demonstrate that this framework can explain psychophysical data of a recently reported decision-estimation experiment. The model well accounts for the data, predicting the same estimation bias as a consequence of the preceding decision step. The power of the framework is that it has no free parameters except the degree of the observer's uncertainty about its internal sensory representation. All other parameters are defined by the particular experiment which allows us to make quantitative predictions of human perception to two modifications of the original experiment.
Scan Strategies for Meteorological Radars
Authors: Jim Kurose Victoria Manfredi
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We address the problem of adaptive sensor control in dynamic resource-constrained sensor networks. We focus on a meteorological sensing network comprising radars that can perform sector scanning rather than always scanning 360 degrees. We compare three sector scanning strategies. The sit-and-spin strategy always scans 360 degrees. The limited lookahead strategy additionally uses the expected environmental state K decision epochs in the future, as predicted from Kalman filters, in its decision-making. The full lookahead strategy uses all expected future states by casting the problem as a Markov decision process and using reinforcement learning to estimate the optimal scan strategy. We show that the main benefits of using a lookahead strategy are when there are multiple meteorological phenomena in the environment, and when the maximum radius of any phenomenon is sufficiently smaller than the radius of the radars. We also show that there is a trade-off between the average quality with which a phenomenon is scanned and the number of decision epochs before which a phenomenon is rescanned.
The Tradeoffs of Large Scale Learning
Authors: Olivier Bousquet Léon Bottou
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for the case of small-scale and large-scale learning problems. Small-scale learning problems are subject to the usual approximation--estimation tradeoff. Large-scale learning problems are subject to a qualitatively different tradeoff involving the computational complexity of the underlying optimization algorithms in non-trivial ways.
Inferring Elapsed Time from Stochastic Neural Processes
Authors: Maneesh Sahani Misha Ahrens
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Many perceptual processes and neural computations, such as speech recognition, motor control and learning, depend on the ability to measure and mark the passage of time. However, the processes that make such temporal judgements possible are unknown. A number of different hypothetical mechanisms have been advanced, all of which depend on the known, temporally predictable evolution of a neural or psychological state, possibly through oscillations or the gradual decay of a memory trace. Alternatively, judgements of elapsed time might be based on observations of temporally structured, but stochastic processes. Such processes need not be specific to the sense of time; typical neural and sensory processes contain at least some statistical structure across a range of time scales. Here, we investigate the statistical properties of an estimator of elapsed time which is based on a simple family of stochastic process.
A learning framework for nearest neighbor search
Authors: Sanjoy Dasgupta Lawrence Cayton
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Can we leverage learning techniques to build a fast nearest-neighbor (NN) retrieval data structure? We present a general learning framework for the NN problem in which sample queries are used to learn the parameters of a data structure that minimize the retrieval time and/or the miss rate. We explore the potential of this novel framework through two popular NN data structures: KD-trees and the rectilinear structures employed by locality sensitive hashing. We derive a generalization theory for these data structure classes and present simple learning algorithms for both. Experimental results reveal that learning often improves on the already strong performance of these data structures.
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods
Authors: Marcello Restelli, Andrea Bonarini Alessandro Lazaric
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Learning in real-world domains often requires to deal with continuous state and action spaces. Although many solutions have been proposed to apply Reinforce- ment Learning algorithms to continuous state problems, the same techniques can be hardly extended to continuous action spaces, where, besides the computation of a good approximation of the value function, a fast method for the identiﬁcation of the highest-valued action is needed. In this paper, we propose a novel actor-critic approach in which the policy of the actor is estimated through sequential Monte Carlo methods. The importance sampling step is performed on the basis of the values learned by the critic, while the resampling step modiﬁes the actor’s policy. The proposed approach has been empirically compared to other learning algo- rithms into several domains; in this paper, we report results obtained in a control problem consisting of steering a boat across a river.
Ensemble Clustering using Semidefinite Programming
Authors: Lopamudra Mukherjee, Jiming Peng, Jinhui Xu Vikas Singh
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We consider the ensemble clustering problem where the task is to ‘aggregate’ multiple clustering solutions into a single consolidated clustering that maximizes the shared information among given clustering solutions. We obtain several new results for this problem. First, we note that the notion of agreement under such circumstances can be better captured using an agreement measure based on a 2D string encoding rather than voting strategy based methods proposed in literature. Using this generalization, we ﬁrst derive a nonlinear optimization model to max- imize the new agreement measure. We then show that our optimization problem can be transformed into a strict 0-1 Semideﬁnite Program (SDP) via novel con- vexiﬁcation techniques which can subsequently be relaxed to a polynomial time solvable SDP. Our experiments indicate improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. We discuss evaluations on clustering and image segmentation databases.
Theoretical Analysis of Heuristic Search Methods for Online POMDPs
Authors: Joelle Pineau, Brahim Chaib-draa Stephane Ross
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Planning in partially observable environments remains a challenging problem, despite significant recent advances in offline approximation techniques. A few online methods have also been proposed recently, and proven to be remarkably scalable, but without the theoretical guarantees of their offline counterparts. Thus it seems natural to try to unify offline and online techniques, preserving the theoretical properties of the former, and exploiting the scalability of the latter. In this paper, we provide theoretical guarantees on an anytime algorithm for POMDPs which aims to reduce the error made by approximate offline value iteration algorithms through the use of an efficient online searching procedure. The algorithm uses search heuristics based on an error analysis of lookahead search, to guide the online search towards reachable beliefs with the most potential to reduce error. We provide a general theorem showing that these search heuristics are admissible, and lead to complete and epsilon-optimal algorithms. This is, to the best of our knowledge, the strongest theoretical result available for online POMDP solution methods. We also provide empirical evidence showing that our approach is also practical, and can find (provably) near-optimal solutions in reasonable time.
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems
Authors: Geoffrey J. Gordon, Sajid M. Siddiqi Byron Boots
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learning stable linear dynamical systems: we formulate an approxima- tion of the problem as a convex program, start with a solution to a relaxed version of the program, and incrementally add constraints to improve stability. Rather than continuing to generate constraints until we reach a feasible solution, we test stability at each step; because the convex program is only an approximation of the desired problem, this early stopping rule can yield a higher-quality solution. We apply our algorithm to the task of learning dynamic textures from image sequences as well as to modeling biosurveillance drug-sales data. The constraint generation approach leads to noticeable improvement in the quality of simulated sequences. We compare our method to those of Lacy and Bernstein [1, 2], with positive results in terms of accuracy, quality of simulated sequences, and efﬁciency.
An online Hebbian learning rule that performs Independent Component Analysis
Authors: André Longtin, Wulfram Gerstner Claudia Clopath
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Independent component analysis (ICA) is a powerful method to decouple signals. Most of the algorithms performing ICA do not consider the temporal correlations of the signal, but only higher moments of its amplitude distribution. Moreover, they require some preprocessing of the data (whitening) so as to remove second order correlations. In this paper, we are interested in understanding the neural mechanism responsible for solving ICA. We present an online learning rule that exploits delayed correlations in the input. This rule performs ICA by detecting joint variations in the firing rates of pre- and postsynaptic neurons, similar to a local rate-based Hebbian learning rule.
Modeling Natural Sounds with Modulation Cascade Processes
Authors: Maneesh Sahani Richard Turner
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Natural sounds are structured on many time-scales. A typical segment of speech, for example, contains features that span four orders of magnitude: Sentences (~1s); phonemes (~0.1s); glottal pulses (~0.01s); and formants (<0.001s). The auditory system uses information from each of these time-scales to solve complicated tasks such as auditory scene analysis. One route toward understanding how auditory processing accomplishes this analysis is to build neuroscience-inspired algorithms which solve similar tasks and to compare the properties of these algorithms with properties of auditory processing. There is however a discord: Current machine-audition algorithms largely concentrate on the shorter time-scale structures in sounds, and the longer structures are ignored. The reason for this is two-fold. Firstly, it is a difficult technical problem to construct an algorithm that utilises both sorts of information. Secondly, it is computationally demanding to simultaneously process data both at high resolution (to extract short temporal information) and for long duration (to extract long temporal information). The contribution of this work is to develop a new statistical model for natural sounds that captures structure across a wide range of time-scales, and to provide efficient learning and inference algorithms. We demonstrate the success of this approach on a missing data task.
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition
Authors: Tanzeem Choudhury Maryam Mahdaviani
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs). In real-world applications such as activity recognition, unlabeled sensor traces are relatively easy to obtain whereas labeled examples are expensive and tedious to collect. Furthermore, the ability to automatically select a small subset of discriminatory features from a large pool can be advantageous in terms of computational speed as well as accuracy. In this paper, we introduce the semi-supervised virtual evidence boosting (sVEB) algorithm for training CRFs -- a semi-supervised extension to the recently developed virtual evidence boosting (VEB) method for feature selection and parameter learning. Semi-supervised VEB takes advantage of the unlabeled data via minimum entropy regularization -- the objective function combines the unlabeled conditional entropy with labeled conditional pseudo-likelihood. The sVEB algorithm reduces the overall system cost as well as the human labeling cost required during training, which are both important considerations in building real world inference systems. In a set of experiments on synthetic data and real activity traces collected from wearable sensors, we illustrate that our algorithm benefits from both the use of unlabeled data and automatic feature selection, and outperforms other semi-supervised training approaches.
How SVMs can estimate quantiles and the median
Authors: Ingo Steinwart Andreas Christmann
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We investigate quantile regression based on the pinball loss and the ǫ-insensitive loss. For the pinball loss a condition on the data-generating distribution P is given that ensures that the conditional quantiles are approximated with respect to k · k1. This result is then used to derive an oracle inequality for an SVM based on the pinball loss. Moreover, we show that SVMs based on the ǫ-insensitive loss estimate the conditional median only under certain conditions on P .
Random Projections for Manifold Learning
Authors: Michael Wakin, Richard Baraniuk Chinmay Hegde
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We propose a novel method for {\em linear} dimensionality reduction of manifold modeled data. First, we show that with a small number $M$ of {\em random projections} of sample points in $\reals^N$ belonging to an unknown $K$-dimensional Euclidean manifold, the intrinsic dimension (ID) of the sample set can be estimated to high accuracy. Second, we rigorously prove that using only this set of random projections, we can estimate the structure of the underlying manifold. In both cases, the number random projections required is linear in $K$ and logarithmic in $N$, meaning that $K
Hippocampal Contributions to Control: The Third Way
Authors: Peter Dayan Máté Lengyel
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Recent experimental studies have focused on the specialization of different neural structures for different types of instrumental behavior. Recent theoretical work has provided normative accounts for why there should be more than one control system, and how the output of different controllers can be integrated. Two par- ticlar controllers have been identiﬁed, one associated with a forward model and the prefrontal cortex and a second associated with computationally simpler, habit- ual, actor-critic methods and part of the striatum. We argue here for the normative appropriateness of an additional, but so far marginalized control system, associ- ated with episodic memory, and involving the hippocampus and medial temporal cortices. We analyze in depth a class of simple environments to show that episodic control should be useful in a range of cases characterized by complexity and in- ferential noise, and most particularly at the very early stages of learning, long before habitization has set in. We interpret data on the transfer of control from the hippocampus to the striatum in the light of this hypothesis.
Rapid Inference on a Novel AND/OR graph for Object Detection, Segmentation and Parsing
Authors: Long Zhu, Chenxi Lin, Hongjiang Zhang, Alan L. Yuille Yuanhao Chen
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: In this paper we formulate a novel AND/OR graph representation capable of describing the different configurations of deformable articulated objects such as horses. The representation makes use of the summarization principle so that lower level nodes in the graph only pass on summary statistics to the higher level nodes. The probability distributions are invariant to position, orientation, and scale. We develop a novel inference algorithm that combined a bottom-up process for proposing configurations for horses together with a top-down process for refining and validating these proposals. The strategy of surround suppression is applied to ensure that the inference time is polynomial in the size of input data. The algorithm was applied to the tasks of detecting, segmenting and parsing horses. We demonstrate that the algorithm is fast and comparable with the state of the art approaches.
Convex Learning with Invariances
Authors: Amir Globerson, Sam T. Roweis, Alex J. Smola Choon H. Teo
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: Incorporating invariances into a learning algorithm is a common problem in ma- chine learning. We provide a convex formulation which can deal with arbitrary loss functions and arbitrary losses. In addition, it is a drop-in replacement for most optimization algorithms for kernels, including solvers of the SVMStruct family. The advantage of our setting is that it relies on column generation instead of mod- ifying the underlying optimization problem directly.
The Noisy-Logical Distribution and its Application to Causal Inference
Authors: Hongjing Lu Alan L. Yuille
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We describe a novel noisy-logical distribution for representing the distribution of a binary output variable conditioned on multiple binary input variables. The distribution is represented in terms of noisy-or's and noisy-and-not's of causal features which are conjunctions of the binary inputs. The standard noisy-or and noisy-and-not models, used in causal reasoning and artificial intelligence, are special cases of the noisy-logical distribution. We prove that the noisy-logical distribution is complete in the sense that it can represent all conditional distributions provided a sufficient number of causal factors are used. We illustrate the noisy-logical distribution by showing that it can account for new experimental findings on how humans perform causal reasoning in more complex contexts. Finally, we speculate on the use of the noisy-logical distribution for causal reasoning and artificial intelligence.
DIFFRAC: a discriminative and flexible framework for clustering
Authors: Zaïd Harchaoui Francis R. Bach
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We present a novel linear clustering framework (Diffrac) which relies on a linear discriminative cost function and a convex relaxation of a combinatorial optimization problem. The large convex optimization problem is solved through a sequence of lower dimensional singular value decompositions. This framework has several attractive properties: (1) although apparently similar to K-means, it exhibits superior clustering performance than K-means, in particular in terms of robustness to noise. (2) It can be readily extended to non linear clustering if the discriminative cost function is based on positive definite kernels, and can then be seen as an alternative to spectral clustering. (3) Prior information on the partition is easily incorporated, leading to state-of-the-art performance for semi-supervised learning, for clustering or classification. We present empirical evaluations of our algorithms on synthetic and real medium-scale datasets.
Adaptive Online Gradient Descent
Authors: Elad Hazan, Alexander Rakhlin Peter L. Bartlett
Published in: Advances in Neural Information Processing Systems 20  (NIPS 2007) (Main Conference Track), 2007

Abstract: We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates T and log T . Furthermore, we show strong optimality of the algorithm. between Finally, we provide an extension of our results to general norms.