mtybilly/Instruct2DS huggingface dataset:
Information Factorization in Connectionist Models of Perception
Authors: James L. McClelland Javier R. Movellan
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We  examine  a  psychophysical  law  that  describes  the  influence  of  stimulus and context  on  perception.  According to this law  choice  probability  ratios  factorize  into  components  independently  con(cid:173) trolled by stimulus and context.  It has been argued that this  pat(cid:173) tern of results is  incompatible with feedback models of perception.  In this  paper we  examine this  claim  using neural  network models  defined via stochastic differential equations.  We show that the law  is  related to a  condition  named channel separability and has little  to do with the existence of feedback connections.  In essence, chan(cid:173) nels are separable if they converge into the response units without  direct lateral connections to other channels and if their sensors are  not  directly  contaminated  by  external  inputs  to  the  other  chan(cid:173) nels.  Implications of the analysis for  cognitive and computational  neurosicence are discussed.
Hierarchical Image Probability (H1P) Models
Authors: Lucas C. Parra Clay Spence
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We formulate a model for probability distributions on image spaces.  We  show that any distribution of images can be factored exactly into condi(cid:173) tional  distributions of feature  vectors at  one resolution  (pyramid level)  conditioned on the  image information at  lower resolutions.  We  would  like to factor this over positions in the pyramid levels to make it tractable,  but such factoring may miss long-range dependencies. To fix this, we in(cid:173) troduce hidden class  labels  at  each  pixel  in  the pyramid.  The result  is  a  hierarchical mixture of conditional probabilities,  similar  to  a  hidden  Markov model on a tree.  The model parameters can be found with max(cid:173) imum likelihood estimation using the EM algorithm.  We  have obtained  encouraging preliminary results on the problems of detecting various ob(cid:173) jects in SAR images and target recognition in optical aerial images.
Reinforcement Learning for Spoken Dialogue Systems
Authors: Michael J. Kearns, Diane J. Litman, Marilyn A. Walker Satinder P. Singh
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: Recently, a number of authors have proposed treating dialogue systems as Markov  decision processes (MDPs). However, the practical application ofMDP algorithms  to dialogue systems faces a number of severe technical challenges. We have built a  general software tool (RLDS, for Reinforcement Learning for Dialogue Systems)  based on the MDP framework, and have applied it to dialogue corpora gathered  from two dialogue systems built at AT&T Labs. Our experiments demonstrate that  RLDS holds promise as a tool for "browsing" and understanding correlations in  complex, temporally dependent dialogue corpora.
Distributed Synchrony of Spiking Neurons in a Hebbian Cell Assembly
Authors: Nir Levy, Isaac Meilijson, Eytan Ruppin David Horn
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We  investigate the behavior of a  Hebbian  cell  assembly of spiking  neurons formed via a temporal synaptic learning curve.  This learn(cid:173) ing  function  is  based  on  recent  experimental findings .  It  includes  potentiation for  short time delays  between  pre- and  post-synaptic  neuronal spiking,  and depression for  spiking events occuring in the  reverse order.  The coupling  between the dynamics of the synaptic  learning and of the neuronal activation leads to interesting results.  We  find  that  the  cell  assembly  can  fire  asynchronously,  but  may  also  function  in  complete  synchrony,  or  in  distributed  synchrony.  The latter implies spontaneous division of the Hebbian cell  assem(cid:173) bly into groups of cells  that fire  in  a cyclic manner.  We  invetigate  the behavior of distributed synchrony both by  simulations and by  analytic calculations of the resulting synaptic distributions.
Image Representations for Facial Expression Coding
Authors: Gianluca Donato, Javier R. Movellan, Joseph C. Hager, Paul Ekman, Terrence J. Sejnowski Marian Stewart Bartlett
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: The  Facial  Action  Coding  System  (FACS)  (9)  is  an  objective  method  for  quantifying  facial  movement  in  terms  of  component  actions.  This  system  is  widely  used  in  behavioral  investigations  of emotion,  cognitive  processes,  and  social  interaction.  The  cod(cid:173) ing is  presently  performed by  highly trained human experts.  This  paper  explores  and  compares  techniques  for  automatically  recog(cid:173) nizing facial actions in sequences of images.  These methods include  unsupervised  learning techniques  for  finding  basis  images  such  as  principal component analysis, independent component analysis and  local  feature  analysis,  and supervised learning  techniques  such  as  Fisher's  linear  discriminants.  These  data-driven  bases  are  com(cid:173) pared to Gabor wavelets, in which the basis images are predefined.  Best  performances  were  obtained  using  the  Gabor  wavelet  repre(cid:173) sentation and the  independent  component  representation,  both of  which achieved 96%  accuracy for  classifying 12 facial  actions.  The  ICA  representation employs  2 orders of magnitude fewer  basis im(cid:173) ages than the  Gabor representation and takes 90%  less  CPU  time  to compute for new images.  The results provide converging support  for using local basis images, high spatial frequencies, and statistical  independence for  classifying facial  actions.
Algorithms for Independent Components Analysis and Higher Order Statistics
Authors: Uri Rokni, Haim Sompolinsky Daniel D. Lee
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: A  latent  variable  generative  model  with  finite  noise  is  used  to  de(cid:173) scribe  several  different  algorithms  for  Independent Components  Anal(cid:173) ysis  (lCA).  In  particular,  the  Fixed  Point  ICA  algorithm  is  shown  to  be equivalent to  the Expectation-Maximization algorithm for maximum  likelihood  under certain  constraints,  allowing  the conditions for  global  convergence to  be  elucidated.  The algorithms can  also  be explained by  their generic  behavior  near  a  singular point where the  size  of the  opti(cid:173) mal generative bases vanishes.  An expansion of the likelihood about this  singular point indicates the role of higher order correlations in  determin(cid:173) ing the features discovered by  ICA. The application and convergence of  these algorithms are demonstrated on a simple illustrative example.
A SNoW-Based Face Detector
Authors: Dan Roth, Narendra Ahuja Ming-Hsuan Yang
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: A novel learning approach for human face detection using a network  of linear units is  presented.  The SNoW  learning architecture is  a  sparse network  of linear functions  over a  pre-defined or incremen(cid:173) tally learned feature  space  and  is  specifically tailored for  learning  in the presence of a  very large number of features.  A wide range of  face  images in different poses,  with different expressions and under  different  lighting conditions  are  used  as  a  training  set  to  capture  the  variations of human faces.  Experimental results on commonly  used benchmark data sets of a wide range of face images show that  the  SNoW-based  approach  outperforms  methods  that  use  neural  networks,  Bayesian  methods,  support  vector  machines  and  oth(cid:173) ers.  Furthermore, learning and  evaluation using  the  SNoW-based  method are significantly more efficient  than with other methods.
A Winner-Take-All Circuit with Controllable Soft Max Property
Authors: Shih-Chii Liu
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: I describe a silicon network consisting of a group of excitatory neu(cid:173) rons  and a  global inhibitory neuron.  The output of the inhibitory  neuron is normalized with respect to the input strengths.  This out(cid:173) put models the normalization property of the wide-field direction(cid:173) selective cells in the fly  visual system.  This normalizing property is  also useful  in any system where we  wish  the output signal to code  only the strength of the inputs, and not be dependent on the num(cid:173) ber of inputs.  The circuitry in each neuron is equivalent to that in  Lazzaro's winner-take-all  (WTA)  circuit  with one additional tran(cid:173) sistor  and  a  voltage  reference.  Just  as  in  Lazzaro's  circuit,  the  outputs of the excitatory neurons code the neuron with the largest  input.  The difference here is  that multiple winners can be chosen.  By  varying  the  voltage  reference  of the  neuron,  the  network  can  transition  between  a  soft-max  behavior  and  a  hard  WTA  behav(cid:173) ior.  I show results from  a fabricated chip of 20  neurons in a  1.2J.Lm  CMOS technology.
Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers
Authors: Matthias Seeger
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We present a  variational Bayesian method for model selection over  families of kernels classifiers like Support Vector machines or Gaus(cid:173) sian processes.  The algorithm needs no user interaction and is  able  to adapt a large number of kernel parameters to given data without  having to sacrifice training cases for validation.  This opens the pos(cid:173) sibility to use  sophisticated families  of kernels  in  situations  where  the small  "standard kernel"  classes  are clearly  inappropriate.  We  relate  the  method  to other work  done  on  Gaussian  processes  and  clarify the  relation between  Support Vector  machines  and  certain  Gaussian process models.
Kirchoff Law Markov Fields for Analog Circuit Design
Authors: Richard M. Golden
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: Three contributions to developing an algorithm for  assisting engi(cid:173) neers in designing analog circuits are provided in this paper.  First,  a  method  for  representing  highly  nonlinear  and  non-continuous  analog circuits using Kirchoff current law potential functions within  the context of a Markov field  is described.  Second, a relatively effi(cid:173) cient  algorithm for  optimizing the Markov field  objective function  is  briefly  described  and the  convergence proof is  briefly  sketched.  And  third,  empirical  results  illustrating  the strengths and limita(cid:173) tions  of the approach  are provided within the context  of a  JFET  transistor design problem.  The proposed algorithm generated a set  of circuit components for  the JFET circuit model  that accurately  generated the desired characteristic curves. 
1  Analog circuit design using Markov random fields 
1.1  Markov random field  models 
A Markov random field  (MRF) is a generalization of the concept of a Markov chain.  In a Markov field  one begins with a set of random variables and a  neighborhood re(cid:173) lation which  is  represented by a  graph.  Each  random  variable  will  be  assumed  in  this paper to be  a  discrete random variable which  takes on  one of a  finite  number  of possible  values.  Each  node  of the graph  indexs  a  specific  random  variable.  A  link  from  the  jth node  to  the  ith  node  indicates  that  the  conditional  probability  distribution of the ith random variable in the field  is functionally  dependent  upon  the jth random variable.  That is,  random variable j  is  a  neighbor of random  vari(cid:173) able i.  The only restriction upon the definition of a  Markov field  (Le.,  the positivity  condition)  is  that  the  probability  of every  realization  of the field  is  strictly  posi(cid:173) tive.  The essential idea behind  Markov field  design is that one specifies  a potential  (energy)  function  for  every clique in  the  neighborhood graph such  that the subset  of random  variables  associated  with  that clique  obtain their  optimal values  when  that clique'S  potential function  obtains  its  minimal  value  (for  reviews  see  [1]-[2]) . 
•  Associate Professor  at University of Texas at Dallas  (www.utdallas.eduj-901den) 
908
Can VI Mechanisms Account for Figure-Ground and Medial Axis Effects?
Authors: Zhaoping Li
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: When a  visual image consists of a figure against a background, V1  cells are physiologically observed to give higher responses to image  regions  corresponding  to  the figure  relative  to  their  responses  to  the  background.  The  medial  axis  of the figure  also  induces  rela(cid:173) tively  higher  responses  compared  to responses  to  other  locations  in the figure  (except for  the boundary between  the figure  and the  background).  Since the receptive fields  of V1  cells  are very smal(cid:173) l  compared with  the global  scale  of the figure-ground  and  medial  axis effects, it has been suggested that these effects  may be caused  by feedback from  higher visual areas.  I show how these effects can  be  accounted  for  by  V1  mechanisms  when  the  size  of the  figure  is  small  or  is  of a  certain scale.  They are  a  manifestation  of the  processes of pre-attentive segmentation which detect and highlight  the boundaries between homogeneous image regions.
Policy Gradient Methods for Reinforcement Learning with Function Approximation
Authors: David A. McAllester, Satinder P. Singh, Yishay Mansour Richard S. Sutton
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: Function  approximation  is  essential  to  reinforcement  learning,  but  the standard approach of approximating a  value function and deter(cid:173) mining  a  policy  from  it  has so  far  proven theoretically  intractable.  In this paper we explore an alternative approach in which the policy  is explicitly represented by its own function approximator,  indepen(cid:173) dent of the value function,  and is  updated according to the gradient  of expected reward with respect to the policy parameters.  Williams's  REINFORCE method and actor-critic methods are examples of this  approach.  Our  main  new  result  is  to  show  that  the  gradient  can  be  written  in  a  form  suitable  for  estimation  from  experience  aided  by  an  approximate  action-value  or  advantage  function.  Using  this  result,  we  prove for  the first  time that a  version  of policy  iteration  with arbitrary differentiable function approximation is convergent to  a  locally optimal policy. 
Large applications of reinforcement learning (RL) require the use of generalizing func(cid:173) tion approximators such neural networks,  decision-trees,  or instance-based methods.  The dominant approach for the last decade has been the value-function approach,  in  which  all  function  approximation  effort  goes  into  estimating  a  value  function,  with  the action-selection policy represented implicitly as the  "greedy"  policy with respect  to the estimated values  (e.g.,  as the policy that selects in each state the action with  highest estimated value).  The value-function approach has worked well in many appli(cid:173) cations,  but has several limitations.  First, it is oriented toward finding  deterministic  policies, whereas the optimal policy is often stochastic, selecting different actions with  specific probabilities (e.g.,  see Singh,  Jaakkola,  and Jordan,  1994).  Second,  an arbi(cid:173) trarily small change in the estimated value of an action can cause it to be, or not be,  selected.  Such discontinuous changes have been identified as a  key obstacle to estab(cid:173) lishing  convergence  assurances for  algorithms  following  the value-function approach  (Bertsekas  and Tsitsiklis,  1996).  For example,  Q-Iearning,  Sarsa,  and dynamic pro(cid:173) gramming methods have all  been shown unable to converge to any policy for  simple  MDPs  and simple  function  approximators  (Gordon,  1995,  1996;  Baird,  1995;  Tsit(cid:173) siklis and van Roy,  1996;  Bertsekas  and Tsitsiklis,  1996).  This can occur even if the  best approximation is found at each step before changing the policy,  and whether the  notion of "best"  is in the mean-squared-error sense or the slightly different  senses of  residual-gradient,  temporal-difference,  and dynamic-programming methods. 
In this  paper we  explore an  alternative  approach to  function  approximation  in  RL.
Lower Bounds on the Complexity of Approximating Continuous Functions by Sigmoidal Neural Networks
Authors: Michael Schmitt
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We calculate lower bounds on the size of sigmoidal neural networks  that  approximate  continuous  functions.  In  particular,  we  show  that  for  the  approximation  of  polynomials  the  network  size  has  to grow as  O((logk)1/4)  where  k  is  the degree of the polynomials.  This bound is  valid for  any input dimension, i.e.  independently of  the  number of variables.  The result  is  obtained  by  introducing a  new method employing upper bounds on the Vapnik-Chervonenkis  dimension  for  proving lower  bounds  on  the  size  of networks  that  approximate continuous functions.
Large Margin DAGs for Multiclass Classification
Authors: Nello Cristianini, John Shawe-Taylor John C. Platt
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We  present a  new  learning  architecture:  the  Decision  Directed  Acyclic  Graph  (DDAG),  which  is  used  to  combine  many  two-class  classifiers  into  a  multiclass  classifier.  For  an  N -class  problem,  the  DDAG  con(cid:173) tains N(N - 1)/2 classifiers, one for each pair of classes.  We present a  VC analysis of the case when the node classifiers are hyperplanes; the re(cid:173) sulting bound on the test error depends on N  and on the margin achieved  at  the  nodes,  but not  on  the  dimension  of the  space.  This motivates an  algorithm, DAGSVM, which operates in  a kernel-induced feature  space  and  uses  two-class maximal  margin  hyperplanes at each  decision-node  of the  DDAG.  The DAGSVM  is  substantially faster  to  train  and  evalu(cid:173) ate  than  either the  standard  algorithm  or Max Wins,  while maintaining  comparable accuracy to both of these algorithms.
Approximate Planning in Large POMDPs via Reusable Trajectories
Authors: Yishay Mansour, Andrew Y. Ng Michael J. Kearns
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We consider the problem of reliably choosing a near-best strategy from  a restricted class  of strategies TI  in  a partially observable Markov deci(cid:173) sion process (POMDP). We assume we are given the ability to simulate  the POMDP,  and study  what might be called the sample complexity - that  is,  the  amount of data one must  generate in  the  POMDP  in  order  to choose a good strategy.  We  prove upper bounds on  the sample com(cid:173) plexity  showing  that,  even  for  infinitely  large  and arbitrarily  complex  POMDPs,  the  amount  of data needed  can  be finite,  and  depends  only  linearly  on  the complexity of the  restricted strategy class  TI,  and  expo(cid:173) nentially  on  the horizon time.  This latter dependence can be eased in  a  variety  of ways,  including the  application  of gradient and  local  search  algorithms.  Our measure of complexity generalizes the classical super(cid:173) vised  learning notion  of VC  dimension to the settings  of reinforcement  learning and planning.
Maximum Entropy Discrimination
Authors: Marina Meila, Tony Jebara Tommi Jaakkola
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We present a general framework for discriminative estimation based  on the maximum entropy principle and its extensions.  All  calcula(cid:173) tions involve distributions over structures and/or parameters rather  than  specific  settings  and  reduce  to  relative  entropy  projections.  This holds  even  when  the data is  not  separable within  the chosen  parametric class,  in  the context of anomaly  detection  rather than  classification, or when the labels in the training set are uncertain or  incomplete.  Support  vector  machines  are  naturally subsumed  un(cid:173) der  this  class  and we  provide several extensions.  We  are also  able  to estimate exactly and efficiently discriminative distributions over  tree structures of class-conditional  models  within  this framework.  Preliminary experimental results  are  indicative of the potential in  these techniques.
The Relaxed Online Maximum Margin Algorithm
Authors: Philip M. Long Yi Li
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We  describe  a  new  incremental  algorithm  for  training  linear  thresh(cid:173) old  functions:  the  Relaxed  Online  Maximum  Margin  Algorithm,  or  ROMMA. ROMMA can be viewed as  an approximation to the algorithm  that repeatedly chooses the hyperplane that classifies previously seen ex(cid:173) amples  correctly  with  the  maximum  margin.  It  is  known  that  such  a  maximum-margin hypothesis can be computed by minimizing the length  of the weight vector subject to a number of linear constraints.  ROMMA  works  by  maintaining a relatively simple relaxation of these constraints  that can be efficiently updated.  We prove a mistake bound for ROMMA  that is the same as that proved for the perceptron algorithm.  Our analysis  implies that the more computationally intensive maximum-margin algo(cid:173) rithm also satisfies this mistake bound; this is the first worst-case perfor(cid:173) mance guarantee for this algorithm.  We describe some experiments us(cid:173) ing ROMMA and  a variant that updates its hypothesis more aggressively  as  batch  algorithms to recognize handwritten digits.  The computational  complexity  and  simplicity of these algorithms is  similar to  that of per(cid:173) ceptron algorithm , but their generalization is much better.  We describe a  sense  in  which the performance of ROMMA converges  to that of SVM  in the limit if bias isn't considered.
Bayesian Modelling of fMRI lime Series
Authors: Lars Kai Hansen, Carl Edward Rasmussen Pedro A. d. F. R. Højen-Sørensen
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We present a Hidden Markov Model (HMM) for inferring the hidden  psychological state (or neural activity) during single trial tMRI activa(cid:173) tion experiments with blocked task paradigms. Inference is based on  Bayesian methodology, using a combination of analytical and a variety  of Markov Chain Monte Carlo (MCMC) sampling techniques. The ad(cid:173) vantage of this method is that detection of short time learning effects be(cid:173) tween repeated trials is possible since inference is based only on single  trial experiments.
Bayesian Averaging is Well-Temperated
Authors: Lars Kai Hansen
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: Bayesian predictions are stochastic just like predictions of any other  inference scheme that generalize from a finite sample.  While a sim(cid:173) ple variational argument shows  that Bayes averaging is  generaliza(cid:173) tion  optimal given  that  the  prior  matches  the  teacher  parameter  distribution the situation is  less  clear  if the  teacher  distribution is  unknown.  I define  a class of averaging procedures,  the temperated  likelihoods,  including  both  Bayes  averaging  with  a  uniform  prior  and  maximum likelihood estimation as  special  cases.  I  show  that  Bayes  is  generalization optimal in this family  for  any  teacher  dis(cid:173) tribution for  two  learning problems that are  analytically tractable:  learning the mean of a Gaussian and asymptotics of smooth learn(cid:173) ers.
Policy Search via Density Estimation
Authors: Ronald Parr, Daphne Koller Andrew Y. Ng
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We  propose  a  new  approach  to  the  problem  of searching  a  space  of  stochastic controllers for a Markov decision process (MDP) or a partially  observable Markov decision process (POMDP). Following several other  authors,  our approach  is  based  on  searching  in  parameterized  families  of policies (for example, via gradient descent) to optimize solution qual(cid:173) ity.  However,  rather than  trying  to  estimate  the  values  and  derivatives  of a policy  directly,  we do  so  indirectly  using  estimates  for  the  proba(cid:173) bility  densities  that  the  policy  induces  on  states  at  the  different points  in  time.  This enables our algorithms to  exploit the many techniques for  efficient  and  robust approximate density  propagation  in  stochastic  sys(cid:173) tems.  We show how our techniques can  be applied both to deterministic  propagation schemes (where the MDP's dynamics are given explicitly in  compact form,)  and  to  stochastic  propagation schemes (where we  have  access only to a generative model, or simulator, of the MDP). We present  empirical results for both of these variants on complex problems.
Low Power Wireless Communication via Reinforcement Learning
Authors: Timothy X. Brown
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: This paper examines the application of reinforcement learning to a wire(cid:173) less  communication problem.  The problem requires  that channel  util(cid:173) ity  be  maximized while simultaneously minimizing battery  usage.  We  present a  solution  to  this  multi-criteria problem  that  is  able  to  signifi(cid:173) cantly reduce power consumption.  The solution uses a variable discount  factor to capture the effects of battery usage.
Learning to Parse Images
Authors: Zoubin Ghahramani, Yee Whye Teh Geoffrey E. Hinton
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We  describe a class of probabilistic models  that we  call credibility  networks.  Using  parse trees as  internal representations of images,  credibility  networks  are able  to  perform  segmentation  and  recog(cid:173) nition simultaneously,  removing the need for  ad hoc  segmentation  heuristics.  Promising results  in  the  problem of segmenting  hand(cid:173) written digits  were obtained. 
1
Robust Recognition of Noisy and Superimposed Patterns via Selective Attention
Authors: Michael Mozer Soo-Young Lee
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: In many classification tasks, recognition accuracy is low because input  patterns  are  corrupted  by  noise  or  are  spatially  or  temporally  overlapping. We propose an approach to overcoming these  limitations  based  on  a  model  of human  selective  attention.  The  model,  an  early  selection filter guided by top-down attentional control, entertains each  candidate  output  class  in  sequence  and  adjusts  attentional  gain  coefficients  in  order to produce a strong response  for that class.  The  chosen class is then the one that  obtains the strongest response with the  least  modulation  of  attention.  We  present  simulation  results  on  classification of corrupted and superimposed handwritten digit patterns,  showing a significant improvement in recognition rates.  The algorithm  has  also  been  applied  in  the  domain  of  speech  recognition,  with  comparable results.
Bayesian Network Induction via Local Neighborhoods
Authors: Sebastian Thrun Dimitris Margaritis
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: In  recent  years,  Bayesian  networks  have  become  highly  successful  tool  for  di(cid:173) agnosis,  analysis,  and  decision  making  in  real-world domains.  We  present  an  efficient algorithm for  learning Bayes  networks from  data.  Our approach  con(cid:173) structs Bayesian networks by first identifying each node's Markov blankets, then  connecting nodes  in  a maximally consistent way.  In  contrast to the majority of  work, which typically uses hill-climbing approaches that may produce dense and  causally incorrect nets, our approach yields much more compact causal networks  by heeding independencies in the data.  Compact causal networks facilitate fast in(cid:173) ference and are also easier to understand. We prove that under mild assumptions,  our approach requires time polynomial in the size of the data and the number of  nodes.  A randomized variant,  also presented here,  yields comparable results at  much higher speeds.
Spiking Boltzmann Machines
Authors: Andrew D. Brown Geoffrey E. Hinton
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We first show how to represent sharp posterior probability distribu(cid:173) tions using real valued coefficients on broadly-tuned basis functions.  Then we  show how  the precise times of spikes can be used to con(cid:173) vey the real-valued  coefficients on  the basis functions  quickly and  accurately.  Finally we  describe a  simple  simulation in which spik(cid:173) ing neurons learn to model an image sequence by fitting a dynamic  generative model. 
1  Population codes and  energy landscapes 
A perceived object is represented in the brain by the activities of many neurons, but  there is no general consensus on how the activities of individual neurons combine to  represent the multiple properties of an object.  We start by focussing on the case of  a single object that has multiple instantiation parameters such as position, velocity,  size and orientation.  We assume that each neuron has an ideal stimulus in the space  of instantiation parameters and that its activation rate or probability of activation  falls off monotonically in all directions as the actual stimulus departs from this ideal.  The semantic problem is  to define  exactly what instantiation parameters are being  represented when  the activities of many such neurons are specified. 
Hinton,  Rumelhart and  McClelland  (1986)  consider  binary neurons with  receptive  fields  that  are  convex  in  instantiation  space.  They  assume  that  when  an  object  is  present  it  activates  all  of the neurons  in whose  receptive  fields  its  instantiation  parameters  lie.  Consequently,  if it  is  known  that  only  one  object  is  present,  the  parameter  values  of the  object  must  lie  within  the  feasible  region  formed  by  the  intersection of the receptive fields  of the active neurons.  This will  be called a  con(cid:173) junctive  distributed  representation.  Assuming  that  each  receptive  field  occupies  only  a  small  fraction  of the  whole  space,  an  interesting  property  of this  type  of  "coarse coding"  is that the bigger the receptive fields,  the more accurate the repre(cid:173) sentation.  However,  large receptive fields  lead to a  loss  of resolution when  several  objects are present simultaneously. 
When the sensory input  is  noisy,  it  is  impossible  to infer  the exact  parameters of  objects so it  makes sense  for  a  perceptual system to represent  the probability dis(cid:173) tribution  across  parameters  rather  than  just  a  single  best  estimate  or  a  feasible  region.  The full  probability distribution is  essential for  correctly  combining  infor-
Spiking Boltzmann Machines
Actor-Critic Algorithms
Authors: John N. Tsitsiklis Vijay R. Konda
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We  propose  and  analyze  a  class  of  actor-critic  algorithms  for  simulation-based  optimization  of  a  Markov  decision  process  over  a  parameterized  family  of randomized  stationary  policies.  These  are two-time-scale  algorithms in  which  the critic uses TD learning  with  a  linear approximation architecture and the actor is  updated  in  an  approximate  gradient  direction  based  on  information  pro(cid:173) vided by the critic.  We  show that the features for  the critic should  span a subspace prescribed by the choice of parameterization of the  actor.  We  conclude by discussing convergence properties and some  open problems.
Training Data Selection for Optimal Generalization in Trigonometric Polynomial Networks
Authors: Hidemitsu Ogawa Masashi Sugiyama
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: In this  paper,  we  consider the problem of active learning  in trigonomet(cid:173) ric  polynomial  networks  and  give  a  necessary  and sufficient  condition of  sample  points to  provide the  optimal generalization capability.  By ana(cid:173) lyzing the condition from  the functional analytic point of view,  we  clarify  the  mechanism  of  achieving  the  optimal  generalization  capability.  We  also  show  that  a  set  of  training  examples  satisfying  the  condition  does  not only provide the optimal generalization  but also  reduces the compu(cid:173) tational  complexity and memory required  for  the  calculation of learning  results.  Finally, examples  of sample  points  satisfying  the  condition  are  given and computer simulations are performed to demonstrate the effec(cid:173) tiveness of the proposed  active learning method.
Image Recognition in Context: Application to Microscopic Urinalysis
Authors: Joseph Sill, Yaser S. Abu-Mostafa, Harvey Kasdan Xubo B. Song
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We  propose a  new  and efficient technique for  incorporating contextual  information into object classification. Most of the current techniques face  the problem of exponential computation cost.  In this paper, we propose a  new general framework that incorporates partial context at a linear cost.  This technique  is  applied  to  microscopic  urinalysis  image  recognition,  resulting in a significant improvement of recognition rate over the context  free approach. This gain would have been impossible using conventional  context incorporation techniques. 
1  BACKGROUND: RECOGNITION IN CONTEXT 
There are a number of pattern recognition problem domains where the classification of an  object should be based on more than simply the appearance of the object itself.  In remote  sensing image classification, where each pixel is part of ground cover, a pixel is more like(cid:173) ly  to  be a glacier if it is  in  a mountainous area, than  if surrounded by  pixels of residential  areas.  In text analysis, one can expect to find certain letters occurring regularly in  particu(cid:173) lar arrangement with  other letters(qu, ee,est, tion,  etc.).  The information conveyed by  the  accompanying entities is referred to as contextual information.  Human experts apply con(cid:173) textual information in their decision making [2][ 6].  It makes sense to design techniques and  algorithms to make computers aggregate and utilize a more complete set of information in  their decision making the way human experts do.  In  pattern recognition systems, however, 
*Author for correspondence 
964 
X  B.  Song, J  Sill,  Y.  Abu-Mostafa and H.  Kasdan 
the primary (and often only) source of information used to identify an object is the set of  measurements, or features, associated with the object itself.  Augmenting this information  by incorporating context into the classification process can yield significant benefits. 
i  =  1, ... N.  With  each  object  we  associate  a  Consider  a  set  of  N  objects  Ti ,  class  label  Ci  that  is  a  member  of  a  label  set  n  =  {1 , ... , D} .  Each  object  Ti  is  characterized  by  a  set  of  measurements  Xi  E  R P,  which  we  call  a  feature  vec(cid:173) tor.  Many  techniques  [1][2][4J[6}  incorporate  context  by  conditioning  the  posterior  probability  of objects'  identities  on  the joint features  of all  accompanying objects.  i.e .•  P(Cl, C2,··· , cNlxl , . . . , XN). and then maximizing it with respectto Cl, C2, . .. , CN . It can  be  shown  thatp(cl,c2, . . . ,cNlxl, . . . ,xN)  ex  p(cllxl) ... p(CNlxN)  (~ci""'(N\ given  certain reasonable assumptions.
Bayesian Map Learning in Dynamic Environments
Authors: Kevin P. Murphy
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We consider the problem of learning a grid-based map using a robot  with noisy sensors and actuators. We compare two approaches:  online EM, where the map is treated as a fixed parameter, and  Bayesian inference, where the map is a (matrix-valued) random  variable. We show that even on a very simple example, online EM  can get stuck in local minima, which causes the robot to get "lost"  and the resulting map to be useless. By contrast, the Bayesian  approach, by maintaining multiple hypotheses, is much more ro(cid:173) bust. We then introduce a method for approximating the Bayesian  solution, called Rao-Blackwellised particle filtering. We show that  this approximation, when coupled with an active learning strategy,  is fast but accurate.
Better Generative Models for Sequential Data Problems: Bidirectional Recurrent Mixture Density Networks
Authors: Mike Schuster
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: This  paper  describes  bidirectional  recurrent  mixture  density  net(cid:173) works,  which  can  model  multi-modal  distributions  of  the  type  P(Xt Iyf)  and  P(Xt lXI, X2 , ... ,Xt-l, yf) without  any  explicit  as(cid:173) sumptions  about  the  use  of context .  These  expressions  occur  fre(cid:173) quently  in  pattern  recognition  problems  with  sequential  data,  for  example  in  speech  recognition.  Experiments  show  that  the  pro(cid:173) posed generative models give  a higher likelihood on test data com(cid:173) pared to a  traditional modeling approach, indicating that they can  summarize  the statistical  properties of the data better.
Statistical Dynamics of Batch Learning
Authors: K. Y. Michael Wong Song Li
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: An important issue in neural computing concerns the description of  learning dynamics  with  macroscopic  dynamical  variables.  Recen(cid:173) t  progress on  on-line learning only  addresses the often unrealistic  case of an infinite  training set.  We  introduce a  new  framework  to  model batch learning of restricted sets of examples, widely applica(cid:173) ble to  any learning cost function,  and fully  taking into account the  temporal correlations introduced by the recycling of the examples.  For  illustration  we  analyze  the  effects  of weight  decay  and  early  stopping during the learning of teacher-generated examples.
Scale Mixtures of Gaussians and the Statistics of Natural Images
Authors: Eero P. Simoncelli Martin J. Wainwright
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: The statistics of photographic images, when represented using  multiscale (wavelet) bases, exhibit two striking types of non(cid:173) Gaussian behavior. First, the marginal densities of the coefficients  have extended heavy tails. Second, the joint densities exhibit vari(cid:173) ance dependencies not captured by second-order models. We ex(cid:173) amine properties of the class of Gaussian scale mixtures, and show  that these densities can accurately characterize both the marginal  and joint distributions of natural image wavelet coefficients. This  class of model suggests a Markov structure, in which wavelet coeffi(cid:173) cients are linked by hidden scaling variables corresponding to local  image structure. We derive an estimator for these hidden variables,  and show that a nonlinear "normalization" procedure can be used  to Gaussianize the coefficients. 
Recent years have witnessed a surge of interest in modeling the statistics of natural  images. Such models are important for applications in image processing and com(cid:173) puter vision, where many techniques rely (either implicitly or explicitly) on a prior  density. A number of empirical studies have demonstrated that the power spectra  of natural images follow a 1/ f'Y law in radial frequency, where the exponent "f is  typically close to two [e.g., 1]. Such second-order characterization is inadequate,  however, because images usually exhibit highly non-Gaussian behavior. For in(cid:173) stance, the marginals of wavelet coefficients typically have much heavier tails than  a Gaussian [2]. Furthermore, despite being approximately decorrelated (as sug(cid:173) gested by theoretical analysis of 1/ f processes [3]), orthonormal wavelet coefficients  exhibit striking forms of statistical dependency [4, 5]. In particular, the standard  deviation of a wavelet coefficient typically scales with the absolute values of its  neighbors [5]. 
A number of researchers have modeled the marginal distributions of wavelet coef(cid:173) ficients with generalized Laplacians, py(y) ex exp( -Iy/ AlP) [e.g. 6, 7, 8]. Special  cases include the Gaussian (p = 2) and the Laplacian (p = 1), but appropriate ex- Research supported by NSERC 1969 fellowship 160833 to MJW, and NSF CAREER grant  MIP-9796040 to EPS. 
856 
M J Wainwright and E. P. Simoncelli
Independent Factor Analysis with Temporally Structured Sources
Authors: Hagai Attias
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We  present  a  new  technique  for  time  series  analysis  based on  dy(cid:173) namic probabilistic networks.  In this approach,  the observed data  are modeled in terms of unobserved, mutually independent factors,  as in the recently introduced technique of Independent Factor Anal(cid:173) ysis  (IFA).  However,  unlike  in  IFA,  the  factors  are not  Li.d.;  each  factor has its own temporal statistical characteristics.  We  derive a  family of EM  algorithms that learn the structure of the underlying  factors  and  their  relation  to  the  data.  These  algorithms  perform  source separation and noise reduction in an integrated manner, and  demonstrate superior performance compared to IFA.
Potential Boosters?
Authors: David P. Helmbold Nigel Duffy
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: Recent  interpretations  of the  Adaboost  algorithm  view  it  as  per(cid:173) forming a gradient descent on a  potential function.  Simply chang(cid:173) ing the potential function  allows  one to create new  algorithms re(cid:173) lated  to  AdaBoost.  However,  these  new  algorithms are generally  not  known  to have  the formal  boosting property.  This  paper ex(cid:173) amines  the question  of which  potential  functions  lead  to  new  al(cid:173) gorithms that are boosters.  The two main results are general sets  of  conditions  on  the  potential;  one  set  implies  that  the  resulting  algorithm is  a  booster,  while the other implies  that the algorithm  is  not.  These conditions are applied to previously studied potential  functions , such as those used by LogitBoost and  Doom II.
Resonance in a Stochastic Neuron Model with Delayed Interaction
Authors: Yuzuru Sato, Jack D. Cowan Toru Ohira
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: We study here a simple stochastic single neuron model with delayed  self-feedback capable of generating spike  trains.  Simulations show  that its spike trains exhibit resonant behavior between "noise"  and  "delay".  In  order to gain  insight  into this  resonance,  we  simplify  the model and study a  stochastic binary element whose  transition  probability  depends  on  its  state  at  a  fixed  interval  in  the  past.  With this simplified model we  can analytically compute interspike  interval histograms, and show how the resonance between noise and  delay  arises.  The resonance  is  also  observed  when  such  elements  are coupled through delayed interaction. 
1
Wiring Optimization in the Brain
Authors: Charles F. Stevens Dmitri B. Chklovskii
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: The complexity of cortical circuits may be characterized by the number  of synapses per neuron.  We study the dependence of complexity on the  fraction of the cortical volume that is made up of "wire" (that is, ofaxons  and dendrites),  and find  that complexity is  maximized when wire takes  up about 60% of the cortical volume.  This prediction is  in  good agree(cid:173) ment with experimental observations.  A consequence of our arguments  is that any rearrangement of neurons that takes more wire would sacrifice  computational power. 
Wiring a brain presents formidable problems because of the extremely large number of con(cid:173) nections:  a microliter of cortex contains approximately 105  neurons, 109  synapses, and 4  km ofaxons, with 60% of the cortical  volume being taken up with "wire", half of this  by  axons and the other half by dendrites. [ 1] Each cortical neighborhood must have exactly the  right balance of components; if too many cell bodies were present in a particular mm cube,  for example, insufficient space would remain for the axons, dendrites and synapses.  Here  we ask "What fraction of the cortical volume should be wires (axons + dendrites)?" We ar(cid:173) gue that physiological properties ofaxons and dendrites dictate an optimal wire fraction of  0.6, just what is actually observed. 
To calculate the optimal wire fraction, we start with a real cortical region containing a fixed  number of neurons, a mm cube, for example, and imagine perturbing it by adding or sub(cid:173) tracting synapses and the axons and dendrites needed to support them.  The rules for per(cid:173) turbing the cortical cube require that the existing circuit connections and function remain  intact (except for what may have been removed in  the perturbation), that no holes are cre(cid:173) ated, and that all added (or subtracted) synapses are typical of those present; as wire volume  is added, the volume of the cube of course increases.  The ratio of the number of synapses  per neuron in  the perturbed cortex to  that in  the real  cortex is  denoted by 8,  a  parameter  we call the relative complexity.  We  require that the volume of non-wire components (cell  bodies, blood vessels,  glia, etc) is  unchanged by  our perturbation and use 4>  to denote the  volume fraction of the perturbed cortical region that is made up of wires (axons + dendrites;  4> can vary between zero and one), with the fraction for the real brain being 4>0.  The relation  between relative complexity 8 and wire volume fraction 4>  is given by the equation (derived  in Methods)