mtybilly/Instruct2DS huggingface dataset:
The Power of Amnesia
Authors: Yoram Singer, Naftali Tishby Dana Ron
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We  propose  a  learning  algorithm  for  a  variable  memory  length  Markov  process.  Human  communication,  whether  given  as  text,  handwriting,  or  speech,  has  multi  characteristic  time  scales.  On  short  scales  it  is  characterized  mostly  by  the  dynamics  that  gen(cid:173) erate  the  process,  whereas  on  large  scales,  more syntactic  and  se(cid:173) mantic information is  carried.  For  that  reason  the  conventionally  used  fixed  memory  Markov  models  cannot  capture  effectively  the  complexity of such structures.  On the other hand using long mem(cid:173) ory  models uniformly is  not  practical  even  for  as  short memory as  four.  The  algorithm  we  propose  is  based  on  minimizing  the  sta(cid:173) tistical  prediction  error  by  extending  the  memory, or state length,  adaptively, until the total prediction error  is  sufficiently small.  We  demonstrate the algorithm by learning the structure of natural En(cid:173) glish  text  and applying the learned  model to the correction  of cor(cid:173) rupted  text .  Using  less  than  3000  states  the  model's  performance  is  far  superior  to  that  of fixed  memory models  with  similar num(cid:173) ber  of states.  We  also  show  how  the  algorithm  can  be  applied  to  intergenic  E.  coli DNA  base  prediction  with  results  comparable to  HMM  based methods.
On the Non-Existence of a Universal Learning Algorithm for Recurrent Neural Networks
Authors: Herbert Wiklicky
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We prove that the so called "loading problem" for (recurrent) neural net(cid:173) works is unsolvable.  This extends several results which already demon(cid:173) strated that training and related design problems for neural networks are  (at least)  NP-complete.  Our result also implies  that it is impossible  to  find  or to  formulate  a  universal training  algorithm,  which  for  any  neu(cid:173) ral  network  architecture could determine a  correct set of weights.  For  the  simple proof of this,  we  will just show  that the loading  problem  is  equivalent to "Hilbert's tenth problem" which is known to be unsolvable. 
1  THE NEURAL NETWORK MODEL 
It seems that there are relatively few  commonly accepted general formal  definitions of the  notion  of a  "neural  network".  Although  our results also  hold  if based on  other formal  definitions  we  will  try  to  stay here  very  close  to  the original  setting  in  which Judd's NP  completeness result was given [Judd,  1990].  But in  contrast to  [Judd,  1990]  we will  deal  here with simple recurrent networks instead of feed forward architectures. 
Our networks  are constructed from  three different types  of units:  .E-units compute just  the sum of all incoming signals; for II -units the activation (node) function is given by the  product of the incoming signals; and with E)-units - depending if the input signal is smaller  or larger than  a certain  threshold parameter fl  - the output is  zero or one.  Our units are  connected or linked by real weighted connections and operate synchronously. 
Note that we could base our construction also just on  one general  type of units,  namely  what usually is called .E II -units.  Furthermore, one could replace the II -units in the below
Constructive Learning Using Internal Representation Conflicts
Authors: Marwan A. Jabri Laurens R. Leerink
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We  present an algorithm for  the training of feedforward  and recur(cid:173) rent  neural  networks.  It  detects  internal  representation  conflicts  and  uses  these  conflicts  in  a  constructive manner  to  add new  neu(cid:173) rons to the network .  The advantages are twofold:  (1)  starting with  a  small network  neurons  are  only  allocated  when  required;  (2)  by  detecting  and resolving  internal conflicts at an  early stage learning  time is  reduced.  Empirical results on two real-world problems sub(cid:173) stantiate  the  faster  learning  speed;  when  applied  to  the  training  of a  recurrent  network  on  a  well  researched  sequence  recognition  task (the Reber grammar), training times are significantly less than  previously  reported .
A Local Algorithm to Learn Trajectories with Stochastic Neural Networks
Authors: Javier R. Movellan
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: This paper presents  a simple algorithm to learn trajectories with a  continuous  time,  continuous  activation  version  of the  Boltzmann  machine.  The  algorithm  takes  advantage  of intrinsic  Brownian  noise in the network to easily compute gradients using entirely local  computations.  The  algorithm may  be  ideal  for  parallel hardware  implementations. 
This  paper  presents  a  learning  algorithm to train  continuous stochastic  networks  to  respond  with  desired  trajectories  in  the  output  units  to  environmental  input  trajectories.  This is a task, with potential applications to a variety of problems such  as stochastic modeling of neural processes,  artificial motor control,  and continuous  speech  recognition .  For  example,  in  a  continuous speech  recognition  problem,  the  input trajectory  may be  a  sequence  of fast  Fourier  transform coefficients,  and  the  output  a  likely  trajectory  of phonemic  patterns  corresponding  to  the  input.  This  paper was based on recent  work on diffusion networks by Movellan and McClelland  (in  press)  and  by  recent  papers  by  Apolloni  and  de  Falco  (1991)  and  Neal  (1992)  on  asymmetric  Boltzmann  machines.  The  learning  algorithm  can  be  seen  as  a  generalization of their work  to  the stochastic  diffusion  case  and  to  the problem of  learning continuous stochastic trajectories. 
Diffusion networks are governed by the standard connectionist differential equations  plus  an  independent  additive  noise  component.  The resulting  process  is  governed 
·Pa.rt of this work was done  while a.t  Ca.rnegie  Mellon  University.
Use of Bad Training Data for Better Predictions
Authors: Alan Lapedes Tal Grossman
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We show how randomly scrambling the output classes of various  fractions of the training data may be used to improve predictive  accuracy of a classification algorithm. We present a method for  calculating the "noise sensitivity signature" of a learning algorithm  which is based on scrambling the output classes. This signature can  be used to indicate a good match between the complexity of the  classifier and the complexity of the data. Use of noise sensitivity  signatures is distinctly different from other schemes to avoid over(cid:173) training, such as cross-validation, which uses only part of the train(cid:173) ing data, or various penalty functions, which are not data-adaptive.  Noise sensitivity signature methods use all of the training data and  are manifestly data-adaptive and non-parametric. They are well  suited for situations with limited training data.
Non-Intrusive Gaze Tracking Using Artificial Neural Networks
Authors: Dean Pomerleau Shumeet Baluja
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We have developed an  artificial  neural  network based gaze  tracking system  which  can  be  customized  to  individual  users.  Unlike  other gaze  trackers,  which  normally  require the  user to  wear cumbersome headgear,  or to use a  chin  rest  to  ensure  head  immobility,  our  system  is  entirely  non-intrusive.  Currently, the best intrusive gaze tracking  systems are  accurate  to  approxi(cid:173) mately  0.75  degrees.  In  our experiments,  we  have  been  able  to  achieve an  accuracy  of  1.5  degrees,  while  allowing  head  mobility.  In  this  paper  we  present an empirical analysis of the performance of a large number of artifi(cid:173) cial neural network architectures for this task.
Learning Curves: Asymptotic Values and Rate of Convergence
Authors: L. D. Jackel, Sara A. Solla, Vladimir Vapnik, John S. Denker Corinna Cortes
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Training classifiers on large databases is computationally demand(cid:173) ing. It is desirable to develop efficient procedures for a reliable  prediction of a classifier's suitability for implementing a given task,  so that resources can be assigned to the most promising candidates  or freed for exploring new classifier candidates. We propose such  a practical and principled predictive method. Practical because it  avoids the costly procedure of training poor classifiers on the whole  training set, and principled because of its theoretical foundation.  The effectiveness of the proposed procedure is demonstrated for  both single- and multi-layer networks. 
1
How to Describe Neuronal Activity: Spikes, Rates, or Assemblies?
Authors: J. Leo van Hemmen Wulfram Gerstner
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: What  is  the  'correct'  theoretical  description  of neuronal  activity?  The  analysis  of the  dynamics  of a  globally  connected  network  of  spiking neurons  (the Spike Response  Model)  shows  that a  descrip(cid:173) tion  by  mean firing  rates  is  possible  only if active  neurons  fire  in(cid:173) coherently.  If firing  occurs  coherently  or  with spatio-temporal cor(cid:173) relations,  the  spike  structure of the neural  code  becomes  relevant.  Alternatively, neurons can  be gathered into local or distributed en(cid:173) sembles or 'assemblies'.  A description  based on the mean ensemble  activity is,  in principle, possible but the interaction  between  differ(cid:173) ent assemblies becomes highly nonlinear.  A description with spikes  should  therefore  be  preferred.
Digital Boltzmann VLSI for constraint satisfaction and learning
Authors: Ming-Tak Leung, Kan Boonyanit, Kong Kritayakirana, James B. Burg, Gregory J. Wolff, Tokahiro Watanabe, Edward Schwartz, David G. Stork, Allen M. Peterson Michael Murray
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We built a high-speed, digital mean-field Boltzmann chip and SBus  board for general problems in constraint satjsfaction and learning.  Each chip has 32 neural processors and 4 weight update processors,  supporting an arbitrary topology of up to 160 functional neurons.  On-chip learning is at a theoretical maximum rate of 3.5 x 108 con(cid:173) nection updates/sec; recall is 12000 patterns/sec for typical condi(cid:173) tions. The chip's high speed is due to parallel computation of inner  products, limited (but adequate) precision for weights and activa(cid:173) tions (5 bits), fast clock (125 MHz), and several design insights.
Feature Densities are Required for Computing Feature Correspondences
Authors: Subutai Ahmad
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: The feature correspondence problem is a classic hurdle in visual  object-recognition concerned with determining the correct mapping  between the features measured from the image and the features ex(cid:173) pected by the model. In this paper we show that determining good  correspondences requires information about the joint probability  density over the image features. We propose "likelihood based  correspondence matching" as a general principle for selecting op(cid:173) timal correspondences. The approach is applicable to non-rigid  models, allows nonlinear perspective transformations, and can op(cid:173) timally deal with occlusions and missing features. Experiments  with rigid and non-rigid 3D hand gesture recognition support the  theory. The likelihood based techniques show almost no decrease  in classification performance when compared to performance with  perfect correspondence knowledge.
Emergence of Global Structure from Local Associations
Authors: Paul W. Munro Thea B. Ghiselli-Crippa
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: A variant of the encoder architecture, where units at the input and out(cid:173) put layers represent nodes on a graph. is applied to the task of mapping  locations to  sets of neighboring locations. The degree to  which  the re(cid:173) suIting  internal (i.e.  hidden  unit)  representations reflect global proper(cid:173) ties of the environment depends upon several parameters of the learning  procedure. Architectural bottlenecks. noise. and incremental learning of  landmarks are shown to  be important factors in  maintaining topograph(cid:173) ic relationships at a global scale.
Lower Boundaries of Motoneuron Desynchronization via Renshaw Interneurons
Authors: Robert E. Druzinsky, C. J. Heckman, W. Zev Rymer Mitchell Gil Maltenfort
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Using a quasi-realistic model of the feedback inhibition ofmotoneurons  (MNs) by Renshaw cells, we show that weak inhibition is sufficient to  maximally  desynchronize MNs,  with  negligible  effects  on  total  MN  activity.  MN synchrony can produce a 20 - 30 Hz peak in  the force  power spectrum, which may cause instability in feedback loops.
Signature Verification using a "Siamese" Time Delay Neural Network
Authors: Isabelle Guyon, Yann LeCun, Eduard Säckinger, Roopak Shah Jane Bromley
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: This paper describes an algorithm for verification of signatures  written on a pen-input tablet. The algorithm is based on a novel,  artificial neural network, called a "Siamese" neural network. This  network consists of two identical sub-networks joined at their out(cid:173) puts. During training the two sub-networks extract features from  two signatures, while the joining neuron measures the distance be(cid:173) tween the two feature vectors. Verification consists of comparing an  extracted feature vector ~ith a stored feature vector for the signer.  Signatures closer to this stored representation than a chosen thresh(cid:173) old are accepted, all other signatures are rejected as forgeries.
Connectionism for Music and Audition
Authors: Andreas S. Weigend
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: This  workshop  explored  machine  learning  approaches  to  3  topics:  (1)  finding structure in  music (analysis,  continuation, and comple(cid:173) tion  of an  unfinished  piece),  (2)  modeling  perception  of time  (ex(cid:173) traction  of musical meter,  explanation of human  data on  timing),  and  (3)  interpolation in  timbre space. 
In  recent  years,  NIPS  has  heard  neural  networks  generate  tunes  and  harmonize  chorales.  With  a  large  amount of music  becoming available  in  computer  readable  form,  real  data  can  be  used  to  train  connectionist  models.  At  the  beginning  of  this  workshop,  Andreas  Weigend focused  on  architectures  to  capture  structure  on  multiple  time scales.  J.  S.  Bach's  last  (unfinished)  fugue  from  Die  Kunst  der  Fuge  served  as  an  example (Dirst  &  Weigend,  1994).1  The prediction  approach  to  continuation  and  completion,  as  well  as  to  modeling expectations,  can  be  charac(cid:173) terized  by  the  question  "What's  next?".  Moving  to  time  as  the  primary  medium  of musical communication, the inquiry in music perception  and cognition shifted to  the question  "When next?" .  In other words, so far we  have considered patterns  in  time.  They assume prior iden(cid:173) tification and subsequent  processing  of events.  Bob Port, coming from  the speech  community, considered patterns of time, discussing timing in linguistic polyrhythms  (e.g.,  hot  cup  of tea).  He  also  drew  parallels  between  timing in  Japanese  language  and timing in  music,  supporting the hypothesis  that perceptional  rhythms entrain  attentional rhythms.  As  a mechanism for entrainment, Devin McAuley presented  adaptive oscillators:  the oscillators adapt  their  frequencies  such  that  their  "firing"  coincides  with the  beat of the music (McAuley,  1994). 
As  the  beat  can  be  viewed  as  entrainment  of  an  individual  oscillator,  the  meter  can  be  viewed  as  entrainment of multiple oscillators.  Ed  Large described  human  perception of metrical structure in analogy to two pendulum clocks that synchronize  their  motions  by  hanging  on  the  same  wall.  An  advantage  of these  entrainment 
1 This fugue is  available  via anonymous ftp from  ftp. santafe. edu as data set F. dat of 
the Santa  Fe  Time Series  Analysis  and  Prediction  Competition.
Central and Pairwise Data Clustering by Competitive Neural Networks
Authors: Thomas Hofmann Joachim Buhmann
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Data clustering amounts to a combinatorial optimization problem to re(cid:173) duce the complexity of a data representation and to increase its precision.  Central and pairwise data clustering are studied in the maximum en(cid:173) tropy framework. For central clustering we derive a set of reestimation  equations and a minimization procedure which yields an optimal num(cid:173) ber of clusters, their centers and their cluster probabilities. A meanfield  approximation for pairwise clustering is used to estimate assignment  probabilities. A se1fconsistent solution to multidimensional scaling and  pairwise clustering is derived which yields an optimal embedding and  clustering of data points in a d-dimensional Euclidian space.
A Connectionist Model of the Owl's Sound Localization System
Authors: David E. Rumelhart, Eric I. Knudsen Daniel J. Rosen
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: ,,"'e do not have a good understanding of how  theoretical principles  of learning are realized in neural systems.  To address this problem  we  built a  computational model of development in the owl's sound  localization  system.  The  structure  of  the  model  is  drawn  from  known  experimental data while  the  learning principles  come from  recent  work  in  the  field  of  brain  style  computation.  The  model  accounts  for  numerous  properties  of the  owl's  sound  localization  system,  makes specific  and  testable  predictions  for  future  experi(cid:173) ments, and provides a  theory of the  developmental process.
Identifying Fault-Prone Software Modules Using Feed-Forward Networks: A Case Study
Authors: N. Karunanithi
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Functional complexity of a software module can be measured in  terms of static complexity metrics of the program text. Classify(cid:173) ing software modules, based on their static complexity measures,  into different fault-prone categories is a difficult problem in soft(cid:173) ware engineering. This research investigates the applicability of  neural network classifiers for identifying fault-prone software mod(cid:173) ules using a data set from a commercial software system. A pre(cid:173) liminary empirical comparison is performed between a minimum  distance based Gaussian classifier, a perceptron classifier and a  multilayer layer feed-forward network classifier constructed using  a modified Cascade-Correlation algorithm. The modified version  of the Cascade-Correlation algorithm constrains the growth of the  network size by incorporating a cross-validation check during the  output layer training phase. Our preliminary results suggest that  a multilayer feed-forward network can be used as a tool for iden(cid:173) tifying fault-prone software modules early during the development  cycle. Other issues such as representation of software metrics and  selection of a proper training samples are also discussed.
Event-Driven Simulation of Networks of Spiking Neurons
Authors: Lloyd Watts
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: A fast event-driven software simulator has been  developed for sim(cid:173) ulating large networks of spiking neurons and synapses.  The prim(cid:173) itive  network  elements  are  designed  to exhibit  biologically  realis(cid:173) tic  behaviors,  such  as  spiking,  refractoriness,  adaptation,  axonal  delays,  summation of post-synaptic  current  pulses,  and tonic  cur(cid:173) rent  inputs.  The efficient  event-driven  representation  allows  large  networks  to  be  simulated in a  fraction  of the  time  that  would  be  required  for  a  full  compartmental-model simulation.  Correspond(cid:173) ing analog CMOS  VLSI  circuit  primitives have  been  designed  and  characterized,  so  that  large-scale  circuits  may  be simulated  prior  to fabrication.
Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman Network
Authors: Todd Troyer, Frank Eeckman Bill Baird
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We show how an "Elman" network architecture, constructed from  recurrently connected oscillatory associative memory network mod(cid:173) ules, can employ selective "attentional" control of synchronization  to direct the flow of communication and computation within the  architecture to solve a grammatical inference problem.  Previously we have shown how the discrete time "Elman" network  algorithm can be implemented in a network completely described  by continuous ordinary differential equations. The time steps (ma(cid:173) chine cycles) of the system are implemented by rhythmic variation  (clocking) of a bifurcation parameter. In this architecture, oscilla(cid:173) tion amplitude codes the information content or activity of a mod(cid:173) ule (unit), whereas phase and frequency are used to "softwire" the  network. Only synchronized modules communicate by exchang(cid:173) ing amplitude information; the activity of non-resonating modules  contributes incoherent crosstalk noise.  Attentional control is modeled as a special subset of the hidden  modules with ouputs which affect the resonant frequencies of other  hidden modules. They control synchrony among the other mod(cid:173) ules and direct the flow of computation (attention) to effect transi(cid:173) tions between two subgraphs of a thirteen state automaton which  the system emulates to generate a Reber grammar. The internal  crosstalk noise is used to drive the required random transitions of  the automaton.
Bayesian Self-Organization
Authors: Stelios M. Smirnakis, Lei Xu Alan L. Yuille
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Recent  work  by  Becker  and  Hinton  (Becker  and  Hinton,  1992)  shows  a  promising  mechanism,  based  on  maximizing  mutual  in(cid:173) formation  assuming spatial coherence,  by which  a  system  can self(cid:173) organize itself to learn visual abilities such  as  binocular stereo.  We  introduce  a  more general  criterion,  based  on  Bayesian  probability  theory,  and  thereby  demonstrate  a  connection  to  Bayesian  theo(cid:173) ries  of visual  perception  and  to  other  organization  principles  for  early  vision  (Atick  and  Redlich,  1990).  Methods  for  implementa(cid:173) tion using variants of stochastic learning are  described  and, for  the  special  case  of linear filtering,  we  derive  an analytic expression for  the output.
The "Softmax" Nonlinearity: Derivation Using Statistical Mechanics and Useful Properties as a Multiterminal Analog Circuit Element
Authors: J. L. Wyatt Jr. I. M. Elfadel
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We use mean-field theory methods from Statistical Mechanics to  derive the "softmax" nonlinearity from the discontinuous winner(cid:173) take-all (WTA) mapping. We give two simple ways of implementing  "soft max" as a multiterminal network element. One of these has a  number of important network-theoretic properties. It is a recipro(cid:173) cal, passive, incrementally passive, nonlinear, resistive multitermi(cid:173) nal element with a content function having the form of information(cid:173) theoretic entropy. These properties should enable one to use this  element in nonlinear RC networks with such other reciprocal el(cid:173) ements as resistive fuses and constraint boxes to implement very  high speed analog optimization algorithms using a minimum of  hardware.
Robust Parameter Estimation and Model Selection for Neural Network Regression
Authors: Yong Liu
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: In  this  paper,  it  is  shown  that  the  conventional  back-propagation  (BPP)  algorithm for  neural  network  regression  is  robust  to  lever(cid:173) ages  (data  with  :n  corrupted),  but  not  to  outliers  (data  with  y  corrupted).  A  robust  model is  to  model the  error  as  a  mixture of  normal distribution.  The influence function for  this mixture model  is calculated and the condition for  the model to be robust to outliers  is  given.  EM  algorithm [5]  is  used  to estimate the parameter.  The  usefulness  of model selection  criteria is  also discussed.  Illustrative  simulations are  performed.
Computational Elements of the Adaptive Controller of the Human Arm
Authors: Ferdinando A. Mussa-Ivaldi Reza Shadmehr
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We consider the problem of how the CNS  learns to control dynam(cid:173) ics  of a  mechanical system.  By using a  paradigm where  a subject's  hand  interacts  with  a  virtual  mechanical  environment,  we  show  that learning  control is  via composition  of a  model  of the imposed  dynamics.  Some  properties  of  the  computational  elements  with  which the CNS  composes  this model  are  inferred  through  the gen(cid:173) eralization  capabilities of the  subject outside  the training data.
Transition Point Dynamic Programming
Authors: Peter D. Lawrence Kenneth M. Buckland
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Transition  point  dynamic  programming  (TPDP)  is  a  memory(cid:173) based,  reinforcement  learning,  direct  dynamic  programming  ap(cid:173) proach  to  adaptive  optimal  control  that  can  reduce  the  learning  time  and  memory  usage  required  for  the  control  of  continuous  stochastic  dynamic  systems.  TPDP  does  so  by  determining  an  ideal  set  of transition  points  (TPs)  which  specify only  the  control  action  changes  necessary  for  optimal  control.  TPDP  converges  to  an ideal TP set by using a variation of Q-Iearning to assess the mer(cid:173) its of adding,  swapping and  removing TPs from  states throughout  the  state  space.  When  applied  to  a  race  track  problem,  TPDP  learned  the optimal  control  policy  much  sooner  than  conventional  Q-Iearning,  and was  able  to do so using  less  memory.
Dopaminergic Neuromodulation Brings a Dynamical Plasticity to the Retina
Authors: Jean-François Vibert Eric Boussard
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: The fovea of a mammal retina was simulated with its detailed bio(cid:173) logical  properties  to  study  the local  preprocessing  of images.  The  direct  visual  pathway (photoreceptors,  bipolar and  ganglion cells)  and the  horizontal units,  as  well  as the  D-amacrine cells  were sim(cid:173) ulated.  The  computer  program simulated the  analog  non-spiking  transmission between photoreceptor and bipolar cells,  and between  bipolar and ganglion cells,  as well as the gap-junctions between hor(cid:173) izontal cells,  and the release  of dopamine by  D-amacrine cells  and  its  diffusion  in  the  extra-cellular  space.  A  64 x 64  photoreceptors  retina,  containing  16,448  units,  was  carried  out.  This  retina  dis(cid:173) played  contour  extraction  with  a  Mach  effect,  and  adaptation  to  brightness.  The simulation showed that the dopaminergic amacrine  cells  were  necessary  to  ensure  adaptation to  local brightness.
Monte Carlo Matrix Inversion and Reinforcement Learning
Authors: Michael Duff Andrew Barto
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We describe the relationship between certain reinforcement learn(cid:173) ing (RL) methods based on dynamic programming (DP) and a class  of unorthodox Monte Carlo methods for solving systems of linear  equations proposed in the 1950's. These methods recast the solu(cid:173) tion of the linear system as the expected value of a statistic suitably  defined over sample paths of a Markov chain. The significance of  our observations lies in arguments (Curtiss, 1954) that these Monte  Carlo methods scale better with respect to state-space size than do  standard, iterative techniques for solving systems of linear equa(cid:173) tions. This analysis also establishes convergence rate estimates.  Because methods used in RL systems for approximating the evalu(cid:173) ation function of a fixed control policy also approximate solutions  to systems of linear equations, the connection to these Monte Carlo  methods establishes that algorithms very similar to TD algorithms  (Sutton, 1988) are asymptotically more efficient in a precise sense  than other methods for evaluating policies. Further, all DP-based  RL methods have some of the properties of these Monte Carlo al(cid:173) gorithms, which suggests that although RL is often perceived to  be slow, for sufficiently large problems, it may in fact be more ef(cid:173) ficient than other known classes of methods capable of producing  the same results.
Globally Trained Handwritten Word Recognizer using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models
Authors: Yann LeCun, Donnie Henderson Yoshua Bengio
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We introduce a new approach for on-line recognition of handwrit(cid:173) ten words written in unconstrained mixed style. The preprocessor  performs a word-level normalization by fitting a model of the word  structure using the EM algorithm. Words are then coded into low  resolution "annotated images" where each pixel contains informa(cid:173) tion about trajectory direction and curvature. The recognizer is a  convolution network which can be spatially replicated. From the  network output, a hidden Markov model produces word scores. The  entire system is globally trained to minimize word-level errors.
Robust Reinforcement Learning in Motion Planning
Authors: Andrew G. Barto, Roderic Grupen, Christopher Connolly Satinder P. Singh
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: While exploring to find better solutions, an agent performing on(cid:173) line reinforcement learning (RL) can perform worse than is accept(cid:173) able. In some cases, exploration might have unsafe, or even catas(cid:173) trophic, results, often modeled in terms of reaching 'failure' states  of the agent's environment. This paper presents a method that uses  domain knowledge to reduce the number of failures during explo(cid:173) ration. This method formulates the set of actions from which the  RL agent composes a control policy to ensure that exploration is  conducted in a policy space that excludes most of the unacceptable  policies. The resulting action set has a more abstract relationship  to the task being solved than is common in many applications of  RL. Although the cost of this added safety is that learning may  result in a suboptimal solution, we argue that this is an appropri(cid:173) ate tradeoff in many problems. We illustrate this method in the  domain of motion planning. 
"'This work was done while the first author was finishing his Ph.D in computer science 
at the University of Massachusetts, Amherst.
Optimal Stopping and Effective Machine Complexity in Learning
Authors: Santosh S. Venkatesh, J. Stephen Judd Changfeng Wang
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We  study  tltt'  problem  of  when  to  stop  If'arning  a  class  of  feedforward  networks  - networks  with  linear  outputs  I1PUrOIl  and  fixed  input  weights  - when  they  are  trained  with  a  gradient  descent  algorithm  on  a  finite  number  of examples.  Under  general  regularity  conditions,  it  is  shown  that  there  a.re  in  general  three  distinct  phases in  the generalization  performance in  the learning process,  and in  particular,  the  network  has  hetter  gt'neralization  pPTformance  when  learning  is  stopped  at  a  certain  time  before til('  global  miniIl111lu  of the empirical error is  reachert.  A  notion  of effective  size  of a  machine  is  rtefil1e
Exploiting Chaos to Control the Future
Authors: Guo-Zhen Sun, Yee-Chun Lee Gary W. Flake
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Recently, Ott, Grebogi and Yorke (OGY) [6] found an effective  method to control chaotic systems to unstable fixed points by us(cid:173) ing only small control forces; however, OGY's method is based on  and limited to a linear theory and requires considerable knowledge  of the dynamics of the system to be controlled. In this paper we use  two radial basis function networks: one as a model of an unknown  plant and the other as the controller. The controller is trained  with a recurrent learning algorithm to minimize a novel objective  function such that the controller can locate an unstable fixed point  and drive the system into the fixed point with no a priori knowl(cid:173) edge of the system dynamics. Our results indicate that the neural  controller offers many advantages over OGY's technique.
Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization
Authors: M. V. Solodov O. L. Mangasarian
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: The fundamental backpropagation (BP) algorithm for training ar(cid:173) tificial neural networks is cast as a deterministic nonmonotone per(cid:173) turbed gradient method. Under certain natural assumptions, such  as the series of learning rates diverging while the series of their  squares converging, it is established that every accumulation point  of the online BP iterates is a stationary point of the BP error func(cid:173) tion. The results presented cover serial and parallel online BP,  modified BP with a momentum term, and BP with weight decay.
Fool's Gold: Extracting Finite State Machines from Recurrent Network Dynamics
Authors: John F. Kolen
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Several recurrent networks have been proposed as representations for the  task of formal language learning. After training a recurrent network rec(cid:173) ognize a formal  language or predict the next symbol of a sequence, the  next logical step is to  understand the information processing carried out  by  the  network.  Some researchers have  begun  to  extracting  finite  state  machines from the internal state trajectories of their recurrent networks.  This  paper describes  how  sensitivity  to  initial  conditions  and  discrete  measurements can trick these extraction methods to return illusory finite  state descriptions.
Two Iterative Algorithms for Computing the Singular Value Decomposition from Input/Output Samples
Authors: Terence D. Sanger
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: The Singular Value  Decomposition (SVD)  is  an important tool for  linear  algebra and  can  be  used  to invert  or  approximate matrices.  Although  many  authors  use  "SVD"  synonymously  with  "Eigen(cid:173) vector  Decomposition"  or  "Principal  Components  Transform",  it  is  important  to  realize  that  these  other  methods  apply  only  to  symmetric  matrices,  while  the  SVD  can  be  applied  to  arbitrary  nonsquare matrices.  This property is  important for  applications to  signal transmission and  control.  I propose two new  algorithms for iterative computation of the SVD  given  only  sample  inputs  and  outputs  from  a  matrix.  Although  there  currently exist  many algorithms for  Eigenvector  Decomposi(cid:173) tion  (Sanger  1989,  for  example),  these  are  the  first  true  sample(cid:173) based  SVD algorithms.
Efficient Computation of Complex Distance Metrics Using Hierarchical Filtering
Authors: Patrice Y. Simard
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: By their very nature, memory based algorithms such as KNN or  Parzen windows require a computationally expensive search of a  large database of prototypes. In this paper we optimize the search(cid:173) ing process for tangent distance (Simard, LeCun and Denker, 1993)  to improve speed performance. The closest prototypes are found  by recursively searching included subset.s of the database using dis(cid:173) tances of increasing complexit.y. This is done by using a hierarchy  of tangent distances (increasing the Humber of tangent. vectors from  o to its maximum) and multiresolution (using wavelets). At each  stage, a confidence level of the classification is computed. If the  confidence is high enough, the c.omputation of more complex dis(cid:173) tances is avoided. The resulting algorithm applied to character  recognition is close to t.hree orders of magnitude faster than com(cid:173) puting the full tangent dist.ance on every prot.ot.ypes .
Statistics of Natural Images: Scaling in the Woods
Authors: William Bialek Daniel L. Ruderman
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: In  order  to  best  understand  a  visual  system  one  should  attempt  to characterize  the natural images it processes.  We  gather  images  from the woods and find that these scenes possess an ensemble scale  invariance.  Further,  they  are  highly  non-Gaussian,  and  this  non(cid:173) Gaussian  character  cannot  be  removed  through local  linear filter(cid:173) ing.  We find  that including a simple  "gain control"  nonlinearity in  the filtering  process  makes the filter  output quite Gaussian, mean(cid:173) ing information is maximized at fixed  channel variance.  Finally, we  use  the measured power spectrum to place  an upper bound on the  information conveyed about natural scenes by an array of receptors.
GDS: Gradient Descent Generation of Symbolic Classification Rules
Authors: Reinhard Blasig
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Imagine you have designed a neural network that successfully learns  a complex classification task.  What are the relevant input features  the classifier relies on and how  are these features  combined to pro(cid:173) duce  the  classification  decisions?  There  are  applications  where  a  deeper  insight  into  the  structure  of an  adaptive  system  and  thus  into the underlying classification problem may well be as important  as  the  system's  performance  characteristics,  e.g.  in  economics  or  medicine.  GDSi  is  a  backpropagation-based  training scheme  that  produces networks  transformable into an equivalent and concise  set  of IF-THEN rules.  This is achieved by  imposing penalty terms  on the network parameters that adapt the network to the expressive  power of this class of rules.  Thus during training we simultaneously  minimize classification  and  transformation error.  Some real-world  tasks  demonstrate the viability of our approach.
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach
Authors: Michael L. Littman Justin A. Boyan
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: This  paper  describes  the  Q-routing  algorithm for  packet  routing,  in  which  a  reinforcement  learning  module  is  embedded  into  each  node  of a  switching  network.  Only  local  communication  is  used  by each node to keep  accurate statistics on which routing decisions  lead  to  minimal  delivery  times.  In  simple  experiments  involving  a  36-node,  irregularly  connected  network,  Q-routing  proves  supe(cid:173) rior  to  a  nonadaptive  algorithm  based  on  precomputed  shortest  paths  and  is  able  to route  efficiently  even  when  critical  aspects  of  the  simulation,  such  as  the  network  load,  are  allowed  to  vary  dy(cid:173) namically.  The  paper  concludes  with  a  discussion  of the  tradeoff  between  discovering  shortcuts  and  maintaining stable policies.
A Massively-Parallel SIMD Processor for Neural Network and Machine Vision Applications
Authors: W. Thomas Miller III Michael A. Glover
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: This paper describes the MM32k, a massively-parallel SIMD com(cid:173) puter which is easy to program, high in performance, low in cost  and effective for implementing highly parallel neural network ar(cid:173) chitectures. The MM32k has 32768 bit serial processing elements,  each of which has 512 bits of memory, and all of which are inter(cid:173) connected by a switching network. The entire system resides on  a single PC-AT compatible card. It is programmed from the host  computer using a C++ language class library which abstracts the  parallel processor in terms of fast arithmetic operators for vectors  of variable precision integers.
How to Choose an Activation Function
Authors: C. A.. Micchelli H. N. Mhaskar
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We study the complexity problem in artificial feedforward neural networks  designed to approximate real valued functions of several real variables; i.e.,  we estimate the number of neurons in a network required to ensure a given  degree of approximation to every function in a given function class. We  indicate how to construct networks with the indicated number of neurons  evaluating standard activation functions. Our general theorem shows that  the smoother the activation function, the better the rate of approximation.
Efficient Simulation of Biological Neural Networks on Massively Parallel Supercomputers with Hypercube Architecture
Authors: Dean Brettle Ernst Niebur
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: We  present  a  neural  network  simulation  which  we  implemented  on  the  massively  parallel  Connection  Machine  2.  In  contrast  to  previous  work,  this simulator is  based on biologically realistic neu(cid:173) rons  with nontrivial single-cell  dynamics,  high  connectivity with a  structure  modelled in  agreement  with  biological data,  and preser(cid:173) vation of the temporal dynamics of spike interactions.  We simulate  neural networks of 16,384 neurons  coupled by about 1000 synapses  per neuron, and estimate the performance for  much larger systems.  Communication between neurons  is  identified as the computation(cid:173) ally  most  demanding task  and we  present a  novel method to over(cid:173) come this bottleneck.  The simulator has already been used to study  the primary visual system of the  cat.
Convergence of Stochastic Iterative Dynamic Programming Algorithms
Authors: Michael I. Jordan, Satinder P. Singh Tommi Jaakkola
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Increasing attention has recently  been  paid to algorithms based on  dynamic programming (DP) due  to the suitability of DP for  learn(cid:173) ing  problems  involving control.  In  stochastic  environments  where  the  system  being  controlled  is  only  incompletely  known,  however,  a  unifying theoretical  account  of these  methods has  been  missing.  In  this  paper  we  relate  DP-based  learning  algorithms to  the  pow(cid:173) erful  techniques of stochastic approximation via a  new convergence  theorem,  enabling us  to establish  a  class  of convergent  algorithms  to which  both TD(")  and Q-Iearning belong.
High Performance Neural Net Simulation on a Multiprocessor System with "Intelligent" Communication
Authors: Michael Kocheisen, Anton Gunzinger Urs A. Müller
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: The performance requirements in experimental research on arti(cid:173) ficial neural nets often exceed the capability of workstations and  PCs by a great amount. But speed is not the only requirement.  Flexibility and implementation time for new algorithms are usually  of equal importance. This paper describes the simulation of neural  nets on the MUSIC parallel supercomputer, a system that shows a  good balance between the three issues and therefore made many  research projects possible that were unthinkable before. (MUSIC  stands for Multiprocessor System with Intelligent Communication) 
1 Overview of the MUSIC System 
The goal of the MUSIC project was to build a fast parallel system and to use it in  real-world applications like neural net simulations, image processing or simulations  in chemistry and physics [1, 2]. The system should be flexible, simple to program  and the realization time should be short enough to not have an obsolete system by  the time it is finished. Therefore, the fastest available standard components were  used. The key idea of the architecture is to support the collection and redistribution  of complete data blocks by a simple, efficient and autonomously working commu(cid:173) nication network realized in hardware. Instead of considering where to send data  and where from to receive data, each processing element determines which part of  a (virtual) data block it has produced and which other part of the same data block  it wants to receive for the continuation of the algorithm.