mtybilly/Instruct2DS huggingface dataset:
Tree-Based Modeling and Estimation of Gaussian Processes on Graphs with Cycles
Authors: Erik B. Sudderth, Alan S. Willsky Martin J. Wainwright
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  present  the embedded  trees  algorithm,  an  iterative technique  for  estimation  of Gaussian  processes  defined  on  arbitrary  graphs.  By exactly solving a series of modified problems on embedded span(cid:173) ning  trees,  it  computes  the  conditional  means  with  an  efficiency  comparable to or better than other techniques.  Unlike other meth(cid:173) ods,  the  embedded trees  algorithm  also  computes  exact  error  co(cid:173) variances.  The error  covariance  computation  is  most  efficient  for  graphs in  which  removing a  small number of edges  reveals an em(cid:173) bedded  tree.  In  this  context,  we  demonstrate  that  sparse  loopy  graphs  can  provide  a  significant  increase  in  modeling  power  rela(cid:173) tive to trees,  with only a  minor increase in  estimation complexity.
Foundations for a Circuit Complexity Theory of Sensory Processing
Authors: Wolfgang Maass Robert A. Legenstein
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We introduce total wire length as salient complexity measure for an anal(cid:173) ysis of the circuit complexity of sensory processing in biological neural  systems and neuromorphic engineering. This new complexity measure is  applied to a set of basic computational problems that apparently need to  be solved by circuits for translation- and scale-invariant sensory process(cid:173) ing.  We  exhibit new  circuit design strategies  for these  new  benchmark  functions that can be implemented within realistic complexity bounds, in  particular with linear or almost linear total  wire length.
Bayes Networks on Ice: Robotic Search for Antarctic Meteorites
Authors: Dimitrios Apostolopoulos, William Whittaker Liam Pedersen
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: A  Bayes  network  based  classifier  for  distinguishing  terrestrial  rocks  from  meteorites  is  implemented  onboard  the  Nomad  robot.  Equipped with a camera,  spectrometer and eddy current sensor, this  robot searched the  ice  sheets of Antarctica and autonomously made  the first robotic identification of a meteorite, in January 2000 at the  Elephant Moraine.  This paper discusses  rock classification from  a  robotic platform, and describes the system onboard Nomad.
Learning and Tracking Cyclic Human Motion
Authors: Hedvig Sidenbladh, Michael J. Black, Trevor Hastie Dirk Ormoneit
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  present  methods  for  learning  and  tracking  human  motion  in  video.  We  estimate  a  statistical model of typical  activities from  a  large  set  of 3D  periodic  human  motion data  by  segmenting these  data automatically into  "cycles".  Then  the  mean  and  the  princi(cid:173) pal components of the cycles  are  computed using  a  new  algorithm  that  accounts  for  missing  information  and  enforces  smooth  tran(cid:173) sitions  between  cycles.  The  learned  temporal  model  provides  a  prior probability distribution over human motions that can be used  in  a  Bayesian  framework  for  tracking  human subjects  in  complex  monocular video sequences  and recovering  their  3D  motion.
Active Support Vector Machine Classification
Authors: David R. Musicant Olvi L. Mangasarian
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: An active set strategy is applied to the dual of a simple  reformula(cid:173) tion of the standard quadratic program of a  linear support vector  machine.  This  application  generates  a  fast  new  dual  algorithm  that consists of solving a  finite  number of linear equations,  with a  typically large dimensionality equal to the number of points to  be  classified.  However, by making novel use of the Sherman-Morrison(cid:173) Woodbury formula, a  much smaller matrix of the order of the orig(cid:173) inal  input  space  is  inverted at each step.  Thus,  a  problem  with a  32-dimensional input space and 7 million points required inverting  positive definite symmetric matrices of size 33 x 33 with a total run(cid:173) ning time of 96  minutes  on a  400 MHz  Pentium II.  The algorithm  requires  no  specialized  quadratic or linear  programming code,  but  merely a  linear equation solver which is  publicly  available.
Decomposition of Reinforcement Learning for Admission Control of Self-Similar Call Arrival Processes
Authors: Jakob Carlström
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: This paper presents predictive gain scheduling, a technique for simplify(cid:173) ing reinforcement learning problems by decomposition. Link admission  control  of self-similar call traffic is  used  to  demonstrate the  technique.  The control problem is  decomposed into  on-line prediction of near-fu(cid:173) ture call arrival rates, and precomputation of policies for Poisson call ar(cid:173) rival  processes.  At  decision  time,  the  predictions  are  used  to  select  among the policies. Simulations show that this technique results in  sig(cid:173) nificantly  faster learning  without any performance loss,  compared to  a  reinforcement  learning controller that does not decompose the problem.
Generalizable Singular Value Decomposition for Ill-posed Datasets
Authors: Lars Kai Hansen, Stephen C. Strother Ulrik Kjems
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We demonstrate that statistical analysis of ill-posed data sets is  subject to a bias, which can be observed when projecting indepen(cid:173) dent test set examples onto a basis defined by the training exam(cid:173) ples. Because the training examples in an ill-posed data set do not  fully span the signal space the observed training set variances in  each basis vector will be too high compared to the average vari(cid:173) ance of the test set projections onto the same basis vectors. On  basis of this understanding we introduce the Generalizable Singu(cid:173) lar Value Decomposition (GenSVD) as a means to reduce this bias  by re-estimation of the singular values obtained in a conventional  Singular Value Decomposition, allowing for a generalization perfor(cid:173) mance increase of a subsequent statistical model. We demonstrate  that the algorithm succesfully corrects bias in a data set from a  functional PET activation study of the human brain. 
1 
Ill-posed Data Sets 
An ill-posed data set has more dimensions in each example than there are examples.  Such data sets occur in many fields of research typically in connection with image  measurements. The associated statistical problem is that of extracting structure  from the observed high-dimensional vectors in the presence of noise. The statistical  analysis can be done either supervised (Le. modelling with target values: classifi(cid:173) cation, regresssion) or unsupervised (modelling with no target values: clustering,  PCA, ICA). In both types of analysis the ill-posedness may lead to immediate prob(cid:173) lems if one tries to apply conventional statistical methods of analysis, for example  the empirical covariance matrix is prohibitively large and will be rank-deficient. 
A common approach is to use Singular Value Decomposition (SVD) or the analogue  Principal Component Analysis (PCA) to reduce the dimensionality of the data. Let  the N observed i-dimensional samples Xj, j = L .N, collected in the data matrix  X = [Xl ... XN] of size I x N, I> N . The SVD-theorem states that such a matrix  can be decomposed as
Vicinal Risk Minimization
Authors: Jason Weston, Léon Bottou, Vladimir Vapnik Olivier Chapelle
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: The  Vicinal  Risk  Minimization  principle establishes  a  bridge  between  generative models  and  methods derived from  the  Structural Risk Mini(cid:173) mization Principle such  as  Support Vector Machines  or Statistical  Reg(cid:173) ularization.  We  explain how  VRM  provides  a  framework  which  inte(cid:173) grates a number of existing algorithms, such as Parzen windows, Support  Vector Machines, Ridge Regression, Constrained Logistic Classifiers and  Tangent-Prop.  We  then  show  how  the approach implies new  algorithm(cid:173) s for solving problems usually associated with generative models.  New  algorithms  are  described for  dealing with pattern recognition problems  with very different pattern distributions and dealing with unlabeled data.  Preliminary empirical results are presented.
Sparse Kernel Principal Component Analysis
Authors: Michael E. Tipping
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: 'Kernel'  principal  component  analysis  (PCA)  is  an  elegant  non(cid:173) linear  generalisation  of the  popular  linear  data  analysis  method,  where  a  kernel function  implicitly  defines  a  nonlinear transforma(cid:173) tion into a feature space wherein standard PCA is  performed.  Un(cid:173) fortunately,  the  technique  is  not  'sparse',  since  the  components  thus obtained are expressed in terms of kernels associated with ev(cid:173) ery training vector.  This paper shows  that by  approximating the  covariance matrix in  feature  space by a  reduced  number of exam(cid:173) ple vectors, using a  maximum-likelihood approach, we may obtain  a highly sparse form  of kernel PCA without loss of effectiveness.
Sparsity of Data Representation of Optimal Kernel Machine and Leave-one-out Estimator
Authors: Adam Kowalczyk
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Vapnik's result that the expectation of the generalisation error ofthe opti(cid:173) mal hyperplane is bounded by the expectation of the ratio of the number  of support vectors to  the  number of training examples is  extended to  a  broad class of kernel machines.  The class includes Support Vector Ma(cid:173) chines  for  soft margin classification and regression,  and  Regularization  Networks with a variety of kernels and cost functions.  We  show that key  inequalities in Vapnik's result become equalities once "the classification  error" is replaced by "the margin error", with the latter defined as  an in(cid:173) stance with positive cost.  In particular we show that expectations of the  true margin error and the empirical margin error are equal, and that the  sparse solutions for kernel machines are possible only if the cost function  is "partially" insensitive.
Rate-coded Restricted Boltzmann Machines for Face Recognition
Authors: Geoffrey E. Hinton Yee Whye Teh
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  describe  a  neurally-inspired,  unsupervised  learning  algorithm  that  builds  a non-linear generative model for pairs  of face  images from  the  same individual.  Individuals are then recognized by  finding  the highest  relative probability pair among all pairs that consist of a test image and  an image whose identity is known.  Our method compares favorably with  other methods in the literature. The generative model consists of a single  layer of rate-coded, non-linear feature detectors  and  it has  the property  that,  given a data vector,  the  true posterior probability distribution  over  the feature detector activities can be inferred rapidly without iteration or  approximation. The weights of the feature detectors are learned by com(cid:173) paring the correlations of pixel intensities and feature activations in two  phases: When the network is observing real data and when it is observing  reconstructions of real data generated from the feature activations.
Shape Context: A New Descriptor for Shape Matching and Object Recognition
Authors: Jitendra Malik, Jan Puzicha Serge Belongie
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  develop  an  approach  to  object  recognition  based  on  match(cid:173) ing shapes and using a resulting measure of similarity in a  nearest  neighbor  classifier.  The  key  algorithmic  problem  here  is  that  of  finding  pointwise  correspondences  between  an  image shape  and  a  stored  prototype  shape.  We  introduce  a  new  shape  descriptor,  the  shape  context,  which  makes  this  possible,  using  a  simple  and  robust algorithm.  The shape context at a point captures the distri(cid:173) bution over relative positions of other shape points and thus sum(cid:173) marizes  global  shape in  a  rich,  local  descriptor.  We  demonstrate  that  shape  contexts  greatly  simplify  recovery  of correspondences  between points of two given shapes.  Once shapes are aligned, shape  contexts are used to define a robust score for measuring shape sim(cid:173) ilarity.  We  have  used  this  score  in  a  nearest-neighbor  classifier  for  recognition of hand written  digits  as  well  as  3D  objects,  using  exactly  the  same  distance  function.  On  the  benchmark  MNIST  dataset  of handwritten  digits,  this  yields  an  error  rate  of 0.63%,  outperforming other published techniques.
Position Variance, Recurrence and Perceptual Learning
Authors: Peter Dayan Zhaoping Li
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Stimulus  arrays  are  inevitably  presented  at  different  positions  on  the  retina in  visual  tasks,  even those that nominally require fixation.  In par(cid:173) ticular, this applies to many perceptual learning tasks.  We show that per(cid:173) ceptual inference or discrimination in the face of positional variance has a  structurally different quality from inference about fixed position stimuli,  involving  a particular,  quadratic,  non-linearity rather than  a purely  lin(cid:173) ear discrimination.  We show the advantage taking this non-linearity into  account has for discrimination, and suggest it as a role for recurrent con(cid:173) nections in area VI, by demonstrating the superior discrimination perfor(cid:173) mance of a recurrent network.  We propose that learning the feedforward  and recurrent neural connections for  these tasks corresponds to  the fast  and slow components of learning observed in perceptual learning tasks.
Competition and Arbors in Ocular Dominance
Authors: Peter Dayan
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Hebbian  and  competitive Hebbian  algorithms  are  almost ubiquitous  in  modeling pattern formation in cortical development.  We  analyse in  the(cid:173) oretical  detail  a  particular  model  (adapted  from  Piepenbrock  &  Ober(cid:173) mayer, 1999) for the development of Id stripe-like patterns, which places  competitive and interactive cortical influences, and free and restricted ini(cid:173) tial arborisation onto a common footing.
Learning Switching Linear Models of Human Motion
Authors: James M. Rehg, John MacCormick Vladimir Pavlovic
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: The human  figure  exhibits  complex and  rich  dynamic  behavior that is  both nonlinear and  time-varying.  Effective models  of human dynamics  can be learned from motion capture data using switching linear dynamic  system  (SLDS)  models.  We  present results  for human  motion  synthe(cid:173) sis, classification, and visual tracking using learned SLDS models.  Since  exact inference in SLDS is intractable, we present three approximate in(cid:173) ference algorithms and compare their performance.  In particular, a new  variational  inference algorithm  is  obtained by  casting the  SLDS  model  as  a Dynamic  Bayesian  Network.  Classification experiments  show  the  superiority of SLDS over conventional HMM's for our problem domain.
Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning
Authors: Fabrizio Smeraldi, Stéphane Hug, Wulfram Gerstner Angelo Arleo
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  model  hippocampal place cells  and head-direction cells by combin(cid:173) ing allothetic  (visual) and idiothetic (proprioceptive) stimuli.  Visual in(cid:173) put, provided by a video camera on a miniature robot, is preprocessed by  a set of Gabor filters on 31 nodes of a log-polar retinotopic graph. Unsu(cid:173) pervised Hebbian learning is employed to  incrementally build a popula(cid:173) tion of localized overlapping place fields.  Place cells serve as basis func(cid:173) tions for reinforcement learning.  Experimental results for goal-oriented  navigation of a mobile robot are presented.
Kernel-Based Reinforcement Learning in Average-Cost Problems: An Application to Optimal Portfolio Choice
Authors: Peter W. Glynn Dirk Ormoneit
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Many  approaches  to  reinforcement  learning  combine  neural  net(cid:173) works  or other  parametric function  approximators with  a  form  of  temporal-difference  learning  to  estimate  the  value  function  of  a  Markov  Decision  Process.  A  significant disadvantage of those  pro(cid:173) cedures  is that the resulting learning algorithms are frequently un(cid:173) stable.  In  this  work,  we  present  a  new,  kernel-based  approach  to  reinforcement learning which overcomes this difficulty and provably  converges  to a  unique solution.  By contrast to existing algorithms,  our  method  can  also  be  shown  to  be  consistent  in  the  sense  that  its  costs  converge  to  the  optimal costs  asymptotically.  Our  focus  is  on learning in  an average-cost  framework  and on a  practical  ap(cid:173) plication to  the optimal portfolio choice problem.
A New Approximate Maximal Margin Classification Algorithm
Authors: Claudio Gentile
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: A new  incremental learning algorithm is  described which approximates  the maximal margin hyperplane w.r.t.  norm p  ~ 2 for  a set of linearly  separable data.  Our algorithm, called ALMAp  (Approximate Large Mar- gin  algorithm  w.r.t.  norm p),  takes  0  ((P~21;;2) corrections  to  sepa(cid:173) rate the  data with p-norm margin larger than  (1  - 0:) ,,(,  where,,(  is  the  p-norm margin of the  data  and  X  is  a bound on  the p-norm of the  in(cid:173) stances.  ALMAp  avoids quadratic  (or higher-order) programming meth(cid:173) ods.  It is very easy to implement and is as fast as on-line algorithms, such  as  Rosenblatt's perceptron.  We  report on some experiments comparing  ALMAp  to  two  incremental  algorithms:  Perceptron  and  Li  and  Long's  ROMMA.  Our algorithm seems  to  perform quite better than  both.  The  accuracy levels achieved by ALMAp are slightly inferior to those obtained  by Support vector Machines (SVMs).  On the other hand, ALMAp is quite  faster and easier to implement than standard SVMs training algorithms.
Ensemble Learning and Linear Response Theory for ICA
Authors: Ole Winther, Lars Kai Hansen Pedro A. d. F. R. Højen-Sørensen
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  propose a general Bayesian framework for performing independent  component analysis  (leA) which  relies  on  ensemble  learning  and  lin(cid:173) ear response theory  known from  statistical physics.  We  apply it to both  discrete and continuous sources. For the continuous source the underde(cid:173) termined (overcomplete) case is  studied.  The naive mean-field approach  fails in this case whereas linear response theory-which gives an improved  estimate  of covariances-is  very  efficient.  The  examples  given  are  for  sources without temporal correlations. However, this derivation can eas(cid:173) ily  be  extended  to  treat  temporal  correlations.  Finally,  the  framework  offers a simple way  of generating new  leA algorithms without needing  to define the prior distribution of the sources explicitly.
Regularization with Dot-Product Kernels
Authors: Zoltán L. Óvári, Robert C. Williamson Alex J. Smola
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: In  this  paper  we  give  necessary  and  sufficient  conditions  under  which  kernels  of dot  product  type  k(x, y)  =  k(x . y)  satisfy Mer(cid:173) cer's  condition  and  thus  may  be  used  in  Support  Vector  Ma(cid:173) chines  (SVM),  Regularization  Networks  (RN)  or  Gaussian  Pro(cid:173) cesses  (GP).  In  particular,  we  show  that  if the  kernel  is  analytic  (i.e.  can be expanded in a  Taylor series),  all expansion coefficients  have to be nonnegative.  We  give an explicit functional form for  the  feature map by calculating its eigenfunctions and eigenvalues.
From Margin to Sparsity
Authors: Ralf Herbrich, Robert C. Williamson Thore Graepel
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  present  an  improvement  of Novikoff's  perceptron convergence  theorem.  Reinterpreting this mistake bound as a margin dependent  sparsity guarantee allows us to give a PAC-style generalisation er(cid:173) ror bound for the classifier learned by the perceptron learning algo(cid:173) rithm.  The bound value crucially depends on the margin a support  vector machine would achieve on the same data set using the same  kernel.  Ironically, the bound yields better guarantees than are cur(cid:173) rently available for  the support vector solution itself.
On a Connection between Kernel PCA and Metric Multidimensional Scaling
Authors: Christopher K. I. Williams
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: In this paper we  show that the kernel peA algorithm of Sch6lkopf  et al  (1998) can be interpreted as a form of metric multidimensional  scaling  (MDS)  when  the kernel function  k(x, y)  is  isotropic,  i.e.  it  depends  only  on  Ilx - yll.  This leads to a  metric  MDS  algorithm  where the desired  configuration of points is  found  via the solution  of an eigenproblem rather than through the iterative optimization  of the  stress  objective function.  The  question  of kernel  choice  is  also  discussed.
Interactive Parts Model: An Application to Recognition of On-line Cursive Script
Authors: Philip C. Davis, Leon N. Cooper Predrag Neskovic
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: In  this  work,  we  introduce  an  Interactive  Parts  (IP)  model as  an  alternative  to  Hidden  Markov  Models  (HMMs).  We  tested  both  models  on  a  database of on-line  cursive  script.  We  show  that  im(cid:173) plementations of HMMs  and the IP model, in which  all letters  are  assumed  to have  the same average width, give  comparable results.  However , in contrast to  HMMs,  the  IP model can handle duration  modeling without  an increase  in  computational complexity.
A New Model of Spatial Representation in Multimodal Brain Areas
Authors: Jean-René Duhamel, Alexandre Pouget Sophie Denève
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Most  models  of spatial representations in  the cortex  assume  cells  with limited receptive fields that are defined in a particular egocen(cid:173) tric frame  of reference.  However,  cells  outside  of primary sensory  cortex  are  either  gain  modulated  by  postural  input  or  partially  shifting.  We  show  that  solving  classical  spatial  tasks,  like  sen(cid:173) sory prediction, multi-sensory integration, sensory-motor transfor(cid:173) mation and motor control requires more complicated intermediate  representations  that  are  not  invariant  in  one  frame  of  reference.  We  present  an  iterative  basis  function  map  that  performs  these  spatial tasks optimally with gain modulated and partially shifting  units,  and tests it against  neurophysiological and neuropsycholog(cid:173) ical data. 
In  order  to  perform  an  action  directed  toward  an  object,  it  is  necessary  to  have  a  representation  of  its  spatial  location.  The  brain  must  be  able  to  use  spatial  cues coming from  different modalities (e.g.  vision, audition, touch, proprioception),  combine  them  to  infer  the  position  of the  object,  and  compute  the  appropriate  movement. 
These  cues  are  in  different  frames  of reference  corresponding  to  different  sensory  or  motor  modalities.  Visual  inputs  are  primarily  encoded  in  retinotopic  maps,  auditory  inputs  are  encoded  in  head  centered  maps  and  tactile  cues  are  encoded  in  skin-centered maps.  Going from  one frame  of reference to the other might  seem  easy.  For  example,  the  head-centered  position  of an  object  can  be  approximated  by the sum of its retinotopic position and the eye position.  However,  positions are  represented by population codes in the brain, and computing a  head-centered map  from  a  retinotopic map  is  a  more  complex computation than the underlying sum.  Moreover,  as  we  get  closer  to  sensory-motor  areas  it  seems  reasonable  to  assume
Feature Correspondence: A Markov Chain Monte Carlo Approach
Authors: Steven M. Seitz, Sebastian Thrun, Charles E. Thorpe Frank Dellaert
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: When  trying  to  recover  3D  structure  from  a  set  of  images,  the  most difficult  problem is  establishing  the correspondence  between  the measurements.  Most existing  approaches  assume that features  can be tracked across frames,  whereas methods that exploit rigidity  constraints to facilitate matching do so only under restricted  cam(cid:173) era  motion.  In  this  paper  we  propose  a  Bayesian  approach  that  avoids  the  brittleness  associated  with  singling out  one  "best"  cor(cid:173) respondence,  and instead consider the distribution over all possible  correspondences.  We  treat  both  a  fully  Bayesian  approach  that  yields  a  posterior  distribution,  and  a  MAP  approach  that  makes  use of EM  to maximize this posterior.  We show how  Markov chain  Monte  Carlo methods can  be  used  to implement these  techniques  in practice,  and present  experimental results on real  data.
Stagewise Processing in Error-correcting Codes and Image Restoration
Authors: Hidetoshi Nishimori K. Y. Michael Wong
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  introduce  stagewise  processing  in  error-correcting  codes  and  image restoration, by extracting information from the former stage  and  using  it  selectively  to  improve  the  performance  of the  latter  one.  Both  mean-field  analysis  using  the  cavity  method  and  sim(cid:173) ulations  show  that  it  has  the  advantage  of  being  robust  against  uncertainties in hyperparameter estimation.
The Kernel Gibbs Sampler
Authors: Ralf Herbrich Thore Graepel
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  present an algorithm that samples the hypothesis space of ker(cid:173) nel classifiers.  Given a uniform prior over normalised weight vectors  and  a  likelihood  based on  a  model  of label  noise leads to a  piece(cid:173) wise  constant  posterior that  can  be  sampled  by  the  kernel  Gibbs  sampler (KGS).  The KGS is a Markov Chain Monte Carlo method  that  chooses  a  random  direction  in  parameter space  and  samples  from the resulting piecewise constant density along the line chosen.  The KGS  can be used  as  an analytical tool for  the exploration of  Bayesian transduction, Bayes point machines,  active learning, and  evidence-based model selection on small data sets that are contam(cid:173) inated with  label noise.  For a  simple toy example  we  demonstrate  experimentally how  a Bayes point machine based on the KGS  out(cid:173) performs  an  SVM  that  is  incapable  of  taking  into  account  label  noise.
Learning Sparse Image Codes using a Wavelet Pyramid Architecture
Authors: Phil Sallee, Michael S. Lewicki Bruno A. Olshausen
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We  show  how  a  wavelet  basis  may  be  adapted  to  best  represent  natural images  in  terms  of sparse coefficients.  The  wavelet  basis,  which  may  be  either  complete  or  overcomplete,  is  specified  by  a  small number of spatial functions  which  are  repeated across space  and combined in a  recursive fashion  so  as  to be self-similar across  scale.  These functions are adapted to minimize the estimated code  length under a model that assumes images are composed of a linear  superposition of sparse,  independent  components.  When  adapted  to natural images, the wavelet  bases take on  different  orientations  and they evenly tile the orientation domain, in stark contrast to the  standard,  non-oriented  wavelet  bases  used  in  image  compression.  When  the  basis  set  is  allowed  to  be  overcomplete,  it  also  yields  higher coding efficiency than standard wavelet  bases.
Weak Learners and Improved Rates of Convergence in Boosting
Authors: Ron Meir Shie Mannor
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: The  problem  of  constructing  weak  classifiers  for  boosting  algo(cid:173) rithms is  studied.  We  present an algorithm that produces a  linear  classifier that is guaranteed to achieve an error better than random  guessing for  any distribution on the data.  While this weak  learner  is not useful for  learning in general, we show that under reasonable  conditions on the distribution it yields an effective weak learner for  one-dimensional  problems.  Preliminary  simulations  suggest  that  similar  behavior  can  be  expected  in  higher  dimensions,  a  result  which  is  corroborated by  some  recent  theoretical  bounds.  Addi(cid:173) tionally, we  provide improved convergence rate bounds for the gen(cid:173) eralization error in situations where the empirical error can be made  small,  which  is  exactly the  situation  that  occurs  if weak  learners  with guaranteed performance that is better than random guessing  can be established.
Keeping Flexible Active Contours on Track using Metropolis Updates
Authors: Brendan J. Frey Trausti T. Kristjansson
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Condensation, a form of likelihood-weighted particle filtering,  has been  successfully used to infer the shapes of highly constrained "active" con(cid:173) tours in video sequences.  However, when the contours are highly flexible  (e.g.  for tracking fingers of a hand), a computationally burdensome num(cid:173) ber of particles is needed to successfully approximate the contour distri(cid:173) bution.  We  show how  the Metropolis algorithm can be used to update a  particle set representing a distribution  over contours  at each  frame in a  video sequence.  We compare this method to condensation using a video  sequence that requires highly  flexible  contours,  and  show  that the  new  algorithm performs dramatically better that the condensation algorithm.  We  discuss  the  incorporation of this  method  into  the  "active  contour"  framework where a shape-subspace is  used constrain shape variation.
Data Clustering by Markovian Relaxation and the Information Bottleneck Method
Authors: Noam Slonim Naftali Tishby
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We introduce a new,  non-parametric and principled, distance based  clustering  method.  This  method  combines  a  pairwise  based  ap(cid:173) proach  with  a  vector-quantization  method which  provide  a  mean(cid:173) ingful  interpretation  to  the  resulting  clusters.  The  idea  is  based  on  turning  the  distance  matrix  into  a  Markov  process  and  then  examine the decay  of mutual-information during  the relaxation of  this  process.  The  clusters  emerge  as  quasi-stable  structures  dur(cid:173) ing  this  relaxation,  and  then  are  extracted  using  the  information  bottleneck method.  These  clusters  capture  the  information about  the  initial point  of the  relaxation  in  the  most  effective  way.  The  method can cluster data with no geometric or other bias and makes  no  assumption about the underlying distribution.
Balancing Multiple Sources of Reward in Reinforcement Learning
Authors: Christian R. Shelton
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: For many problems which  would be natural for reinforcement learning,  the reward signal is not a single scalar value but has multiple scalar com(cid:173) ponents.  Examples of such problems include agents  with multiple goals  and agents  with  multiple users.  Creating a single reward value by com(cid:173) bining  the  multiple  components  can throwaway  vital  information and  can lead  to incorrect solutions.  We describe the multiple reward source  problem  and  discuss  the  problems  with  applying  traditional  reinforce(cid:173) ment learning.  We  then present an  new  algorithm for finding a solution  and results on simulated environments.
Temporally Dependent Plasticity: An Information Theoretic Account
Authors: Naftali Tishby Gal Chechik
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: The paradigm of Hebbian learning has recently received a novel in(cid:173) terpretation with the discovery of synaptic plasticity that depends  on the relative timing of pre and post  synaptic spikes.  This paper  derives a temporally dependent learning rule from the basic princi(cid:173) ple of mutual information maximization and studies its relation to  the experimentally observed  plasticity.  We  find  that  a  supervised  spike-dependent learning rule sharing similar structure with the ex(cid:173) perimentally observed plasticity increases mutual information to a  stable near  optimal  level.  Moreover,  the  analysis  reveals  how  the  temporal structure of time-dependent learning rules is  determined  by the temporal filter  applied by neurons over their inputs.  These  results suggest experimental prediction as to the dependency of the  learning rule on neuronal biophysical parameters
Analysis of Bit Error Probability of Direct-Sequence CDMA Multiuser Demodulators
Authors: Toshiyuki Tanaka
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: We analyze the bit error probability of multiuser demodulators for direct(cid:173) sequence binary phase-shift-keying (DSIBPSK) CDMA channel with ad(cid:173) ditive gaussian noise. The problem of multiuser demodulation is cast  into the finite-temperature decoding problem, and replica analysis is ap(cid:173) plied to evaluate the performance of the resulting MPM (Marginal Pos(cid:173) terior Mode) demodulators, which include the optimal demodulator and  the MAP demodulator as special cases. An approximate implementa(cid:173) tion of demodulators is proposed using analog-valued Hopfield model  as a naive mean-field approximation to the MPM demodulators, and its  performance is also evaluated by the replica analysis. Results of the per(cid:173) formance evaluation shows effectiveness of the optimal demodulator and  the mean-field demodulator compared with the conventional one, espe(cid:173) cially in the cases of small information bit rate and low noise level.
A Variational Mean-Field Theory for Sigmoidal Belief Networks
Authors: S. Sathiya Keerthi Chiranjib Bhattacharyya
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: A variational derivation of Plefka's mean-field theory is  presented.  This  theory  is  then  applied  to  sigmoidal  belief networks  with  the  aid of further approximations.  Empirical evaluation on small scale  networks  show  that  the  proposed  approximations  are  quite  com(cid:173) petitive.
Robust Reinforcement Learning
Authors: Kenji Doya Jun Morimoto
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: This paper proposes a  new  reinforcement  learning  (RL)  paradigm  that explicitly takes into account input disturbance as well as mod(cid:173) eling errors.  The use of environmental models  in  RL  is  quite pop(cid:173) ular  for  both  off-line  learning  by  simulations  and  for  on-line  ac(cid:173) tion planning.  However, the difference between the model and the  real environment can lead to unpredictable, often unwanted results.  Based on the theory of H oocontrol, we consider a  differential game  in  which  a  'disturbing'  agent  (disturber)  tries  to  make  the  worst  possible  disturbance  while  a  'control'  agent  (actor)  tries  to  make  the best control input.  The problem is formulated as finding a min(cid:173) max solution of a  value function that takes into account the norm  of the output deviation and the norm of the disturbance.  We derive  on-line  learning  algorithms  for  estimating  the  value  function  and  for  calculating the worst disturbance and the best  control in refer(cid:173) ence to the value function.  We  tested the paradigm, which we call  "Robust  Reinforcement  Learning  (RRL),"  in  the task  of inverted  pendulum.  In  the  linear  domain,  the  policy  and  the  value  func(cid:173) tion learned by the on-line algorithms coincided with those derived  analytically  by the linear  H ootheory.  For a  fully  nonlinear  swing(cid:173) up task, the control by RRL  achieved  robust  performance against  changes in the pendulum weight  and friction  while a  standard RL  control could not  deal with such environmental changes.
Explaining Away in Weight Space
Authors: Sham Kakade Peter Dayan
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Explaining  away  has  mostly  been  considered  in  terms  of inference  of  states  in  belief networks.  We  show  how  it can  also  arise in a Bayesian  context in  inference about  the  weights  governing relationships  such  as  those  between stimuli  and reinforcers in  conditioning experiments such  as  bacA,'Ward blocking.  We  show how  explaining away  in  weight  space  can be accounted for using an  extension of a Kalman filter model; pro(cid:173) vide a new  approximate way  of looking at the Kalman gain  matrix as  a  whitener for  the correlation  matrix  of the  observation process;  suggest  a network implementation of this  whitener using an  architecture due to  Goodall; and show that the resulting model exhibits backward blocking.
Improved Output Coding for Classification Using Continuous Relaxation
Authors: Yoram Singer Koby Crammer
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Output coding is  a general  method for  solving  multiclass  problems  by  reducing them to  multiple binary classification problems.  Previous re(cid:173) search on output coding has employed, almost solely, predefined discrete  codes. We describe an algorithm that improves the performance of output  codes by relaxing them  to  continuous codes.  The relaxation procedure  is  cast  as  an  optimization  problem and  is  reminiscent of the  quadratic  program for support vector machines.  We describe experiments with the  proposed algorithm, comparing it to  standard discrete output codes.  The  experimental results indicate that continuous relaxations of output codes  often improve the generalization performance, especially for short codes.
Learning Curves for Gaussian Processes Regression: A Framework for Good Approximations
Authors: Manfred Opper Dörthe Malzahn
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: Based  on  a  statistical mechanics  approach,  we  develop  a  method  for approximately computing average case learning curves for Gaus(cid:173) sian  process  regression  models.  The  approximation  works  well  in  the large  sample size  limit  and for  arbitrary dimensionality  of the  input space.  We explain how the approximation can be systemati(cid:173) cally improved and argue that similar techniques can be applied to  general likelihood models.
Sequentially Fitting ``Inclusive'' Trees for Inference in Noisy-OR Networks
Authors: Relu Patrascu, Tommi Jaakkola, Jodi Moran Brendan J. Frey
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: An  important  class  of problems  can  be  cast  as inference  in noisy(cid:173) OR Bayesian networks,  where  the binary state of each variable is  a  logical  OR of noisy  versions  of the  states of the  variable's  par(cid:173) ents.  For example, in medical diagnosis, the presence of a symptom  can be expressed as a noisy-OR of the diseases that may cause the  symptom  - on  some  occasions,  a  disease  may fail  to  activate  the  symptom.  Inference  in  richly-connected noisy-OR networks is  in(cid:173) tractable,  but  approximate methods  (e .g.,  variational techniques)  are  showing  increasing  promise  as  practical  solutions.  One  prob(cid:173) lem  with  most  approximations  is  that  they  tend  to  concentrate  on  a  relatively  small  number  of  modes  in  the  true  posterior,  ig(cid:173) noring  other plausible  configurations of the  hidden  variables.  We  introduce a  new  sequential variational method for  bipartite noisy(cid:173) OR networks, that favors  including  all modes of the true posterior  and models the  posterior distribution  as  a  tree.  We  compare this  method with other approximations using an ensemble of networks  with network statistics that are comparable to the QMR-DT med(cid:173) ical diagnostic network. 
Inclusive variational approximations 
1  Approximate algorithms for probabilistic inference are gaining in popularity and are  now even being incorporated into VLSI hardware (T. Richardson, personal commu(cid:173) nication).  Approximate  methods include  variational techniques  (Ghahramani  and  Jordan 1997; Saul et al.  1996; Frey and Hinton 1999; Jordan et al.  1999), local prob(cid:173) ability propagation (Gallager 1963; Pearl 1988; Frey 1998; MacKay 1999a; Freeman  and Weiss 2001) and Markov chain Monte Carlo (Neal 1993; MacKay 1999b).  Many  algorithms have been proposed in each of these classes. 
One  problem that  most  of the  above  algorithms  suffer  from  is  a  tendency to  con(cid:173) centrate on a  relatively  small  number of modes  of the  target  distribution  (the  dis(cid:173) tribution  being  approximated).  In  the  case  of medical  diagnosis,  different  modes  correspond to different  explanations of the symptoms.  Markov  chain  Monte  Carlo  methods are usually guaranteed to eventually sample from  all  the modes,  but this  may take an extremely long time, even when tempered transitions  (Neal  1996)  are 
(a)
Whence Sparseness?
Authors: Carl van Vreeswijk
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: It has been shown that the receptive fields of simple cells in VI can be ex(cid:173) plained by assuming optimal encoding, provided that an extra constraint  of sparseness  is  added.  This  finding  suggests that there is  a reason, in(cid:173) dependent of optimal representation, for sparseness.  However this work  used  an  ad  hoc  model for the  noise.  Here I show that,  if a biologically  more plausible noise model, describing neurons as  Poisson processes, is  used  sparseness does  not have to  be  added as  a constraint.  Thus I con(cid:173) clude that sparseness is not a feature that evolution has striven for,  but is  simply the result of the evolutionary pressure towards an  optimal repre(cid:173) sentation.
Periodic Component Analysis: An Eigenvalue Method for Representing Periodic Structure in Speech
Authors: Jont B. Allen Lawrence K. Saul
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: An  eigenvalue  method  is  developed  for  analyzing  periodic  structure in  speech.  Signals are analyzed by  a matrix diagonalization reminiscent of  methods  for principal component analysis  (PCA)  and  independent com(cid:173) ponent analysis (ICA).  Our method-called periodic component analysis  (1l"CA)-uses  constructive interference to  enhance periodic  components  of the frequency  spectrum and  destructive interference  to  cancel  noise.  The front end emulates important aspects of auditory processing, such as  cochlear filtering, nonlinear compression, and insensitivity to phase, with  the  aim  of approaching the robustness of human  listeners.  The  method  avoids the inefficiencies of autocorrelation at the pitch period:  it does not  require  long  delay  lines,  and  it correlates  signals  at  a clock rate  on  the  order  of the  actual pitch,  as  opposed  to  the  original  sampling rate.  We  derive its cost function and present some experimental results.
Partially Observable SDE Models for Image Sequence Recognition Tasks
Authors: Paul Mineiro, Ruth J. Williams Javier R. Movellan
Published in: Advances in Neural Information Processing Systems 13  (NIPS 2000) (Main Conference Track), 2000

Abstract: This paper explores a framework for recognition of image sequences  using partially observable stochastic differential equation (SDE)  models. Monte-Carlo importance sampling techniques are used for  efficient estimation of sequence likelihoods and sequence likelihood  gradients. Once the network dynamics are learned, we apply the  SDE models to sequence recognition tasks in a manner similar to  the way Hidden Markov models (HMMs) are commonly applied.  The potential advantage of SDEs over HMMS is the use of contin(cid:173) uous state dynamics. We present encouraging results for a video  sequence recognition task in which SDE models provided excellent  performance when compared to hidden Markov models.