mtybilly/Instruct2DS huggingface dataset:
Improving Policies without Measuring Merits
Authors: Peter Dayan, Satinder P. Singh
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: Performing policy  iteration  in  dynamic  programming should  only  require knowledge of relative rather than absolute measures of the  utility of actions (Werbos,  1991) - what Baird  (1993)  calls the  ad(cid:173) vantages  of actions at states.  Nevertheless, most existing methods  in dynamic programming (including Baird's) compute some form of  absolute utility function .  For smooth problems,  advantages satisfy  two  differential  consistency  conditions  (including  the  requirement  that they be free of curl), and we show that enforcing these can lead  to appropriate policy improvement solely in terms of advantages. 
1
Estimating the Bayes Risk from Sample Data
Authors: Robert R. Snapp, Tong Xu
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: A new nearest-neighbor method is described for estimating the Bayes risk  of a multiclass pattern claSSification  problem  from  sample data  (e.g.,  a  classified training set). Although it is assumed that the classification prob(cid:173) lem can be accurately described by sufficiently smooth class-conditional  distributions, neither these distributions, nor the corresponding prior prob(cid:173) abilities of the classes are required.  Thus  this  method can  be applied to  practical problems where the underlying probabilities are not known.  This  method is illustrated using two different pattern recognition problems.
A Model of Auditory Streaming
Authors: Susan L. McCabe, Michael J. Denham
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: An essential feature of intelligent sensory processing is the ability to  focus on the part of the signal of interest against a background of  distracting signals, and to be able to direct this focus at will. In this  paper the problem of auditory scene segmentation is considered and a  model of the early stages of the process is proposed. The behaviour of  the model is shown to be in agreement with a number of well known  psychophysical results. The principal contribution of this model lies in  demonstrating how streaming might result from interactions between  the tonotopic patterns of activity of input signals and traces of previous  activity which feedback and influence the way in which subsequent  signals are processed.
Context-Dependent Classes in a Hybrid Recurrent Network-HMM Speech Recognition System
Authors: Dan J. Kershaw, Anthony J. Robinson, Mike Hochberg
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: A  method  for  incorporating  context-dependent  phone  classes  in  a  connectionist-HMM  hybrid  speech  recognition  system  is  intro(cid:173) duced.  A modular approach is adopted, where single-layer networks  discriminate between different context classes given the phone class  and  the  acoustic data.  The context networks  are combined with a  context-independent  (CI)  network  to  generate  context-dependent  (CD)  phone  probability estimates.  Experiments show  an  average  reduction  in  word  error  rate  of 16%  and  13%  from  the  CI  system  on  ARPA 5,000  word  and  SQALE  20,000  word  tasks respectively.  Due  to improved modelling, the decoding speed  of the  CD system  is  more than twice  as fast  as  the CI system.
Exploiting Tractable Substructures in Intractable Networks
Authors: Lawrence K. Saul, Michael I. Jordan
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We  develop  a  refined  mean field  approximation for  inference  and  learning  in  probabilistic neural  networks.  Our  mean field  theory,  unlike most, does not assume that the units behave as independent  degrees  of freedom;  instead,  it  exploits  in  a  principled  way  the  existence of large substructures that are computationally tractable.  To  illustrate  the  advantages  of this  framework,  we  show  how  to  incorporate weak higher order interactions into a first-order  hidden  Markov  model,  treating  the  corrections  (but  not  the  first  order  structure)  within mean field  theory.
Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective?
Authors: Shun-ichi Amari, Noboru Murata, Klaus-Robert Müller, Michael Finke, Howard Hua Yang
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: A  statistical  theory  for  overtraining  is  proposed.  The  analysis  treats realizable stochastic neural networks, trained with Kullback(cid:173) Leibler loss in the  asymptotic case.  It is shown that the asymptotic  gain  in  the  generalization error  is  small if we  perform early  stop(cid:173) ping, even if we have access to the optimal stopping time.  Consider(cid:173) ing cross-validation stopping we answer the question:  In what ratio  the examples should be divided into training and testing sets in or(cid:173) der  to  obtain  the  optimum  performance.  In  the  non-asymptotic  region  cross-validated early stopping always  decreases  the general(cid:173) ization  error.  Our  large  scale  simulations  done  on  a  CM5  are  in  nice  agreement with our analytical findings.
Silicon Models for Auditory Scene Analysis
Authors: John Lazzaro, John Wawrzynek
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We  are  developing  special-purpose,  low-power  analog-to-digital  converters  for  speech  and  music  applications,  that  feature  analog  circuit  models  of biological  audition  to  process  the  audio  signal  before  conversion.  This  paper describes  our most recent  converter  design,  and a working system that uses several copies ofthe chip to  compute  multiple representations  of sound  from  an  analog input.  This  multi-representation  system  demonstrates  the  plausibility of  inexpensively implementing an auditory scene  analysis approach to  sound  processing.
Human Face Detection in Visual Scenes
Authors: Henry A. Rowley, Shumeet Baluja, Takeo Kanade
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We  present  a  neural  network-based face  detection  system.  A retinally  connected  neural  network  examines  small  windows  of an  image,  and  decides  whether each  window  contains  a  face.  The  system  arbitrates  between multiple networks to improve performance over a single network.  We  use  a  bootstrap  algorithm for training, which  adds  false  detections  into the training set as  training progresses.  This eliminates the difficult  task  of manually  selecting  non-face  training examples,  which  must  be  chosen  to span the entire space of non-face images.  Comparisons with  another state-of-the-art face  detection system are presented;  our system  has better performance in terms of detection and false-positive rates.
SPERT-II: A Vector Microprocessor System and its Application to Large Problems in Backpropagation Training
Authors: John Wawrzynek, Krste Asanovic, Brian Kingsbury, James Beck, David Johnson, Nelson Morgan
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We  report  on  our  development  of a  high-performance  system  for  neural  network  and other signal  processing  applications.  We  have  designed  and  implemented  a  vector  microprocessor  and  pack(cid:173) aged  it  as  an  attached  processor  for  a  conventional  workstation.  We  present  performance  comparisons  with  commercial  worksta(cid:173) tions on neural  network  backpropagation training.  The SPERT-II  system  demonstrates  significant  speedups  over  extensively  hand(cid:173) optimization code running on  the workstations.
Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms
Authors: Ari Juels, Martin Wattenberg
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We investigate the effectiveness of stochastic hillclimbing as a baseline for  evaluating the  performance  of genetic  algorithms  (GAs)  as combinato(cid:173) rial function optimizers.  In particular, we address two problems to which  GAs have been applied in the literature:  Koza's ll-multiplexer problem  and the jobshop  problem.  We  demonstrate that simple  stochastic  hill(cid:173) climbing methods are  able  to achieve results  comparable or superior  to  those obtained by the GAs designed to address these two problems.  We  further  illustrate,  in the case  of the jobshop  problem,  how  insights  ob(cid:173) tained in the formulation of a stochastic hillclimbing algorithm can lead  to improvements in the encoding used by a  GA.
Using the Future to "Sort Out" the Present: Rankprop and Multitask Learning for Medical Risk Evaluation
Authors: Rich Caruana, Shumeet Baluja, Tom Mitchell
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: A patient visits the doctor; the doctor reviews  the patient's history,  asks questions, makes basic measurements (blood pressure,  .. . ), and  prescribes  tests  or  treatment .  The  prescribed  course  of action  is  based  on  an assessment of patient risk-patients at higher risk  are  given  more  and  faster  attention.  It is  also  sequential- it  is  too  expensive  to  immediately order  all  tests  which  might  later  be  of  value.  This  paper  presents  two  methods  that  together  improve  the  accuracy  of  backprop  nets  on  a  pneumonia  risk  assessment  problem  by  10-50%.  Rankprop improves on backpropagation  with  sum of squares error in ranking patients by risk.  Multitask learning  takes advantage of future lab tests  available in the training set,  but  not  available  in  practice  when  predictions  must  be  made.  Both  methods are broadly  applicable.
Exponentially many local minima for single neurons
Authors: Peter Auer, Mark Herbster, Manfred K. Warmuth
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We show that for a single neuron with the logistic function as the transfer  function the number of local  minima of the error function based on the  square loss can grow exponentially in the dimension.
An Information-theoretic Learning Algorithm for Neural Network Classification
Authors: David J. Miller, Ajit V. Rao, Kenneth Rose, Allen Gersho
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: A  new  learning  algorithm is  developed for  the design  of statistical  classifiers  minimizing  the  rate  of misclassification.  The  method,  which  is  based on  ideas  from  information  theory  and  analogies  to  statistical  physics,  assigns  data to  classes  in  probability.  The  dis(cid:173) tributions  are  chosen  to minimize the  expected  classification  error  while  simultaneously enforcing  the classifier's structure and a level  of "randomness"  measured  by  Shannon's entropy.  Achievement of  the classifier structure is  quantified by an  associated cost.  The con(cid:173) strained optimization  problem is  equivalent to the minimization of  a  Helmholtz  free  energy,  and  the  resulting  optimization  method  is  a  basic  extension  of the  deterministic  annealing  algorithm  that  explicitly  enforces  structural  constraints on  assignments  while  re(cid:173) ducing  the  entropy  and  expected  cost  with  temperature.  In  the  limit of low temperature, the error rate is minimized directly and a  hard classifier with the requisite structure is  obtained.  This learn(cid:173) ing algorithm can be used to design a variety of classifier structures.  The approach is  compared with standard methods for  radial basis  function  design  and  is  demonstrated  to  substantially  outperform  other  design  methods  on  several  benchmark  examples,  while  of(cid:173) ten retaining design complexity comparable to,  or only moderately  greater than that of strict descent-based methods. 
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D.  NnLLER.A.RAO.K. ROSE.A. GERSHO
Improving Elevator Performance Using Reinforcement Learning
Authors: Robert H. Crites, Andrew G. Barto
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: This paper describes the application of reinforcement learning (RL)  to the difficult  real  world problem of elevator dispatching.  The el(cid:173) evator domain poses a  combination of challenges not seen in  most  RL  research to date.  Elevator systems operate in continuous state  spaces  and in continuous time  as discrete  event  dynamic systems.  Their  states  are  not  fully  observable  and  they  are  nonstationary  due to changing passenger arrival rates.  In addition, we  use a team  of RL  agents,  each  of which is  responsible  for  controlling one  ele(cid:173) vator car.  The team receives  a  global reinforcement  signal  which  appears noisy to each agent due to the effects of the actions of the  other agents, the random nature of the arrivals and the incomplete  observation of the state.  In  spite  of these complications,  we  show  results that in simulation surpass the best of the heuristic elevator  control  algorithms  of which  we  are  aware.  These  results  demon(cid:173) strate  the  power  of RL  on  a  very  large  scale  stochastic  dynamic  optimization problem of practical utility.
Recursive Estimation of Dynamic Modular RBF Networks
Authors: Visakan Kadirkamanathan, Maha Kadirkamanathan
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: In this paper, recursive estimation algorithms for dynamic modular  networks  are  developed.  The  models  are  based  on Gaussian  RBF  networks  and  the  gating network  is  considered  in two  stages:  At  first,  it  is  simply  a  time-varying  scalar  and  in  the  second,  it  is  based  on the state, as in the mixture of local experts scheme.  The  resulting  algorithm  uses  Kalman  filter  estimation  for  the  model  estimation and the gating probability estimation.  Both, 'hard' and  'soft' competition based estimation schemes are developed where in  the former, the most probable network is  adapted and in the latter  all networks  are  adapted by  appropriate weighting of the data.
A Neural Network Classifier for the I100 OCR Chip
Authors: John C. Platt, Timothy P. Allen
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: This paper describes a neural network classifier for  the 11000  chip, which  optically  reads  the  E13B  font  characters  at  the  bottom of checks.  The  first  layer  of  the  neural  network  is  a  hardware  linear  classifier  which  recognizes  the  characters  in  this  font .  A  second  software  neural  layer  is  implemented  on  an  inexpensive  microprocessor  to  clean  up  the  re(cid:173) sults of the first  layer.  The hardware linear  classifier  is  mathematically  specified  using  constraints  and  an  optimization  principle.  The  weights  of the  classifier  are  found  using  the  active  set  method,  similar  to  Vap(cid:173) nik's separating hyperplane algorithm.  In 7.5 minutes ofSPARC 2 time,  the  method solves for  1523  Lagrange mUltipliers,  which  is  equivalent  to  training  on  a  data set  of approximately  128,000  examples.  The  result(cid:173) ing network  performs quite  well:  when  tested  on  a  test set  of 1500  real  checks,  it has  a  99.995%  character accuracy  rate. 
1  A  BRIEF OVERVIEW OF THE 11000  CHIP 
At Synaptics, we have created the 11000,  an analog VLSI chip that, when combined  with associated software, optically  reads  the E13B font  from  the bottom of checks.  This  E13B  font  is  shown  in  figure  1.  The  overall  architecture  of the  11000  chip  is  shown  in  figure  2.  The 11000  recognizes  checks  hand-swiped  through  a  slot.  A  lens  focuses  the image  of the bottom of the  check  onto the  retina.  The  retina has  circuitry  which  locates  the  vertical  position  of the  characters  on  the  check .  The  retina then  sends  an  image  vertically  centered  around  a  possible  character  to  the  classifier.  The classifier  in  the nooo  has  a  tough job.  It  must  be  very accurate  and immune  to  noise  and ink scribbles  in  the input .  Therefore , we  decided  to use  an  integrated  segmentation  and  recognition  approach  (Martin  &  Pittman,  1992)(Platt,  et  al.,  1992).  When the classifier produces a  strong response,  we  know  that a  character is  horizontally  centered in  the retina. 
A Neural Network Classifier for the 11000 OCR Chip 
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Figure  1: The  E13B font,  as  seen  by  the 11000  chip
Modeling Saccadic Targeting in Visual Search
Authors: Rajesh P. N. Rao, Gregory J. Zelinsky, Mary M. Hayhoe, Dana H. Ballard
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: Visual cognition depends criticalIy on the ability to make rapid eye movements  known as saccades that orient the fovea over targets of interest in a visual  scene. Saccades are known to be ballistic: the pattern of muscle activation  for foveating a prespecified target location is computed prior to the movement  and visual feedback is precluded. Despite these distinctive properties, there  has been no general model of the saccadic targeting strategy employed by  the human visual system during visual search in natural scenes. This paper  proposes a model for saccadic targeting that uses iconic scene representations  derived from oriented spatial filters at multiple scales. Visual search proceeds  in a coarse-to-fine fashion with the largest scale filter responses being compared  first. The model was empirically tested by comparing its perfonnance with  actual eye movement data from human subjects in a natural visual search task;  preliminary results indicate substantial agreement between eye movements  predicted by the model and those recorded from human subjects.
Plasticity of Center-Surround Opponent Receptive Fields in Real and Artificial Neural Systems of Vision
Authors: S. Yasui, T. Furukawa, M. Yamada, T. Saito
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: Despite  the  phylogenic  and  structural  differences,  the  visual  sys(cid:173) tems of different species, whether vertebrate or invertebrate, share  certain functional properties.  The center-surround opponent recep(cid:173) tive field  (CSRF)  mechanism represents  one  such example.  Here,  analogous  CSRFs  are  shown  to  be  formed  in  an  artificial  neural  network which learns to localize contours (edges)  of the luminance  difference.  Furthermore,  when  the  input  pattern  is  corrupted  by  a  background noise,  the CSRFs of the hidden  units  becomes shal(cid:173) lower and broader with decrease of the signal-to-noise ratio (SNR).  The same kind of SNR-dependent plasticity is present in the CSRF  of real visual neurons; in bipolar cells of the carp retina as is shown  here  experimentally,  as  well  as  in  large  monopolar  cells  of the  fly  compound eye  as  was  described  by others.  Also,  analogous  SNR(cid:173) dependent  plasticity  is  shown  to  be  present  in  the  biphasic  flash  responses  (BPFR) of these  artificial  and  biological visual systems .  Thus,  the spatial  (CSRF)  and  temporal  (BPFR) filtering  proper(cid:173) ties with  which a  wide variety of creatures see the world appear to  be optimized for  detectability of changes in space  and time.
Active Gesture Recognition using Learned Visual Attention
Authors: Trevor Darrell, Alex Pentland
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We have developed a foveated  gesture recognition system that runs  in an unconstrained office environment with an active camera.  Us(cid:173) ing vision routines previously implemented for  an interactive envi(cid:173) ronment,  we  determine  the  spatial  location  of salient  body  parts  of a  user  and guide  an active  camera to obtain images of gestures  or expressions.  A hidden-state reinforcement  learning paradigm is  used  to implement visual  attention.  The  attention  module selects  targets  to foveate  based on the goal of successful  recognition,  and  uses  a  new  multiple-model  Q-Iearning  formulation.  Given  a  set  of target  and  distractor  gestures,  our  system  can  learn  where  to  foveate  to maximally discriminate a  particular gesture.
On Neural Networks with Minimal Weights
Authors: Vasken Bohossian, Jehoshua Bruck
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: Linear threshold elements are the basic building blocks of artificial  neural  networks.  A  linear  threshold element computes  a  function  that is  a sign of a weighted sum of the input variables.  The weights  are  arbitrary  integers;  actually,  they  can  be  very  big  integers-(cid:173) exponential  in  the  number  of  the  input  variables.  However,  in  practice,  it  is  difficult  to  implement  big  weights.  In  the  present  literature  a  distinction  is  made  between  the  two  extreme  cases:  linear threshold functions  with polynomial-size weights as opposed  to those  with exponential-size weights.  The  main  contribution  of  this paper is  to fill  up  the gap by further  refining  that separation.  Namely,  we  prove that the class of linear threshold functions  with  polynomial-size  weights  can  be  divided  into  subclasses  according  to the degree of the polynomial.  In fact,  we  prove  a  more general  that there exists a minimal weight linear threshold function  result- for  any arbitrary number of inputs and any  weight  size.  To prove  those results we  have developed a  novel technique for  constructing  linear threshold functions  with minimal weights.
Neural Networks with Quadratic VC Dimension
Authors: Pascal Koiran, Eduardo D. Sontag
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: This paper shows that neural  networks  which  use  continuous acti(cid:173) vation functions have VC  dimension at least as large as the square  of  the  number  of weights  w.  This  result  settles  a  long-standing  open  question,  namely whether  the well-known  O( w log w)  bound,  known for hard-threshold nets, also held for  more general sigmoidal  nets.  Implications for  the number of samples needed for  valid gen(cid:173) eralization are  discussed.
Factorial Hidden Markov Models
Authors: Zoubin Ghahramani, Michael I. Jordan
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We present a framework for learning in hidden Markov models with  distributed  state  representations.  Within  this framework ,  we  de(cid:173) rive  a  learning algorithm based  on  the  Expectation-Maximization  (EM)  procedure for  maximum likelihood estimation.  Analogous to  the  standard  Baum-Welch  update  rules,  the  M-step  of our  algo(cid:173) rithm is  exact and can be solved analytically.  However,  due  to the  combinatorial nature of the  hidden state representation,  the exact  E-step is intractable.  A simple and tractable mean field approxima(cid:173) tion is  derived.  Empirical results on a set of problems suggest that  both  the mean field  approximation and Gibbs sampling are viable  alternatives to  the computationally expensive exact algorithm.
Extracting Tree-Structured Representations of Trained Networks
Authors: Mark Craven, Jude W. Shavlik
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: A  significant  limitation  of  neural  networks  is  that  the  represen(cid:173) tations  they  learn  are  usually  incomprehensible  to  humans.  We  present a  novel algorithm , TREPAN,  for extracting comprehensible,  symbolic representations from  trained neural  networks.  Our algo(cid:173) rithm uses  queries to induce a  decision tree that approximates the  concept  represented by  a  given  network.  Our experiments demon(cid:173) strate that TREPAN is  able to produce decision trees that maintain  a high level of fidelity to their respective networks while being com(cid:173) prehensible  and  accurate.  Unlike  previous  work  in  this  area,  our  algorithm is  general in its applicability and scales well  to large net(cid:173) works  and problems with  high-dimensional input spaces.
Improving Committee Diagnosis with Resampling Techniques
Authors: Bambang Parmanto, Paul W. Munro, Howard R. Doyle
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: Central to the performance improvement of a committee relative to  individual networks is the error correlation between networks in the  committee.  We  investigated  methods  of achieving  error  indepen(cid:173) dence between the networks by training the networks with different  resampling sets  from  the  original training set.  The  methods were  tested  on the sinwave artificial task and the real-world problems of  hepatoma (liver cancer)  and breast  cancer  diagnoses.
Boosting Decision Trees
Authors: Harris Drucker, Corinna Cortes
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We introduce a constructive,  incremental learning system for regression  problems that models data by means of locally linear experts.  In contrast  to  other approaches,  the  experts  are  trained  independently  and  do  not  compete for data during learning.  Only when a prediction for  a query  is  required  do  the  experts  cooperate  by  blending  their  individual  predic(cid:173) tions.  Each expert is trained by  minimizing  a penalized local cross vali(cid:173) dation error using second order methods. In this way, an expert is able to  find a local distance metric by adjusting the size and  shape of the recep(cid:173) tive field in which its predictions are valid, and also to detect relevant in(cid:173) put features  by  adjusting  its bias  on  the  importance of individual input  dimensions. We derive asymptotic results for our method. In a variety of  simulations the properties of the algorithm are demonstrated with respect  to  interference,  learning  speed,  prediction  accuracy,  feature  detection,  and task oriented incremental learning.
Some results on convergent unlearning algorithm
Authors: Serguei A. Semenov, Irina B. Shuvalova
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: In  this  paper  we  consider  probabilities of different  asymptotics of  convergent  unlearning  algorithm for  the  Hopfield-type  neural  net(cid:173) work  (Plakhov  &  Semenov,  1994)  treating  the  case  of unbiased  random  patterns.  We  show  also  that  failed  unlearning  results  in  total memory breakdown.
Forward-backward retraining of recurrent neural networks
Authors: Andrew W. Senior, Anthony J. Robinson
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: This  paper  describes  the  training  of  a  recurrent  neural  network  as  the  letter  posterior  probability estimator for  a  hidden  Markov  model,  off-line  handwriting recognition  system.  The network  esti(cid:173) mates  posterior  distributions for  each  of a  series  of frames  repre(cid:173) senting  sections  of a  handwritten  word.  The  supervised  training  algorithm,  backpropagation through time,  requires  target outputs  to  be  provided for  each  frame.  Three  methods for  deriving  these  targets  are  presented.  A  novel  method  based  upon  the  forward(cid:173) backward  algorithm is  found  to  result  in  the  recognizer  with  the  lowest  error  rate.
KODAK lMAGELINK™ OCR Alphanumeric Handprint Module
Authors: Alexander Shustorovich, Christopher W. Thrasher
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: This  paper  describes  the  Kodak  Imageliok TM  OCR  alphanumeric  handprint  module.  There  are  two  neural  network  algorithms  at  its  cme:  the  first  network  is  trained  to  find  individual  characters  in  an  alphamuneric  field,  while  the  second one perfmns the  classification.  Both  networks  were  trained  on  Gabor  projections  of  the  ociginal  pixel  images,  which  resulted  in higher  recognition  rates  and  greater  noise  immunity.  Compared  its  purely  numeric  counterpart  (Shusurovich  and  Thrasher,  1995),  this  version  of the  system  has  a  significant  applicatim  specific  postprocessing  module.  The  system  has  been  implemented in specialized parallel hardware,  which allows  it to run at 80 char/sec/board.  It has been installed  at the Driver and  Vehicle  Licensing  Agency  (DVLA)  in  the  United  Kingdom.  and  its  overall  success  rate  exceeds  96%  (character  level  without  rejects).  which  translates  into  85%  field  rate.  If approximately  20%  of the  fields  are  rejected.  the  system  achieves  99.8%  character  and  99.5%  field success rate. 
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Predictive Q-Routing: A Memory-based Reinforcement Learning Approach to Adaptive Traffic Control
Authors: Samuel P. M. Choi, Dit-Yan Yeung
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: In  this  paper,  we  propose  a  memory-based  Q-Iearning  algorithm  called  predictive  Q-routing  (PQ-routing)  for  adaptive  traffic  con(cid:173) trol.  We attempt to address two problems encountered in Q-routing  (Boyan  &  Littman,  1994),  namely,  the inability to fine-tune  rout(cid:173) ing policies under  low  network  load and  the  inability to learn  new  optimal  policies  under  decreasing  load  conditions.  Unlike  other  memory-based  reinforcement  learning  algorithms  in  which  mem(cid:173) ory  is  used  to  keep  past  experiences  to  increase  learning  speed,  PQ-routing  keeps  the  best  experiences  learned  and  reuses  them  by  predicting  the  traffic  trend.  The  effectiveness  of  PQ-routing  has been  verified  under various network  topologies and traffic con(cid:173) ditions.  Simulation  results  show  that  PQ-routing  is  superior  to  Q-routing in terms of both learning speed and  adaptability.
Modeling Interactions of the Rat's Place and Head Direction Systems
Authors: A. David Redish, David S. Touretzky
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We  have  developed  a  computational  theory  of rodent  navigation  that  includes analogs of the place cell system, the head direction system,  and  path integration.  In this paper we present simulation results showing how  interactions between the place and head direction systems can account for  recent observations about hippocampal  place cell  responses to doubling  and/or rotation of cue cards in a cylindrical arena (Sharp et at.,  1990). 
Rodents have multiple internal representations of their relationship to their environment.  They  have,  for example,  a representation of their location (place cells in  the hippocampal  formation,  see  Muller  et  at.,  1991),  and  a  location-independent representation  of their  heading  (head  direction  cells  in  the  postsubiculum and  the  anterior thalamic  nuclei,  see  Taube et at.,  1990; Taube,  1995).  If these  representations  are  to  be  used  for  navigation, they  must  be  aligned  consistently  whenever the animal reenters a familiar environment.  This process was examined in a set  of experiments by Sharp et at.  (1990). 
1  The Sharp et al., 1990 experiment 
Rats spent multiple sessions finding food scattered randomly on the floor of a black cylin(cid:173) drical arena with a white cue card along the wall subtending 90°  of arc.  The animals were  not disoriented before entering the arena, and they always entered at the same location:  the  northwest corner.  See  Figure 3a.  Hippocampal  place  fields  were  mapped  by  single-cell  recording.  A variety of probe trials were then  introduced.  When  an  identical second  cue 
62 
A. D. REDISH, D. S. TOURETZKY
Empirical Entropy Manipulation for Real-World Problems
Authors: Paul A. Viola, Nicol N. Schraudolph, Terrence J. Sejnowski
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: No finite sample is sufficient to determine the density, and therefore  the entropy, of a signal directly.  Some assumption about either the  functional form of the density or about its smoothness is necessary.  Both amount to a prior over the space of possible density functions.  By  far  the  most common approach  is  to  assume  that  the  density  has a  parametric form. 
By  contrast  we  derive  a  differential  learning  rule  called  EMMA  that  optimizes  entropy  by  way  of kernel  density  estimation.  En(cid:173) tropy  and  its  derivative  can  then  be  calculated  by  sampling from  this density  estimate.  The resulting  parameter update rule  is  sur(cid:173) prisingly simple and efficient. 
We  will  show  how  EMMA  can  be  used  to detect  and  correct  cor(cid:173) ruption  in  magnetic  resonance  images  (MRI).  This  application  is  beyond  the scope  of existing parametric entropy  models.
A Neural Network Model of 3-D Lightness Perception
Authors: Luiz Pessoa, William D. Ross
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: A  neural  network  model  of 3-D  lightness  perception  is  presented  which  builds  upon  the  FACADE  Theory  Boundary  Contour  Sys(cid:173) tem/Feature Contour  System  of Grossberg  and  colleagues.  Early  ratio encoding by  retinal ganglion neurons  as  well  as psychophysi(cid:173) cal  results on constancy  across  different  backgrounds (background  constancy)  are used to provide functional constraints to the theory  and  suggest  a  contrast  negation  hypothesis  which  states that ratio  measures  between  coplanar  regions  are  given  more  weight  in  the  determination  of lightness  of the  respective  regions.  Simulations  of the  model  address  data on  lightness  perception,  including  the  coplanar ratio hypothesis,  the  Benary cross,  and White's illusion.
Adaptive Retina with Center-Surround Receptive Field
Authors: Shih-Chii Liu, Kwabena Boahen
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: Both vertebrate and invertebrate retinas are highly efficient  in ex(cid:173) tracting contrast independent of the background intensity over five  or  more  decades.  This  efficiency  has  been  rendered  possible  by  the adaptation of the DC operating point to the background inten(cid:173) sity while maintaining high gain  transient responses.  The center(cid:173) surround properties of the retina allows the system  to extract in(cid:173) formation at the edges in the image.  This silicon retina models the  adaptation properties of the receptors and the antagonistic center(cid:173) surround properties of the laminar cells  of the invertebrate retina  and the outer-plexiform layer of the vertebrate retina.  We also illus(cid:173) trate the spatio-temporal responses of the silicon retina on moving  bars.  The  chip  has  59x64  pixels  on  a  6.9x6.8mm2  die  and  it  is  fabricated in 2 J-tm  n-well  technology.
Does the Wake-sleep Algorithm Produce Good Density Estimators?
Authors: Brendan J. Frey, Geoffrey E. Hinton, Peter Dayan
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: The wake-sleep algorithm (Hinton, Dayan, Frey and Neal 1995) is a rel(cid:173) atively efficient method of fitting  a multilayer stochastic generative  model to high-dimensional data. In addition to  the top-down connec(cid:173) tions in the generative model, it makes use of bottom-up connections for  approximating the probability distribution over the hidden units given  the data, and it trains these bottom-up connections using a simple delta  rule. We use a variety of synthetic and real data sets to compare the per(cid:173) formance of the wake-sleep algorithm with Monte Carlo and mean field  methods for fitting  the same generative model and also compare it with  other models that are less powerful but easier to fit.
EM Optimization of Latent-Variable Density Models
Authors: Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: There is  currently  considerable interest  in developing general non(cid:173) linear  density  models  based  on  latent,  or  hidden,  variables.  Such  models have the ability to discover the presence of a relatively small  number of underlying  'causes'  which,  acting  in  combination,  give  rise  to the apparent complexity of the observed  data set.  Unfortu(cid:173) nately, to train such models generally requires large computational  effort.  In this paper we  introduce a  novel  latent variable algorithm  which retains the general non-linear capabilities of previous models  but  which  uses  a  training procedure  based  on  the  EM  algorithm.  We  demonstrate  the  performance  of the  model  on  a  toy  problem  and on data from flow  diagnostics for  a  multi-phase oil  pipeline.
Modern Analytic Techniques to Solve the Dynamics of Recurrent Neural Networks
Authors: A.C.C. Coolen, S. N. Laughton, D. Sherrington
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We  describe the use of modern analytical techniques in  solving the  dynamics  of  symmetric  and  nonsymmetric  recurrent  neural  net(cid:173) works  near saturation.  These explicitly take into account the cor(cid:173) relations between  the  post-synaptic  potentials,  and  thereby  allow  for  a  reliable prediction of transients.
Learning Fine Motion by Markov Mixtures of Experts
Authors: Marina Meila, Michael I. Jordan
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: Compliant control is a standard method for performing fine  manip(cid:173) ulation tasks, like grasping and assembly, but it requires estimation  of the state of contact  (s.o.c.)  between  the robot  arm and  the  ob(cid:173) jects  involved.  Here  we  present  a  method to learn  a  model of the  movement from  measured  data.  The  method  requires  little or  no  prior  knowledge  and  the  resulting  model  explicitly  estimates  the  s.o.c.  The  current  s.o.c.  is  viewed  as  the  hidden  state  variable  of  a  discrete  HMM.  The  control  dependent  transition  probabilities  between  states  are  modeled  as  parametrized functions  of the mea(cid:173) surement.  We  show  that  their  parameters  can  be  estimated  from  measurements at the same time as the parameters of the movement  in  each  s.o.c.  The learning algorithm is  a  variant of the EM  proce(cid:173) dure.  The  E step  is  computed exactly ; solving  the  M step  exactly  is  not possible  in general.  Here,  gradient ascent  is  used  to produce  an increase  in  likelihood .
Stable LInear Approximations to Dynamic Programming for Stochastic Control Problems with Local Transitions
Authors: Benjamin Van Roy, John N. Tsitsiklis
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We  consider  the  solution  to  large  stochastic  control  problems  by  means of methods that rely on compact representations and a vari(cid:173) ant of the value iteration algorithm to compute approximate cost(cid:173) to-go functions.  While  such methods are known  to be unstable in  general, we identify a new class of problems for  which convergence,  as  well  as  graceful  error  bounds,  are  guaranteed.  This  class  in(cid:173) volves linear parameterizations of the cost-to- go function  together  with  an assumption  that the  dynamic  programming operator is  a  contraction  with  respect  to  the  Euclidean  norm  when  applied  to  functions  in  the  parameterized  class.  We  provide  a  special  case  where  this  assumption  is  satisfied,  which  relies  on  the  locality of  transitions in a  state space.  Other cases will  be discussed  in a  full  length version of this paper.
Generalisation of A Class of Continuous Neural Networks
Authors: John Shawe-Taylor, Jieyu Zhao
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We  propose  a  way of using  boolean circuits  to perform real valued  computation  in  a  way  that  naturally  extends  their  boolean  func(cid:173) tionality.  The  functionality  of multiple  fan  in  threshold  gates  in  this  model  is  shown  to  mimic  that  of a  hardware implementation  of continuous Neural Networks.  A Vapnik-Chervonenkis dimension  and  sample  size  analysis  for  the  systems  is  performed  giving  best  known  sample sizes  for  a  real  valued  Neural Network.  Experimen(cid:173) tal results confirm the conclusion that the sample sizes required for  the  networks  are significantly  smaller than for  sigmoidal networks.
Visual gesture-based robot guidance with a modular neural system
Authors: Enno Littmann, Andrea Drees, Helge Ritter
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: We report on the development of the modular neural system  "SEE(cid:173) EAGLE"  for  the  visual  guidance  of robot  pick-and-place  actions.  Several  neural  networks  are  integrated  to  a  single system  that vi(cid:173) sually  recognizes  human hand  pointing gestures  from  stereo  pairs  of color  video  images.  The output of the  hand recognition stage is  processed  by  a  set  of color-sensitive  neural  networks  to  determine  the cartesian location of the target object that is  referenced  by the  pointing gesture.  Finally, this information is  used  to guide a robot  to grab  the  target  object  and  put  it  at  another  location  that  can  be specified  by a second pointing gesture.  The accuracy of the cur(cid:173) rent  system allows to identify the location of the referenced  target  object to an accuracy of 1 cm  in  a  workspace  area of 50x50 cm.  In  our  current  environment,  this  is  sufficient  to  pick  and  place  arbi(cid:173) trarily positioned target objects within the workspace.  The system  consists  of neural  networks  that  perform  the  tasks  of image seg(cid:173) mentation, estimation of hand  location, estimation of 3D-pointing  direction, object recognition, and necessary  coordinate transforms.  Drawing heavily on the use of learning algorithms, the functions of  all  network modules were  created from  data examples only.
Independent Component Analysis of Electroencephalographic Data
Authors: Scott Makeig, Anthony J. Bell, Tzyy-Ping Jung, Terrence J. Sejnowski
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: Because of the distance between the skull and brain and their differ(cid:173) ent resistivities,  electroencephalographic (EEG) data collected from  any  point  on  the  human scalp  includes  activity  generated  within  a  large  brain area.  This spatial smearing of EEG  data by  volume  conduction  does  not involve significant time delays,  however,  sug(cid:173) gesting that the Independent  Component  Analysis (ICA) algorithm  of Bell and Sejnowski [1]  is suitable for  performing blind source sep(cid:173) aration on EEG data.  The ICA algorithm separates the problem of  source  identification from  that of source  localization.  First  results  of applying the ICA  algorithm to  EEG and  event-related potential  (ERP)  data  collected  during  a  sustained  auditory  detection  task  show:  (1)  ICA training is insensitive to different  random seeds.  (2)  ICA may be used to segregate obvious artifactual EEG components  (line and muscle noise, eye movements) from other sources.  (3) ICA  is  capable of isolating overlapping  EEG  phenomena,  including  al(cid:173) pha and theta bursts and spatially-separable ERP components, to  separate  ICA  channels.  (4)  N onstationarities  in  EEG  and  behav(cid:173) ioral state can be  tracked using ICA  via changes  in  the  amount of  residual  correlation  between  ICA-filtered  output channels. 
146 
S. MAKEIG, A. l . BELL, T.-P. lUNG, T. l. SEJNOWSKI
Competence Acquisition in an Autonomous Mobile Robot using Hardware Neural Techniques
Authors: Geoffrey B. Jackson, Alan F. Murray
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: In  this  paper  we  examine  the  practical  use  of  hardware  neural  networks  in  an  autonomous  mobile  robot.  We  have  developed  a  hardware  neural  system  based  around  a  custom  VLSI  chip,  EP(cid:173) SILON  III,  designed  specifically  for  embedded  hardware  neural  applications.  We  present  here  a  demonstration  application  of an  autonomous mobile robot that highlights the flexibility of this sys(cid:173) tem.  This robot gains basic mobility competence in very few  train(cid:173) ing epochs  using  an  "instinct-rule"  training methodology.
Gaussian Processes for Regression
Authors: Christopher K. I. Williams, Carl Edward Rasmussen
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: The Bayesian analysis of neural networks is difficult because a sim(cid:173) ple  prior  over  weights  implies  a  complex  prior  distribution  over  functions .  In this paper we investigate the use of Gaussian process  priors  over  functions,  which  permit  the  predictive  Bayesian  anal(cid:173) ysis  for  fixed  values  of hyperparameters  to  be  carried  out exactly  using matrix operations.  Two methods, using optimization and av(cid:173) eraging (via Hybrid Monte Carlo) over  hyperparameters have been  tested  on  a  number  of  challenging  problems  and  have  produced  excellent  results.
Reorganisation of Somatosensory Cortex after Tactile Training
Authors: Rasmus S. Petersen, John G. Taylor
Published in: Advances in Neural Information Processing Systems 8  (NIPS 1995) (Main Conference Track), 1995

Abstract: Topographic maps in  primary areas of mammalian cerebral cortex reor(cid:173) ganise as  a result of behavioural  training.  The nature  of this  reorgani(cid:173) sation  seems  consistent  with  the  behaviour  of competitive  neural  net(cid:173) works,  as  has  been  demonstrated  in  the  past  by  computer  simulation.  We model tactile training on the hand representation in primate somato(cid:173) sensory  cortex,  using  the  Neural  Field  Theory  of Amari  and  his  col(cid:173) leagues.  Expressions for  changes in  both receptive field  size  and  mag(cid:173) nification factor are derived,  which  are consistent with  owl  monkey ex(cid:173) periments and make a prediction which goes beyond them.