mtybilly/Instruct2DS huggingface dataset:
Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems
Authors: Tommi Jaakkola, Satinder P. Singh, Michael I. Jordan
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Increasing  attention has  been  paid to reinforcement  learning  algo(cid:173) rithms  in  recent  years,  partly  due  to  successes  in  the  theoretical  analysis  of their  behavior in  Markov  environments.  If the  Markov  assumption  is  removed,  however,  neither  generally  the  algorithms  nor  the  analyses  continue  to  be  usable.  We  propose  and  analyze  a  new  learning  algorithm  to  solve  a  certain  class  of  non-Markov  decision  problems.  Our  algorithm  applies  to  problems  in  which  the  environment  is  Markov,  but  the  learner  has  restricted  access  to  state  information.  The  algorithm involves  a  Monte-Carlo  pol(cid:173) icy evaluation combined with a  policy improvement method that is  similar to  that  of Markov  decision  problems  and  is  guaranteed  to  converge to a local maximum.  The algorithm operates in the space  of stochastic  policies,  a  space  which  can  yield  a  policy  that  per(cid:173) forms  considerably  better  than any deterministic policy.  Although  the  space  of stochastic  policies  is  continuous-even  for  a  discrete  action space-our algorithm is computationally tractable. 
346 
Tommi  Jaakkola,  Satinder P.  Singh,  Michaell.  Jordan
Limits on Learning Machine Accuracy Imposed by Data Quality
Authors: Corinna Cortes, L. D. Jackel, Wan-Ping Chiang
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Random  errors  and  insufficiencies  in  databases  limit  the  perfor(cid:173) mance  of any classifier  trained  from  and applied  to the  database.  In this paper we  propose a method to estimate the limiting perfor(cid:173) mance of classifiers imposed by the database.  We demonstrate this  technique  on  the  task  of predicting  failure  in  telecommunication  paths.
Interference in Learning Internal Models of Inverse Dynamics in Humans
Authors: Reza Shadmehr, Tom Brashers-Krug, Ferdinando A. Mussa-Ivaldi
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Experiments were  performed  to  reveal  some of the  computational  properties  of the  human  motor  memory  system.  We  show  that  as  humans  practice  reaching  movements while  interacting  with  a  novel  mechanical environment, they learn an  internal model of the  inverse dynamics of that environment.  Subjects show recall of this  model  at testing  sessions  24  hours  after  the  initial practice.  The  representation  of the internal model in  memory is  such  that there  is  interference  when  there  is  an  attempt  to  learn  a  new  inverse  dynamics  map  immediately after  an  anticorrelated  mapping  was  learned.  We  suggest  that  this  interference  is  an  indication  that  the same computational elements  used  to encode  the first  inverse  dynamics  map  are  being  used  to  learn  the  second  mapping.  We  predict  that this leads to a forgetting  of the initially learned skill.
Using a Saliency Map for Active Spatial Selective Attention: Implementation & Initial Results
Authors: Shumeet Baluja, Dean A. Pomerleau
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: In many vision based tasks, the ability to focus attention on the important  portions of a scene is crucial for good performance on the tasks. In this paper  we present a simple method of achieving spatial selective attention through  the use of a saliency map. The saliency map indicates which regions of the  input retina are important for performing the task. The saliency map is cre(cid:173) ated through predictive auto-encoding. The performance of this method is  demonstrated on two simple tasks which have multiple very strong distract(cid:173) ing features in the input retina. Architectural extensions and application  directions for this model are presented.
Pairwise Neural Network Classifiers with Probabilistic Outputs
Authors: David Price, Stefan Knerr, Léon Personnaz, Gérard Dreyfus
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Multi-class  classification  problems  can  be  efficiently  solved  by  partitioning the  original problem into  sub-problems  involving  only  two  classes:  for each pair of classes,  a (potentially small)  neural  network is  trained  using  only  the  data  of  these  two  classes.  We  show  how  to  combine the outputs of the  two-class  neural  networks in order to  obtain  posterior probabilities for  the class decisions.  The resulting probabilistic  pairwise classifier is part of a handwriting recognition  system which  is  currently applied to check reading. We present results on real world data  bases and show that, from a practical point of view, these results compare  favorably to other neural network approaches.
Real-Time Control of a Tokamak Plasma Using Neural Networks
Authors: Chris M. Bishop, Paul S. Haynes, Mike E U Smith, Tom N. Todd, David L. Trotman, Colin G. Windsor
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: This  paper  presents  results  from  the  first  use  of neural  networks  for  the real-time feedback  control  of high  temperature plasmas in  a  tokamak fusion  experiment.  The tokamak is  currently  the  prin(cid:173) cipal  experimental  device  for  research  into  the  magnetic  confine(cid:173) ment  approach  to  controlled  fusion.  In  the  tokamak,  hydrogen  plasmas,  at  temperatures  of  up  to  100  Million  K,  are  confined  by  strong  magnetic  fields.  Accurate  control  of the  position  and  shape  of the plasma boundary  requires  real-time feedback  control  of the magnetic field  structure on  a  time-scale of a  few  tens of mi(cid:173) croseconds.  Software simulations have demonstrated that a  neural  network  approach  can  give  significantly  better  performance  than  the linear technique currently  used  on  most tokamak experiments.  The practical  application of the neural  network  approach  requires  high-speed  hardware,  for  which  a  fully  parallel  implementation of  the  multilayer perceptron,  using  a  hybrid  of digital and  analogue  technology,  has been  developed.
An Actor/Critic Algorithm that is Equivalent to Q-Learning
Authors: Robert H. Crites, Andrew G. Barto
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We prove the convergence of an actor/critic algorithm that is equiv(cid:173) alent  to Q-Iearning by construction.  Its equivalence is  achieved by  encoding Q-values within  the  policy  and value function  of the  ac(cid:173) tor and critic.  The resultant actor/critic algorithm is novel in two  ways:  it  updates the critic  only  when  the  most  probable  action  is  executed from  any given state, and it  rewards  the actor using cri(cid:173) teria that depend on the relative probability of the action that was  executed.
Template-Based Algorithms for Connectionist Rule Extraction
Authors: Jay A. Alexander, Michael Mozer
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Casting  neural  network  weights  in  symbolic  terms  is  crucial  for  interpreting  and explaining  the  behavior of a network.  Additionally,  in  some  domains,  a  symbolic  description  may  lead  to  more  robust  generalization.  We  present  a  principled  approach  to  symbolic  rule  extraction  based  on  the  notion  of  weight  templates,  parameterized  regions of weight space corresponding to specific symbolic expressions.  With  an  appropriate  choice  of representation,  we  show  how  template  parameters  may  be  efficiently  identified  and  instantiated  to  yield  the  optimal match to a unit's actual weights. Depending on the requirements  of  the  application  domain,  our  method  can  accommodate  arbitrary  disjunctions  and  conjunctions  with  O(k)  complexity,  simple  n-of-m  expressions with O( k!) complexity, or a more general class of recursive  n-of-m  expressions  with  O(k!)  complexity,  where  k  is  the  number  of  inputs  to  a  unit.  Our method  of rule  extraction  offers  several  benefits  over alternative approaches in the literature, and simulation results on  a  variety of problems demonstrate its effectiveness.
Reinforcement Learning with Soft State Aggregation
Authors: Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: It is  widely  accepted  that the use  of more compact representations  than lookup tables is crucial to scaling reinforcement learning (RL)  algorithms to real-world problems.  Unfortunately almost all of the  theory  of reinforcement  learning assumes  lookup table representa(cid:173) tions.  In  this  paper  we  address  the  pressing  issue  of  combining  function  approximation and RL,  and present  1)  a function approx(cid:173) imator  based  on  a  simple extension  to  state  aggregation  (a  com(cid:173) monly  used  form  of  compact  representation),  namely  soft  state  aggregation,  2)  a  theory  of convergence for  RL  with arbitrary, but  fixed,  soft  state  aggregation,  3)  a  novel  intuitive  understanding of  the effect  of state aggregation on online RL, and 4)  a new heuristic  adaptive  state aggregation algorithm that finds  improved compact  representations  by  exploiting the  non-discrete  nature  of soft  state  aggregation.  Preliminary empirical results  are  also  presented.
Advantage Updating Applied to a Differential Game
Authors: Mance E. Harmon, Leemon C. Baird III, A. Harry Klopf
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: An application of reinforcement learning to a linear-quadratic, differential  game  is  presented.  The reinforcement learning  system  uses  a  recently  developed  algorithm,  the residual gradient form  of advantage updating.  The  game  is a  Markov  Decision  Process  (MDP)  with continuous  time,  states, and actions, linear dynamics, and a quadratic cost function.  The  game consists of two players, a missile and a plane;  the missile pursues  the plane and  the plane evades the  missile.  The reinforcement learning  algorithm for optimal control is modified for differential games in order to  find the minimax point, rather than  the maximum.  Simulation results are  compared  to  the  optimal  solution,  demonstrating  that  the  simulated  reinforcement learning  system  converges  to  the optimal  answer.  The  performance of both the residual gradient and non-residual gradient forms  of advantage updating and Q-learning are compared.  The results show that  advantage  updating  converges faster  than  Q-learning in  all  simulations.  The results also show advantage updating converges regardless of the time  step duration; Q-learning is  unable to converge as the  time step duration  ~rows small. 
U.S .A.F.  Academy,  2354  Fairchild  Dr.  Suite 6K4l,  USAFA,  CO  80840-6234 
354 
Mance E.  Hannon,  Leemon C.  Baird ll/, A.  Harry Klopf 
1  ADVANTAGE  UPDATING 
The advantage updating algorithm (Baird, 1993) is a reinforcement learning algorithm in  which  two types  of information  are stored.  For each  state x, the  value V(x)  is  stored,  representing an estimate of the total discounted return expected when starting in  state x  and performing optimal actions.  For each state x and action u, the advantage, A(x,u), is  stored, representing  an  estimate of the  degree  to  which  the expected  total  discounted  reinforcement  is  increased  by  performing  action  u  rather  than  the  action  currently  considered best.  The optimal value function V* (x) represents the true value of each state.  The optimal advantage function A * (x,u)  will be zero if u is the optimal action (because u  confers no advantage relative to itself) and A * (x,u) will be negative for any suboptimal u  (because a suboptimal action has a negative advantage relative to  the best action).  The  optimal advantage function A * can be defined in terms of the optimal value function v*: 
A(x,u) = ~[RN(X,U)- V(x)+ rNV*(x')]
An Analog Neural Network Inspired by Fractal Block Coding
Authors: Fernando J. Pineda, Andreas G. Andreou
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We consider the problem of decoding block coded data, using a physical  dynamical system. We sketch out a decompression algorithm for fractal  block  codes  and  then  show  how  to  implement  a  recurrent  neural  network  using  physically  simple  but highly-nonlinear,  analog  circuit  models of neurons and synapses. The nonlinear system has many fixed  points, but we have at our disposal a procedure to choose the parameters  in  such a way  that only one solution, the desired solution, is stable. As  a  partial  proof of the  concept,  we  present experimental  data  from  a  small system a 16-neuron analog CMOS chip fabricated in a 2m analog  p-well process. This chip operates in the subthreshold regime and,  for  each choice of parameters, converges to a unique stable state. Each state  exhibits a qualitatively fractal shape.
Phase-Space Learning
Authors: Fu-Sheng Tsung, Garrison W. Cottrell
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Existing recurrent  net learning algorithms are inadequate.  We  in(cid:173) troduce the conceptual framework of viewing recurrent  training as  matching vector fields of dynamical systems in phase space.  Phase(cid:173) space  reconstruction  techniques  make  the  hidden  states  explicit,  reducing  temporal  learning  to  a  feed-forward  problem.  In  short,  we  propose  viewing  iterated  prediction  [LF88]  as  the  best  way  of  training  recurrent  networks  on  deterministic  signals.  Using  this  framework,  we  can  train  multiple trajectories,  insure  their  stabil(cid:173) ity,  and  design  arbitrary dynamical systems.
An experimental comparison of recurrent neural networks
Authors: Bill G. Horne, C. Lee Giles
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Many  different  discrete-time  recurrent  neural  network  architec(cid:173) tures  have  been  proposed.  However,  there  has  been  virtually  no  effort  to compare these arch:tectures experimentally.  In  this paper  we  review  and categorize many of these architectures and compare  how  they  perform on various classes  of simple problems including  grammatical inference  and nonlinear system identification.
Inferring Ground Truth from Subjective Labelling of Venus Images
Authors: Padhraic Smyth, Usama M. Fayyad, Michael C. Burl, Pietro Perona, Pierre Baldi
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: In  remote sensing  applications  "ground-truth"  data is  often  used  as  the  basis for  training  pattern  recognition  algorithms  to  gener(cid:173) ate  thematic  maps  or  to  detect  objects  of  interest.  In  practical  situations, experts may visually examine the images and provide a  subjective noisy  estimate of the  truth.  Calibrating the  reliability  and bias  of expert labellers is  a  non-trivial problem.  In this paper  we  discuss  some  of  our  recent  work  on  this  topic  in  the  context  of  detecting  small  volcanoes  in  Magellan  SAR  images  of  Venus.  Empirical results (using the Expectation-Maximization procedure)  suggest  that  accounting  for  subjective  noise  can  be  quite  signifi(cid:173) cant in terms of quantifying both human and algorithm detection  performance.
Learning Prototype Models for Tangent Distance
Authors: Trevor Hastie, Patrice Simard
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Simard,  LeCun  & Denker  (1993)  showed  that the performance of  nearest-neighbor  classification  schemes  for  handwritten  character  recognition  can  be  improved  by  incorporating  invariance  to  spe(cid:173) the  so  cific  transformations in  the  underlying  distance  metric  - called  tangent  distance.  The  resulting  classifier,  however,  can  be  prohibitively slow and memory intensive due to the large amount of  prototypes that need to be stored and used in the distance compar(cid:173) isons.  In this  paper we  develop  rich  models for  representing  large  subsets of the prototypes.  These models are either used singly per  class,  or  as  basic building blocks  in conjunction with the K-means  clustering  algorithm. 
*This work was performed while Trevor Hastie was a member of the Statistics and Data 
Analysis  Research  Group,  AT&T Bell  Laboratories,  Murray  Hill,  NJ  07974.
Diffusion of Credit in Markovian Models
Authors: Yoshua Bengio, Paolo Frasconi
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: This  paper  studies the  problem of diffusion  in  Markovian  models,  such  as  hidden  Markov  models  (HMMs)  and  how  it  makes  very  difficult the task of learning of long-term dependencies in sequences.  Using results from Markov chain theory,  we show that the problem  of diffusion is reduced if the transition probabilities approach 0 or 1.  Under  this condition, standard HMMs have very  limited modeling  capabilities,  but input/output HMMs can still perform interesting  computations.
The Ni1000: High Speed Parallel VLSI for Implementing Multilayer Perceptrons
Authors: Michael P. Perrone, Leon N. Cooper
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: In  this paper  we  present  a  new  version  of the standard  multilayer  perceptron  (MLP) algorithm for  the state-of-the-art in neural net(cid:173) work VLSI implementations:  the Intel Ni1000.  This new version of  the  MLP  uses  a fundamental property of high dimensional spaces  which  allows  the  12-norm  to  be  accurately  approximated  by  the  It -norm.  This  approach  enables  the  standard  MLP  to  utilize  the  parallel architecture of the Ni1000 to achieve on the order of 40000,  256-dimensional classifications per second. 
1  The Intel NilOOO  VLSI Chip 
The Nestor/Intel radial basis function  neural chip (Ni1000) contains the equivalent  of  1024  256-dimensional  artificial  digital  neurons  and can  perform  at  least  40000  classifications  per  second  [Sullivan, 1993].  To attain  this  great  speed,  the  Ni1000  was  designed  to  calculate  "city  block"  distances  (Le.  the  II-norm)  and  thus  to  avoid the large  number  of multiplication units that  would be required  to calculate  Euclidean dot  products in  parallel.  Each  neuron calculates the city block distance  between  its stored weights and the current  input: 
neuron  activity = L IWi  - :eil 
(1)  where  w,  is  the  neuron's stored  weight  for  the  ith  input  and  :ei  is  the  ith  input.  Thus the Nil000 is ideally suited to perform both the RCE [Reillyet al.,  1982]  and 
748 
Michael P.  Perrone.  Leon N.  Cooper 
PRCE  [Scofield et al.,  1987]  algorithms or  any of the  other  commonly used  radial  basis function  (RBF) algorithms.  However,  dot products are central in the calcula(cid:173) tions performed by most neural network algorithms (e.g.  MLP, Cascade Correlation,  etc.).  Furthermore, for  high dimensional data, the dot product becomes the compu(cid:173) tation bottleneck (i.e.  most ofthe network's time is spent calculating dot products).  If the  dot  product  can  not  be  performed in  parallel  there  will  be  little  advantage  using  the  NilOOO  for  such  algorithms.  In  this  paper,  we  address  this  problem  by  showing  that  we  can  extend  the  NilOOO  to  many of the  standard  neural  network  algorithms by  representing  the  Euclidean  dot  product  as  a  function  of Euclidean  norms and by then using a  city block norm approximation to the  Euclidean norm.  Section  2,  introduces  the  approximate dot  productj  Section  3  describes  the  City  Block  MLP  which  uses  the  approximate dot  productj  and  Section  4  presents  ex(cid:173) periments which demonstrate that the City Block  MLP performs well on the  NIST  OCR data and on human face  recognition data. 
2  Approximate  Dot  Product 
Consider  the following approximation [Perrone,  1993]: 
1
Model of a Biological Neuron as a Temporal Neural Network
Authors: Sean D. Murphy, Edward W. Kairiss
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: A biological neuron can be viewed as a device that maps a multidimen(cid:173) sional  temporal  event signal  (dendritic  postsynaptic  activations)  into a  unidimensional  temporal  event  signal  (action  potentials).  We  have  designed  a  network,  the  Spatio-Temporal  Event  Mapping  (STEM)  architecture, which can learn to perform this mapping for arbitrary bio(cid:173) physical  models  of  neurons.  Such  a  network  appropriately  trained,  called a STEM cell, can be used in place of a conventional compartmen(cid:173) tal  model  in  simulations  where only the  transfer function  is  important,  such  as  network  simulations.  The  STEM  cell  offers  advantages  over  compartmental  models in  terms of computational efficiency, analytical  tractabili1ty,  and as a framework  for  VLSI  implementations of biologi(cid:173) cal neurons.
Non-linear Prediction of Acoustic Vectors Using Hierarchical Mixtures of Experts
Authors: Steve R. Waterhouse, Anthony J. Robinson
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: In this paper we consider speech coding as a problem of speech  modelling. In particular, prediction of parameterised speech over  short time segments is performed using the Hierarchical Mixture of  Experts (HME) (Jordan & Jacobs 1994). The HME gives two ad(cid:173) vantages over traditional non-linear function approximators such  as the Multi-Layer Percept ron (MLP); a statistical understand(cid:173) ing of the operation of the predictor and provision of information  about the performance of the predictor in the form of likelihood  information and local error bars. These two issues are examined  on both toy and real world problems of regression and time series  prediction. In the speech coding context, we extend the principle  of combining local predictions via the HME to a Vector Quantiza(cid:173) tion scheme in which fixed local codebooks are combined on-line  for each observation.
The Electrotonic Transformation: a Tool for Relating Neuronal Form to Function
Authors: Nicholas T Carnevale, Kenneth Y. Tsai, Brenda J. Claiborne, Thomas H. Brown
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: The spatial distribution and time course of electrical signals in neurons  have important theoretical and practical consequences. Because it is  difficult to infer how neuronal form affects electrical signaling, we  have developed a quantitative yet intuitive approach to the analysis of  electrotonus. This approach transforms the architecture of the cell  from anatomical to electrotonic space, using the logarithm of voltage  attenuation as the distance metric. We describe the theory behind this  approach and illustrate its use.
Dynamic Modelling of Chaotic Time Series with Neural Networks
Authors: Jose C. Principe, Jyh-Ming Kuo
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: The auditory system of the barn owl contains several spatial maps.  In young barn owls raised with optical prisms over their eyes, these  auditory maps are shifted to stay in register with the visual map,  suggesting that the visual input imposes a frame of reference on  the auditory maps. However, the optic tectum, the first site of  convergence of visual with auditory information, is not the site of  plasticity for the shift of the auditory maps; the plasticity occurs  instead in the inferior colliculus, which contains an auditory map  and projects into the optic tectum. We explored a model of the owl  remapping in which a global reinforcement signal whose delivery is  controlled by visual foveation. A hebb learning rule gated by rein(cid:173) forcement learned to appropriately adjust auditory maps. In addi(cid:173) tion, reinforcement learning preferentially adjusted the weights in  the inferior colliculus, as in the owl brain, even though the weights  were allowed to change throughout the auditory system. This ob(cid:173) servation raises the possibility that the site of learning does not  have to be genetically specified, but could be determined by how  the learning procedure interacts with the network architecture. 
126 
Alexandre Pouget, Cedric Deffayet, Te"ence J. Sejnowski 
c:::======:::::» •
Learning with Product Units
Authors: Laurens R. Leerink, C. Lee Giles, Bill G. Horne, Marwan A. Jabri
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: The  TNM  staging  system  has  been  used  since  the  early  1960's  to  predict  breast  cancer  patient  outcome.  In  an  attempt  to  in(cid:173) crease  prognostic  accuracy,  many putative prognostic factors  have  been  identified.  Because  the  TNM  stage  model  can  not  accom(cid:173) modate  these  new  factors,  the  proliferation  of factors  in  breast  cancer  has  lead  to  clinical  confusion.  What  is  required  is  a  new  computerized  prognostic system  that  can test  putative prognostic  factors  and  integrate  the  predictive  factors  with  the  TNM  vari(cid:173) ables  in order to increase  prognostic  accuracy.  Using  the area un(cid:173) der  the curve of the receiver  operating characteristic,  we  compare  the  accuracy  of the  following  predictive  models  in  terms  of five  year  breast cancer-specific  survival:  pTNM staging system,  princi(cid:173) pal component analysis,  classification and regression  trees,  logistic  regression,  cascade  correlation neural network,  conjugate gradient  descent  neural,  probabilistic neural network,  and backpropagation  neural network.  Several statistical models are significantly more ac-
Efficient Methods for Dealing with Missing Data in Supervised Learning
Authors: Volker Tresp, Ralph Neuneier, Subutai Ahmad
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We present efficient algorithms for dealing with the problem of mis(cid:173) sing inputs (incomplete feature  vectors) during training and recall.  Our approach is based on the approximation of the input data dis(cid:173) tribution using  Parzen  windows.  For  recall,  we  obtain closed form  solutions for arbitrary feedforward networks.  For training, we show  how  the  backpropagation  step  for  an  incomplete  pattern  can  be  approximated by  a  weighted  averaged backpropagation step.  The  complexity  of the  solutions for  training  and  recall  is  independent  of the number of missing features.  We verify our theoretical results  using one classification and one regression  problem.
Predictive Coding with Neural Nets: Application to Text Compression
Authors: Jürgen Schmidhuber, Stefan Heil
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: To compress text files,  a neural predictor network  P  is used to ap(cid:173) proximate the conditional probability distribution of possible  "next  characters",  given  n  previous  characters.  P's outputs are fed  into  standard coding algorithms that generate short codes for characters  with  high  predicted  probability and  long  codes  for  highly  unpre(cid:173) dictable  characters.  Tested  on  short  German  newspaper  articles,  our method outperforms widely used  Lempel-Ziv algorithms (used  in  UNIX  functions such  as  "compress"  and  "gzip").
Computational Structure of coordinate transformations: A generalization study
Authors: Zoubin Ghahramani, Daniel M. Wolpert, Michael I. Jordan
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: One  of the fundamental properties  that both neural  networks  and  the  central  nervous  system share is  the  ability to learn  and gener(cid:173) alize  from examples.  While this  property  has  been  studied  exten(cid:173) sively  in  the  neural  network  literature  it  has  not  been  thoroughly  explored in human perceptual and motor learning.  We have chosen  a  coordinate  transformation  system-the  visuomotor  map  which  transforms visual coordinates into motor coordinates-to study the  generalization effects  of learning new  input-output  pairs.  Using  a  paradigm of computer  controlled  altered  visual  feedback,  we  have  studied  the  generalization  of the  visuomotor  map  subsequent  to  both local  and context-dependent  remappings.  A  local  remapping  of one  or  two  input-output  pairs  induced  a  significant  global,  yet  decaying,  change  in  the  visuomotor map, suggesting  a  representa(cid:173) tion for  the  map composed of units  with large functional  receptive  fields.  Our study of context-dependent  remappings indicated that  a  single  point  in  visual  space  can  be  mapped to  two different  fin(cid:173) ger  locations  depending  on  a  context  variable-the starting  point  of  the  movement.  Furthermore,  as  the  context  is  varied  there  is  a  gradual  shift  between  the  two  remappings,  consistent  with  two  visuomotor  modules  being  learned  and  gated  smoothly  with  the  context.
Recognizing Handwritten Digits Using Mixtures of Linear Models
Authors: Geoffrey E. Hinton, Michael Revow, Peter Dayan
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We construct a mixture of locally linear generative models of a col(cid:173) lection of pixel-based images of digits, and use them for  recogni(cid:173) tion. Different models of a given digit are used to capture different  styles of writing, and new images are classified by evaluating their  log-likelihoods under each model. We use an EM-based algorithm  in which the M-step is computationally straightforward principal  components analysis (PCA).  Incorporating tangent-plane informa(cid:173) tion [12]  about expected local deformations only requires adding  tangent vectors into the sample covariance matrices for  the PCA,  and it demonstrably improves performance.
A Critical Comparison of Models for Orientation and Ocular Dominance Columns in the Striate Cortex
Authors: E. Erwin, K. Obermayer, K. Schulten
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: More than ten of the most prominent models for the structure  and for the activity dependent formation of orientation and ocu(cid:173) lar dominance columns in the striate cort(>x have been evaluated.  We implemented those models on parallel machines, we extensively  explored parameter space, and we quantitatively compared model  predictions with experimental data which were recorded optically  from macaque striate cortex.  In our contribution we present a summary of our results to date.  Briefly, we find that (i) despite apparent differences, many models  are based on similar principles and, consequently, make similar pre(cid:173) dictions, (ii) certain "pattern models" as well as the developmental  "correlation-based learning" models disagree with the experimen(cid:173) tal data, and (iii) of the models we have investigated, "competitive  Hebbian" models and the recent model of Swindale provide the  best match with experimental data. 
1 Models and Data 
The models for the formation and structure of orientation and ocular dominance  columns which we have investigated are summarized in table 1. Models fall into  two categories: "Pattern models" whose aim is to achieve a concise description of  the observed patterns and "developmental models" which are focussed on the pro-
94 
E. Erwin, K. Obermayer, K. Schulten 
Class  Pattern  Models Models
Classifying with Gaussian Mixtures and Clusters
Authors: Nanda Kambhatla, Todd K. Leen
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: In this paper, we derive classifiers which are winner-take-all (WTA)  approximations  to  a  Bayes  classifier  with  Gaussian  mixtures  for  class conditional densities.  The derived classifiers include clustering  based algorithms like LVQ  and k-Means.  We propose a  constrained  rank  Gaussian  mixtures model and derive a WTA algorithm for  it.  Our experiments with two speech classification tasks indicate that  the constrained rank model and the WTA approximations improve  the performance over the unconstrained models.
Anatomical origin and computational role of diversity in the response properties of cortical neurons
Authors: Kalanit Grill Spector, Shimon Edelman, Rafael Malach
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: The  maximization of  diversity  of neuronal  response  properties  has  been  recently  suggested  as  an  organizing  principle  for  the  formation  of such  prominent features of the functional architecture of the brain as the corti(cid:173) cal columns and the associated patchy projection patterns (Malach,  1994).  We show that (1) maximal diversity is attained when the ratio of dendritic  and  axonal  arbor  sizes  is  equal  to  one,  as  found  in  many  cortical  areas  and  across  species  (Lund  et al.,  1993;  Malach,  1994),  and  (2)  that maxi(cid:173) mization of diversity leads  to  better  performance  in  systems of receptive  fields  implementing steerable/shiftable  filters,  and  in  matching spatially  distributed  signals,  a  problem that  arises  in  many high-level  visual tasks. 
1  Anatomical substrate for  sampling diversity 
A  fundamental  feature  of  cortical  architecture  is  its  columnar  organization,  mani(cid:173) fested  in  the tendency  of neurons  with similar properties to be organized  in  columns  that run perpendicular to the cortical surface.  This organization of the cortex was ini(cid:173) tially discovered  by  physiological experiments  (Mouncastle,  1957;  Hubel  and  Wiesel,  1962),  and  subsequently  confirmed  with  the  demonstration  of histologically  defined  columns.  Tracing  experiments  have  shown  that  axonal  projections  throughout  the  cerebral  cortex tend to be organized in  vertically  aligned  clusters  or patches.  In  par(cid:173) ticular,  intrinsic  horizontal  connections  linking neighboring  cortical sites,  which  may  extend  up  to  2 - 3  mm,  have  a  striking tendency  to  arborize  selectively  in preferred  sites,  forming  distinct  axonal patches  200 - 300  J.lm  in  diameter. 
Recently,  it  has  been  observed  that  the  size  of  these  patches  matches  closely  the  average  diameter  of  individual  dendritic  arbors  of  upper-layer  pyramidal  cells 
118 
Kalanit Grill Spector,  Shimon  Edelman, Rafael Malach
Synchrony and Desynchrony in Neural Oscillator Networks
Authors: Deliang Wang, David Terman
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: An novel class of locally excitatory, globally inhibitory oscillator  networks is proposed. The model of each oscillator corresponds to a  standard relaxation oscillator with two time scales. The network  exhibits a mechanism of selective gating, whereby an oscillator  jumping up to its active phase rapidly recruits the oscillators stimulated  by the same pattern, while preventing others from jumping up. We  show analytically that with the selective gating mechanism the network  rapidly achieves both synchronization within blocks of oscillators that  are stimulated by connected regions and desynchronization between  different blocks. Computer simulations demonstrate the network's  promising ability for segmenting multiple input patterns in real time.  This model lays a physical foundation for the oscillatory correlation  theory of feature binding, and may provide an effective computational  framework for scene segmentation and figure/ground segregation.
A Computational Model of Prefrontal Cortex Function
Authors: Todd S. Braver, Jonathan D. Cohen, David Servan-Schreiber
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Accumulating  data  from  neurophysiology  and  neuropsychology  have  suggested  two  information processing roles  for  prefrontal cor(cid:173) tex  (PFC):  1)  short-term  active  memory;  and  2)  inhibition.  We  present  a  new  behavioral  task  and  a  computational model  which  were  developed  in  parallel.  The  task  was  developed  to probe  both  of these  prefrontal  functions  simultaneously,  and  produces  a  rich  set  of behavioral  data  that  act  as  constraints  on  the  model.  The  model is  implemented in continuous-time, thus providing a  natural  framework  in  which  to study  the  temporal dynamics of processing  in the task.  We show how the model can be used to examine the be(cid:173) havioral  consequences of neuromodulation in PFC . Specifically, we  use  the model to make novel and testable predictions regarding the  behavioral performance of schizophrenics,  who  are hypothesized  to  suffer  from  reduced  dopaminergic tone  in  this brain  area.
Combining Estimators Using Non-Constant Weighting Functions
Authors: Volker Tresp, Michiaki Taniguchi
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: This paper  discusses  the  linearly  weighted combination of estima(cid:173) tors  in which  the weighting functions  are  dependent  on the input .  We  show  that  the  weighting  functions  can  be  derived  either  by  evaluating  the  input  dependent  variance  of each  estimator  or  by  estimating how  likely  it is  that a given  estimator has  seen  data in  the  region  of the input space  close  to the input pattern.  The lat(cid:173) ter  solution  is  closely  related  to  the  mixture of experts  approach  and we  show how  learning rules  for  the mixture of experts  can  be  derived from the theory  about learning with missing features.  The  presented  approaches  are  modular  since  the  weighting  functions  can  easily  be  modified  (no  retraining)  if more  estimators  are  ad(cid:173) ded.  Furthermore, it is  easy  to incorporate estimators which  were  not  derived from  data such as  expert systems or  algorithms.
Stochastic Dynamics of Three-State Neural Networks
Authors: Toru Ohira, Jack D. Cowan
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We  present  here  an  analysis  of  the  stochastic  neurodynamics  of  a  neural  network  composed  of  three-state  neurons  described  by  a  master equation.  An  outer-product representation of the mas(cid:173) ter  equation  is  employed.  In  this  representation,  an  extension  of  the  analysis  from  two  to  three-state neurons is  easily  performed.  We  apply  this  formalism  with  approximation  schemes  to  a  sim(cid:173) ple three-state network and compare the results with Monte Carlo  simulations.
On the Computational Utility of Consciousness
Authors: Donald W. Mathis, Michael Mozer
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We  propose  a  computational  framework  for  understanding  and  modeling  human consciousness.  This  framework  integrates  many  existing theoretical perspectives,  yet is sufficiently concrete to allow  simulation experiments.  We do not attempt to explain qualia (sub(cid:173) jective  experience),  but  instead  ask  what  differences  exist  within  the cognitive information processing  system  when a  person is  con(cid:173) scious  of mentally-represented  information versus  when that infor(cid:173) mation is  unconscious.  The central idea we explore is  that the con(cid:173) tents  of consciousness  correspond  to  temporally  persistent  states  in a  network of computational modules.  Three simulations are de(cid:173) scribed  illustrating  that  the  behavior  of  persistent  states  in  the  models  corresponds  roughly  to  the  behavior  of  conscious  states  people  experience when  performing similar tasks.  Our simulations  show that periodic  settling to persistent  (i.e.,  conscious)  states im(cid:173) proves  performance  by cleaning  up inaccuracies  and noise,  forcing  decisions,  and helping  keep the system on track  toward a  solution.
Ocular Dominance and Patterned Lateral Connections in a Self-Organizing Model of the Primary Visual Cortex
Authors: Joseph Sirosh, Risto Miikkulainen
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: A neural network model for the self-organization of ocular dominance and  lateral connections from binocular input is presented.  The self-organizing  process results in a network where (1) afferent weights of each neuron or(cid:173) ganize into smooth hill-shaped receptive fields primarily on one of the reti(cid:173) nas, (2) neurons with common eye preference form connected, intertwined  patches, and (3) lateral connections primarily link regions of the same eye  preference.  Similar self-organization of cortical  structures has  been  ob(cid:173) served  experimentally in strabismic kittens.  The model  shows how  pat(cid:173) terned lateral connections in  the cortex may  develop based on correlated  activity and explains why lateral connection patterns follow receptive field  properties such as ocular dominance.
Effects of Noise on Convergence and Generalization in Recurrent Networks
Authors: Kam Jim, Bill G. Horne, C. Lee Giles
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: We introduce and study methods of inserting synaptic noise into  dynamically-driven recurrent neural networks and show that ap(cid:173) plying a controlled amount of noise during training may improve  convergence and generalization. In addition, we analyze the effects  of each noise parameter (additive vs. multiplicative, cumulative vs.  non-cumulative, per time step vs. per string) and predict that best  overall performance can be achieved by injecting additive noise at  each time step. Extensive simulations on learning the dual parity  grammar from temporal strings substantiate these predictions.
An Integrated Architecture of Adaptive Neural Network Control for Dynamic Systems
Authors: Ke Liu, Robert L. Tokar, Brain D. McVey
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: In  this  study,  an  integrated  neural  network  control  architecture  for  nonlinear  dynamic  systems  is  presented.  Most of the recent emphasis in  the neural network control field has no error feedback as the  control  input,  which  rises  the  lack  of adaptation  problem.  The  integrated  architecture  in  this  paper  combines  feed  forward  control  and error  feedback  adaptive  control  using  neural  networks.  The  paper  reveals the  different  internal  functionality  of these  two  kinds of neural  network  controllers  for  certain  input  styles,  e.g.,  state  feedback  and error feedback.  With  error  feedback,  neural  network  controllers  learn  the  slopes  or  the  gains  with  respect  to  the  error  feedback,  producing  an  error  driven  adaptive  control  systems.  The  results  demonstrate  that  the  two  kinds  of control  scheme  can  be  combined  to  realize  their individual  advantages.  Testing  with disturbances added to  the  plant  shows  good  tracking  and adaptation with the integrated neural control architecture.
Implementation of Neural Hardware with the Neural VLSI of URAN in Applications with Reduced Representations
Authors: Il Song Han, Ki-Chul Kim, Hwang-Soo Lee
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: implement  Korean 
This  paper  describes  a  way  of  neural  hardware  implementation  with  the  analog-digital  mixed  mode  neural  chip.  The  full  custom  neural  VLSI  of  Universally  Reconstructible  Artificial  Neural  network (URAN)  is  used  system.  A  to  multi-layer  perceptron  with  is  trained  successfully  under  the  limited  accuracy  in  computations.  The  network  with  a  large  frame  input  layer  is  tested  to  recognize  spoken  korean  words  at  a  forward  retrieval.  Multichip  hardware  module  is  suggested  with  eight  chips  or  more  for  the  extended  performance  and  capacity.
Estimating Conditional Probability Densities for Periodic Variables
Authors: Chris M. Bishop, Claire Legleye
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Most of the common techniques for estimating conditional prob(cid:173) ability densities are inappropriate for applications involving peri(cid:173) odic variables. In this paper we introduce three novel techniques  for tackling such problems, and investigate their performance us(cid:173) ing synthetic data. We then apply these techniques to the problem  of extracting the distribution of wind vector directions from radar  scatterometer data gathered by a remote-sensing satellite.
Analysis of Unstandardized Contributions in Cross Connected Networks
Authors: Thomas R. Shultz, Yuriko Oshima-Takane, Yoshio Takane
Published in: Advances in Neural Information Processing Systems 7  (NIPS 1994) (Main Conference Track), 1994

Abstract: Understanding  knowledge  representations  in  neural  nets  has  been  a  difficult  problem.  Principal  components  analysis  (PCA)  of  contributions (products of sending activations and connection weights)  has yielded valuable insights into knowledge representations, but much  of this work has focused on the correlation matrix of contributions. The  present work  shows  that analyzing  the  variance-covariance matrix  of  contributions yields more valid  insights by taking account of weights.