mtybilly/Instruct2DS huggingface dataset:
Distributional Population Codes and Multiple Motion Models
Authors: Richard S. Zemel, Peter Dayan
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Most theoretical and empirical studies of population codes make  the assumption that underlying neuronal activities is a unique and  unambiguous value of an encoded quantity. However, population  activities can contain additional information about such things as  multiple values of or uncertainty about the quantity. We have pre(cid:173) viously suggested a method to recover extra information by treat(cid:173) ing the activities  of the population of cells  as coding for  a com(cid:173) plete distribution over the coded quantity rather than just a single  value.  We  now  show how  this approach bears on psychophys(cid:173) ical and neurophysiological studies of population codes for mo(cid:173) tion direction in tasks involving transparent motion stimuli.  We  show that, unlike standard approaches, it is able to recover mul(cid:173) tiple motions from population responses, and also that its output  is consistent with both correct and erroneous human performance  on psychophysical tasks. 
A  population code can be defined  as a  set of units  whose  activities  collectively  encode some underlying variable (or variables).  The standard view is that popu(cid:173) lation codes are useful for accurately encoding the underlying variable when the  individual units are noisy.  Current statistical approaches to interpreting popula(cid:173) tion activity reflect this view, in that they determine the optimal single value that  explains the observed activity pattern given a particular model of the noise (and  possibly a loss function).  In our work, we have pursued an alternative hypothesis, that the population en(cid:173) codes  additional information  about  the  underlying  variable,  including  multiple  values and uncertainty.  The Distributional Population Coding (DPC) framework  finds the best probability distribution across values that fits the population activity  (Zemel, Dayan, & Pouget, 1998).  The DPC framework is appealing since it makes clear how extra information can  be conveyed in a population code.  In this paper, we use it to address a particu-
Distributional Population  Codes and Multiple Motion Models
Using Collective Intelligence to Route Internet Traffic
Authors: David Wolpert, Kagan Tumer, Jeremy Frank
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: A  COllective INtelligence  (COIN)  is  a  set of interacting reinforce(cid:173) ment  learning  (RL)  algorithms  designed  in  an  automated fashion  so that their collective behavior optimizes a global utility function.  We summarize the theory of COINs, then present experiments us(cid:173) ing that theory to design COINs to control internet traffic routing.  These experiments  indicate that COINs  outperform all previously  investigated RL-based, shortest path routing algorithms.
Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation
Authors: Aapo Hyvärinen, Patrik O. Hoyer, Erkki Oja
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Sparse  coding  is  a  method  for  finding  a  representation  of data in  which  each  of the  components of the representation is  only  rarely  significantly  active.  Such  a  representation is  closely  related  to re(cid:173) dundancy reduction and independent component analysis,  and has  some  neurophysiological  plausibility.  In  this  paper,  we  show  how  sparse coding can be used for denoising.  Using maximum likelihood  estimation of nongaussian variables corrupted by gaussian noise, we  show  how  to  apply a  shrinkage nonlinearity on  the components  of  sparse coding so  as  to reduce noise.  Furthermore,  we  show  how  to  choose the optimal sparse coding basis for  denoising.  Our method  is  closely  related  to  the  method  of wavelet  shrinkage,  but  has  the  important benefit over wavelet methods that both the features and  the shrinkage parameters are estimated directly from  the data.
Finite-Dimensional Approximation of Gaussian Processes
Authors: Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Gaussian process (GP)  prediction suffers from  O(n3)  scaling with the  data set size n.  By using a finite-dimensional basis to approximate the  GP predictor,  the computational complexity  can be reduced.  We  de(cid:173) rive optimal finite-dimensional  predictors under a  number of assump(cid:173) tions,  and show  the superiority of these predictors over the  Projected  Bayes  Regression method  (which  is  asymptotically optimal).  We  also  show  how  to  calculate  the  minimal  model  size  for  a  given  n.  The  calculations are backed up  by numerical experiments.
Controlling the Complexity of HMM Systems by Regularization
Authors: Christoph Neukirchen, Gerhard Rigoll
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: This paper introduces a method for regularization ofHMM systems that  avoids parameter overfitting caused by insufficient training data.  Regu(cid:173) larization is  done by augmenting the  EM  training method by a penalty  term  that  favors  simple  and  smooth HMM  systems.  The penalty  term  is  constructed as  a mixture model of negative exponential distributions  that is assumed to generate the state dependent emission probabilities of  the HMMs.  This new method is the successful transfer of a well known  regularization approach in neural networks to the HMM domain and can  be interpreted as a generalization of traditional state-tying for HMM sys(cid:173) tems.  The effect of regularization is demonstrated for continuous speech  recognition tasks by improving overfitted triphone models and by speaker  adaptation with limited training data.
Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts
Authors: Amy McGovern, J. Eliot B. Moss
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: In  1986, Tanner and Mead [1] implemented an interesting constraint sat(cid:173) isfaction  circuit  for  global  motion  sensing  in  a VLSI.  We  report  here  a  new  and  improved a VLSI implementation that provides smooth optical  flow as well as global motion in a two dimensional visual field.  The com(cid:173) putation of optical flow  is  an ill-posed problem, which expresses itself as  the aperture problem.  However, the optical flow  can be estimated by the  use of regularization methods, in  which additional constraints are intro(cid:173) duced in  terms of a global energy functional that must be minimized . We  show how the algorithmic constraints of Hom and Schunck [2]  on com(cid:173) puting smooth optical flow can be mapped onto the physical constraints  of an equivalent electronic network.
Learning to Estimate Scenes from Images
Authors: William T. Freeman, Egon C. Pasztor
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  seek  the  scene  interpretation  that  best  explains  image  data.  For example,  we  may want to infer the projected velocities  (scene)  which  best  explain  two  consecutive  image  frames  (image).  From  synthetic data , we  model the relationship between image and scene  patches, and between a scene patch and neighboring scene patches.  Given' a  new  image,  we  propagate likelihoods in  a  Markov network  (ignoring  the  effect  of loops)  to  infer  the  underlying  scene.  This  yields  an  efficient  method  to form  low-level  scene  interpretations.  We  demonstrate the technique for  motion analysis  and estimating  high  resolution images from  low-resolution ones.
Facial Memory Is Kernel Density Estimation (Almost)
Authors: Matthew N. Dailey, Garrison W. Cottrell, Thomas A. Busey
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  compare the  ability  of three exemplar-based memory models,  each  using  three  different  face  stimulus  representations,  to  account  for  the  probability a human subject responded "old" in an old/new facial mem(cid:173) ory experiment.  The models are  1) the  Generalized Context Model, 2)  SimSample,  a  probabilistic  sampling  model,  and  3)  MMOM,  a  novel  model related to kernel density estimation that explicitly encodes stim(cid:173) ulus  distinctiveness.  The representations  are  1)  positions of stimuli  in  MDS "face space," 2) projections of test faces onto the  "eigenfaces" of  the study set, and 3) a representation based on response to a grid of Gabor  filter jets.  Of the 9 model/representation combinations, only the distinc(cid:173) tiveness model in  MDS  space predicts the observed "morph familiarity  inversion" effect, in  which the subjects'  false alarm rate for morphs be(cid:173) tween similar faces  is higher than their hit rate for many of the  studied  faces.  This evidence is consistent with the hypothesis that human mem(cid:173) ory for faces is a kernel density estimation task, with the caveat that dis(cid:173) tinctive faces require larger kernels than do typical faces.
VLSI Implementation of Motion Centroid Localization for Autonomous Navigation
Authors: Ralph Etienne-Cummings, Viktor Gruev, Mohammed Abdel Ghani
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: A  circuit  for  fast,  compact and  low-power focal-plane  motion  centroid  localization  is  presented.  This  chip,  which  uses  mixed  signal  CMOS  components  to  implement  photodetection,  edge  detection,  ON-set  detection  and  centroid  localization,  models  the  retina  and  superior  colliculus.  The  centroid  localization  circuit  uses  time-windowed  asynchronously  triggered  row  and  column  address  events  and  two  linear  resistive  grids  to  provide  the  analog  coordinates  of the  motion  centroid.  This  VLSI  chip  is  used  to  realize  fast  lightweight  autonavigating  vehicles.  The  obstacle  avoiding  line-following  algorithm is  discussed.
Discontinuous Recall Transitions Induced by Competition Between Short- and Long-Range Interactions in Recurrent Networks
Authors: N. S. Skantzos, C. F. Beckmann, Anthony C. C. Coolen
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  present exact analytical  equilibrium solutions  for  a  class  of recur(cid:173) rent neural network models,  with  both sequential and parallel neuronal  dynamics,  in  which  there  is  a  tunable  competition  between  nearest(cid:173) neighbour  and  long-range  synaptic  interactions.  This  competition  is  found  to induce novel coexistence phenomena as  well  as  discontinuous  transitions between pattern recall states, 2-cycles and non-recall states.
An Integrated Vision Sensor for the Computation of Optical Flow Singular Points
Authors: Charles M. Higgins, Christof Koch
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: A robust, integrative algorithm is presented for computing the position of  the focus  of expansion or axis of rotation (the singular point)  in optical  flow  fields  such  as  those  generated  by  self-motion.  Measurements  are  shown of a fully parallel CMOS analog VLSI motion sensor array which  computes the direction of local motion (sign of optical flow) at each pixel  and can directly implement this algorithm.  The flow  field  singular point  is  computed in real time  with a power consumption of less than 2 m W.  Computation of the singular point for  more general flow  fields  requires  measures of field  expansion and  rotation,  which  it  is  shown can also  be  computed in real-time hardware, again using only the sign of the optical  flow  field.  These measures, along with the location of the singular point,  provide robust real-time self-motion information for the visual guidance  of a moving platform such as a robot.
Robust, Efficient, Globally-Optimized Reinforcement Learning with the Parti-Game Algorithm
Authors: Mohammad A. Al-Ansari, Ronald J. Williams
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Parti-game (Moore 1994a; Moore 1994b; Moore and Atkeson  1995) is a  reinforcement learning (RL) algorithm that has a lot of promise in over(cid:173) coming the curse of dimensionality that can plague RL algorithms when  applied to high-dimensional problems.  In  this paper we  introduce mod(cid:173) ifications to  the algorithm that further improve its performance and ro(cid:173) bustness. In addition, while parti-game solutions can be improved locally  by standard local path-improvement techniques, we introduce an add-on  algorithm in  the same spirit  as  parti-game that instead tries  to  improve  solutions in a non-local manner.
A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory
Authors: Nobuo Suematsu, Akira Hayashi
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  describe  a  Reinforcement Learning  algorithm  for  partially  observ(cid:173) able environments using short-term memory, which  we call BLHT. Since  BLHT learns a stochastic model  based on Bayesian Learning, the over(cid:173) fitting  problem  is  reasonably  solved.  Moreover,  BLHT has  an  efficient  implementation. This paper shows that the model learned by BLHT con(cid:173) verges  to  one  which  provides the  most accurate  predictions of percepts  and rewards, given short-term memory.
Maximum-Likelihood Continuity Mapping (MALCOM): An Alternative to HMMs
Authors: David A. Nix, John E. Hogden
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We describe Maximum-Likelihood Continuity Mapping (MALCOM), an  alternative to  hidden Markov models (HMMs) for processing sequence  data such as speech.  While HMMs have a discrete "hidden" space con(cid:173) strained by  a fixed  finite-automaton architecture, MALCOM has a con(cid:173) tinuous hidden space-a continuity map-that is  constrained only  by  a  smoothness requirement on paths through the space.  MALCOM fits  into  the same probabilistic framework for speech recognition as  HMMs, but  it represents  a  more  realistic  model  of the  speech  production  process.  To  evaluate the extent to which MALCOM captures speech production  information, we  generated continuous speech continuity maps  for three  speakers  and  used  the  paths  through  them  to  predict measured  speech  articulator data.  The median  correlation  between  the MALCOM paths  obtained from  only the  speech  acoustics  and  articulator measurements  was 0.77 on an  independent test set not used  to train MALCOM or the  predictor.  This  unsupervised  model  achieved  correlations  over speak(cid:173) ers and articulators only 0.02 to 0.15 lower than those obtained using an  analogous supervised method which used articulatory measurements as  well as acoustics ..
Semiparametric Support Vector and Linear Programming Machines
Authors: Alex J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Semiparametric models  are  useful  tools  in the case  where  domain  knowledge exists about the function to be estimated or emphasis is  put onto understandability of the  model.  We  extend two learning  algorithms  - Support  Vector  machines  and  Linear  Programming  machines  to  this  case  and  give  experimental  results  for  SV  ma(cid:173) chines.
Regularizing AdaBoost
Authors: Gunnar Rätsch, Takashi Onoda, Klaus R. Müller
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Boosting methods  maximize  a  hard  classification  margin  and  are  known as powerful techniques that do not exhibit overfitting for low  noise  cases.  Also for noisy data boosting will  try to enforce a  hard  margin and thereby give  too much  weight  to outliers,  which  then  leads to the dilemma of non-smooth fits  and overfitting.  Therefore  we  propose three algorithms to allow for  soft margin classification  by introducing regularization with slack variables into the boosting  concept:  (1)  AdaBoostreg  and  regularized  versions  of  (2)  linear  and  (3)  quadratic programming AdaBoost.  Experiments show  the  usefulness of the proposed algorithms in comparison to another soft  margin classifier:  the support vector machine. 
1
Graph Matching for Shape Retrieval
Authors: Benoit Huet, Andrew D. J. Cross, Edwin R. Hancock
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  propose a new in-sample cross validation based method (randomized  GACV) for choosing smoothing or bandwidth parameters that govern the  bias-variance or fit-complexity tradeoff in  'soft' classification.  Soft clas(cid:173) sification refers to  a  learning procedure which  estimates the probability  that an example with a given attribute vector is  in  class  1 vs  class O.  The  target  for  optimizing  the  the  tradeoff is  the  Kullback-Liebler distance  between  the  estimated  probability  distribution  and  the  'true'  probabil(cid:173) ity  distribution,  representing  knowledge of an  infinite  population.  The  method uses a randomized estimate of the trace of a Hessian and mimics  cross validation at the cost of a single relearning with perturbed outcome  data.
DTs: Dynamic Trees
Authors: Christopher K. I. Williams, Nicholas J. Adams
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: In  this  paper we  introduce a new  class of image models,  which  we  call  dynamic trees or  DTs.  A dynamic tree model specifies  a prior  over a  large number of trees, each one of which is  a tree-structured  belief  net  (TSBN).  Experiments  show  that  DTs  are  capable  of  generating images that are less blocky, and the models have better  translation  invariance properties  than  a  fixed,  "balanced"  TSBN.  We  also show that Simulated Annealing is effective at finding trees  which  have high  posterior probability.
Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields
Authors: Dan Cornford, Ian T. Nabney, Christopher K. I. Williams
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Gaussian Processes provide good prior models for  spatial data,  but can  be  too  smooth.  In  many  physical  situations  there  are  discontinuities  along bounding surfaces, for example fronts in near-surface wind fields.  We  describe  a  modelling  method  for  such  a  constrained  discontinuity  and demonstrate how to  infer the  model parameters in wind fields  with  MCMC sampling.
The Belief in TAP
Authors: Yoshiyuki Kabashima, David Saad
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  show  the  similarity  between  belief propagation  and  TAP,  for  decoding  corrupted  messages  encoded  by  Sourlas's  method.  The  latter is  a  special  case of the Gallager  error-correcting code, where  the code word comprises products of J{ bits selected randomly from  the original message.  We examine the efficacy of solutions obtained  by the two methods for various values of J{ and show that solutions  for  J{  2':  3  may  be  sensitive  to  the  choice  of initial  conditions  in  the  case  of unbiased patterns.  Good  approximations  are obtained  generally  for  J{ = 2  and  for  biased  patterns  in  the  case  of  J{ 2':  3,  especially when  Nishimori's  temperature is  being used.
Synergy and Redundancy among Brain Cells of Behaving Monkeys
Authors: Itay Gat, Naftali Tishby
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Determining the relationship between  the activity of a single nerve  cell  to that of an entire  population is  a  fundamental question  that  bears  on  the  basic  neural  computation  paradigms.  In  this  paper  we  apply  an  information theoretic  approach  to  quantify  the  level  of cooperative  activity  among cells  in  a  behavioral  context.  It is  possible to discriminate between  synergetic  activity of the cells  vs .  redundant  activity, depending on the difference  between  the infor(cid:173) mation  they  provide  when  measured  jointly  and  the  information  they  provide independently.  We define  a synergy  value that is  pos(cid:173) itive in the first  case and negative in  the second  and show  that the  synergy value can be measured by detecting the behavioral mode of  the animal from  simultaneously recorded  activity of the  cells.  We  observe  that  among  cortical  cells  positive  synergy  can  be  found,  while cells  from the  basal ganglia, active during the same task, do  not  exhibit similar synergetic  activity. 
titay,tishby}@cs.huji.ac.il  Permanent  address:  Institute  of Computer  Science  and  Center for  Neural  Computa(cid:173)
tion,  The  Hebrew  University,  Jerusalem  91904,  Israel.
Classification on Pairwise Proximity Data
Authors: Thore Graepel, Ralf Herbrich, Peter Bollmann-Sdorra, Klaus Obermayer
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  investigate the problem of learning a classification task on data  represented in terms of their pairwise proximities.  This representa(cid:173) tion does  not refer  to an explicit feature  representation of the data  items and  is  thus  more  general than the standard approach  of us(cid:173) ing Euclidean feature vectors,  from  which  pairwise proximities can  always  be  calculated.  Our  first  approach  is  based  on  a  combined  linear  embedding  and  classification  procedure  resulting  in  an  ex(cid:173) tension of the Optimal Hyperplane algorithm to pseudo-Euclidean  data.  As  an  alternative  we  present  another  approach  based  on  a  linear threshold model in the proximity values themselves, which is  optimized using Structural Risk Minimization.  We  show that prior  knowledge about the problem can be incorporated by the choice of  distance measures and examine different metrics W.r.t.  their gener(cid:173) alization.  Finally, the algorithms are successfully applied to protein  structure  data  and  to  data from  the  cat's  cerebral  cortex.  They  show  better performance than K-nearest-neighbor classification.
Dynamically Adapting Kernels in Support Vector Machines
Authors: Nello Cristianini, Colin Campbell, John Shawe-Taylor
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: The kernel-parameter is one of the few  tunable parameters in  Sup(cid:173) port  Vector  machines,  controlling  the  complexity  of the  resulting  hypothesis.  Its  choice  amounts to model  selection  and  its  value  is  usually  found  by  means  of a  validation  set.  We  present  an  algo(cid:173) rithm which  can automatically perform  model  selection with little  additional computational cost and with no need of a validation set .  In  this  procedure  model  selection  and  learning  are  not  separate,  but  kernels  are  dynamically  adjusted  during  the  learning  process  to find  the kernel parameter which  provides the best possible upper  bound on  the  generalisation error.  Theoretical  results  motivating  the  approach  and  experimental  results  confirming  its  validity  are  presented.
Temporally Asymmetric Hebbian Learning, Spike liming and Neural Response Variability
Authors: L. F. Abbott, Sen Song
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Recent experimental data indicate that the strengthening or weakening of  synaptic connections between neurons depends on  the relative timing of  pre- and postsynaptic action potentials. A Hebbian synaptic modification  rule based on these data leads to a stable state in which the excitatory and  inhibitory inputs to a neuron are balanced, producing an irregular pattern  of firing.  It  has  been  proposed  that  neurons  in  vivo  operate  in  such  a  mode.
Learning Mixture Hierarchies
Authors: Nuno Vasconcelos, Andrew Lippman
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: The hierarchical representation of data has various applications in do(cid:173) mains such as data mining, machine vision, or information retrieval. In  this paper we introduce an extension of the Expectation-Maximization  (EM) algorithm that learns mixture hierarchies in a computationally ef(cid:173) ficient manner. Efficiency is achieved by progressing in a bottom-up  fashion, i.e. by clustering the mixture components of a given level in the  hierarchy to obtain those of the level above. This cl ustering requires onl y  knowledge of the mixture parameters, there being no need to resort to  intermediate samples. In addition to practical applications, the algorithm  allows a new interpretation of EM that makes clear the relationship with  non-parametric kernel-based estimation methods, provides explicit con(cid:173) trol over the trade-off between the bias and variance of EM estimates, and  offers new insights about the behavior of deterministic annealing methods  commonly used with EM to escape local minima of the likelihood.
Optimizing Admission Control while Ensuring Quality of Service in Multimedia Networks via Reinforcement Learning
Authors: Timothy X. Brown, Hui Tong, Satinder P. Singh
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: This  paper  examines  the  application  of  reinforcement  learning  to  a  telecommunications networking problem . The problem requires that rev(cid:173) enue  be  maximized  while  simultaneously  meeting  a  quality  of service  constraint  that  forbids  entry  into  certain  states.  We  present  a  general  solution  to  this  multi-criteria  problem  that  is  able  to  earn  significantly  higher revenues than alternatives.
Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations
Authors: Michael S. Lewicki, Terrence J. Sejnowski
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: A common way to represent a time series is to divide it into short(cid:173) duration blocks,  each of which is  then represented by a set of basis  functions.  A limitation of this approach, however, is  that the tem(cid:173) poral alignment of the basis functions with the underlying structure  in the time series is arbitrary.  We present an algorithm for encoding  a time series that does not require blocking the data.  The algorithm  finds  an efficient  representation by inferring the best temporal po(cid:173) sitions  for  functions  in  a  kernel  basis.  These  can  have  arbitrary  temporal  extent  and  are  not  constrained  to  be  orthogonal.  This  allows the model to capture structure in the signal that may occur  at arbitrary temporal positions and preserves the relative temporal  structure of underlying events.  The model is shown to be equivalent  to a  very sparse and highly over complete basis.  Under this model,  the mapping from  the data to the representation is  nonlinear,  but  can  be  computed  efficiently.  This form  also  allows  the  use  of ex(cid:173) isting methods for  adapting the basis itself to data.  This approach  is  applied to speech data and results in a shift invariant, spike-like  representation that resembles coding in the cochlear nerve.
Evidence for a Forward Dynamics Model in Human Adaptive Motor Control
Authors: Nikhil Bhushan, Reza Shadmehr
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Based  on  computational  principles,  the  concept  of  an  internal  model for  adaptive control has been divided  into a  forward  and an  inverse model.  However, there is as yet little evidence that learning  control by the eNS is through adaptation of one or the other.  Here  we  examine  two  adaptive control  architectures, one  based only on  the inverse model and other based on a combination of forward and  inverse  models.  We  then show that for  reaching movements of the  hand  in  novel  force  fields,  only  the  learning of the  forward  model  results  in  key  characteristics of performance  that match  the  kine(cid:173) matics of human subjects.  In  contrast, the adaptive control system  that relies only on the inverse model fails to produce the kinematic  patterns observed  in  the subjects,  despite the  fact  that it  is  more  stable.  Our results  provide evidence that learning control of novel  dynamics is  via formation of a  forward  model.
Restructuring Sparse High Dimensional Data for Effective Retrieval
Authors: Charles Lee Isbell Jr., Paul A. Viola
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: The task in text retrieval is to find  the subset of a collection of documents relevant  to  a user's  information request,  usually expressed as  a set of words.  Classically,  documents and queries are represented as  vectors of word counts.  In  its  simplest  form, relevance is  defined to be the dot product between a document and  a query  vector-a measure  of the  number of common  terms.  A  central  difficulty  in  text  retrieval  is  that  the  presence or absence  of a  word  is  not sufficient to  determine  relevance to a query. Linear dimensionality reduction has been proposed as a tech(cid:173) nique for extracting underlying structure from  the document collection.  In  some  domains  (such  as  vision)  dimensionality  reduction  reduces  computational com(cid:173) plexity.  In  text retrieval  it is  more often  used  to  improve retrieval  performance.  We  propose  an  alternative  and  novel  technique  that  produces  sparse  represen(cid:173) tations  constructed  from  sets  of highly-related  words.  Documents  and  queries  are  represented  by  their distance  to  these  sets,  and relevance  is  measured by  the  number of common clusters.  This technique significantly improves retrieval  per(cid:173) formance,  is  efficient  to  compute  and  shares  properties  with  the  optimal  linear  projection operator and the independent components of documents.
Approximate Learning of Dynamic Models
Authors: Xavier Boyen, Daphne Koller
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Inference is a key  component in  learning probabilistic models from par(cid:173) tially  observable  data.  When  learning  temporal  models,  each  of the  many  inference phases requires a  traversal  over an  entire long data se(cid:173) quence;  furthermore,  the  data structures manipulated are exponentially  large, making this process computationally expensive.  In [2], we describe  an approximate inference algorithm for monitoring stochastic processes,  and prove bounds on its approximation error.  In this paper, we apply this  algorithm as an approximate forward propagation step in an EM algorithm  for learning temporal Bayesian networks.  We provide a related approxi(cid:173) mation for the backward step, and prove error bounds for the  combined  algorithm.  We  show empirically that,  for a real-life domain,  EM  using  our inference algorithm  is  much  faster  than  EM  using exact inference,  with almost no degradation in quality of the learned model.  We  extend  our analysis  to  the  online learning  task,  showing a  bound  on  the  error  resulting  from  restricting  attention  to  a  small  window  of observations.  We  present an  online EM  learning algorithm for  dynamic systems,  and  show that it learns much faster than standard offline EM.
Independent Component Analysis of Intracellular Calcium Spike Data
Authors: Klaus Prank, Julia Börger, Alexander von zur Mühlen, Georg Brabant, Christof Schöfl
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Calcium (Ca2+)is an ubiquitous intracellular messenger which reg(cid:173) ulates cellular processes, such as secretion, contraction, and cell  proliferation. A number of different cell types respond to hormonal  stimuli with periodic oscillations of the intracellular free calcium  concentration ([Ca2+]i). These Ca2+ signals are often organized  in complex temporal and spatial patterns even under conditions  of sustained stimulation. Here we study the spatio-temporal as(cid:173) pects of intracellular calcium ([Ca 2+]i) oscillations in clonal J3-cells  (hamster insulin secreting cells, HIT) under pharmacological stim(cid:173) ulation (Schofi et al., 1996). We use a novel fast fixed-point al(cid:173) gorithm (Hyvarinen and Oja, 1997) for Independent Component  Analysis (ICA) to blind source separation of the spatio-temporal  dynamics of [Ca2+]i in a HIT-cell. Using this approach we find two  significant independent components out of five differently mixed in(cid:173) put signals: one [Ca2+]i signal with a mean oscillatory period of  68s and a high frequency signal with a broadband power spectrum  with considerable spectral density. This results is in good agree(cid:173) ment with a study on high-frequency [Ca2+]j oscillations (Palus  et al., 1998) Further theoretical and experimental studies have to  be performed to resolve the question on the functional impact of  intracellular signaling of these independent [Ca2+]i signals. 
932
A Model for Associative Multiplication
Authors: G. Bjorn Christianson, Suzanna Becker
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Despite the fact that mental arithmetic is  based on only a few  hun(cid:173) dred basic facts  and  some simple  algorithms, humans have a  diffi(cid:173) cult time  mastering the subject,  and  even  experienced  individuals  make  mistakes.  Associative  multiplication,  the  process  of doing  multiplication  by  memory  without the  use  of rules  or algorithms,  is  especially  problematic.  Humans  exhibit  certain  characteristic  phenomena in  performing associative  multiplications,  both in  the  type of error and  in  the error frequency.  We  propose a  model  for  the  process  of associative  multiplication,  and  compare  its  perfor(cid:173) mance  in  both  these  phenomena  with  data from  normal  humans  and from  the model proposed  by  Anderson  et al (1994).
Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy
Authors: Yoram Singer, Manfred K. Warmuth
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  describe  a  new  iterative method  for parameter estimation of Gaus(cid:173) sian  mixtures.  The new method is based on a framework developed by  Kivinen and Warmuth for supervised on-line learning. In contrast to gra(cid:173) dient descent and EM, which estimate the mixture's covariance matrices,  the proposed method estimates the inverses  of the covariance matrices.  Furthennore, the new parameter estimation procedure can  be applied in  both on-line and  batch settings.  We show experimentally that it is typi(cid:173) cally faster than EM, and usually requires about half as  many  iterations  as EM.
Fast Neural Network Emulation of Dynamical Systems for Computer Animation
Authors: Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Computer animation through the numerical simulation of physics-based  graphics  models offers  unsurpassed realism,  but  it can be computation(cid:173) ally demanding. This paper demonstrates the possibility of replacing the  numerical simulation of nontrivial dynamic  models  with  a dramatically  more  efficient  "NeuroAnimator"  that  exploits  neural  networks.  Neu(cid:173) roAnimators  are  automatically  trained  off-line  to  emulate  physical  dy(cid:173) namics through  the observation  of physics-based models in  action.  De(cid:173) pending on  the model,  its  neural  network emulator can yield physically  realistic  animation  one  or  two  orders  of magnitude faster  than  conven(cid:173) tional  numerical simulation.  We  demonstrate NeuroAnimators for a va(cid:173) riety of physics-based models.
Improved Switching among Temporally Abstract Actions
Authors: Richard S. Sutton, Satinder P. Singh, Doina Precup, Balaraman Ravindran
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: In robotics and other control applications it is commonplace to have a pre(cid:173) existing  set  of controllers  for  solving  subtasks,  perhaps  hand-crafted  or  previously learned or planned, and still face  a difficult problem of how to  choose and switch among the controllers to solve an overall task as well as  possible.  In this paper we present a framework based on Markov decision  processes and semi-Markov decision processes for phrasing this problem,  a basic theorem regarding the improvement in performance that can be ob(cid:173) tained by switching flexibly between given controllers, and example appli(cid:173) cations of the theorem.  In particular, we show how an  agent can plan with  these high-level controllers and then use the results of such planning to find  an even better plan, by modifying the existing controllers, with negligible  additional cost and no re-planning. In one of our examples, the complexity  of the problem is  reduced from  24 billion state-action pairs to  less than a  million state-controller pairs. 
In many applications, solutions to parts of a task are known, either because they were hand(cid:173) crafted  by  people  or because  they  were  previously  learned  or  planned.  For  example,  in  robotics applications, there may exist controllers for moving joints to  positions,  picking up  objects, controlling eye movements, or navigating along hallways.  More generally, an intelli(cid:173) gent system may have available to it several temporally extended courses of action to choose  from.  In such cases, a key challenge is  to  take full  advantage of the existing temporally ex(cid:173) tended actions, to choose or switch among them effectively, and to plan at their level rather  than at the level of individual actions. 
Recently, several researchers have begun to address these challenges within the framework of  reinforcement learning and Markov decision processes (e.g., Singh,  1992; Kaelbling,  1993;  Dayan & Hinton,  1993; Thrun and Schwartz,  1995;  Sutton,  1995;  Dietterich,  1998; Parr &  Russell,  1998;  McGovern, Sutton & Fagg,  1997).  Common to much of this recent work is  the  modeling  of a  temporally  extended action  as  a  policy  (controller)  and  a  condition  for  terminating,  which  we  together refer to  as  an  option  (Sutton,  Precup &  Singh,  1998).  In  this paper we consider the problem of effectively combining given  options into  one overall  policy, generalizing prior work by Kaelbling (1993).  Sections  1-3 introduce the framework;  our new results are in Sections 4 and 5. 
Improved Switching among Temporally Abstract Actions
Dynamics of Supervised Learning with Restricted Training Sets
Authors: Anthony C. C. Coolen, David Saad
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  study  the  dynamics  of supervised  learning  in  layered  neural  net(cid:173) works,  in  the regime where the size p of the training set is  proportional  to the number N  of inputs.  Here the local fields  are no longer described  by  Gaussian  distributions.  We  use  dynamical  replica theory  to  predict  the  evolution  of macroscopic  observables,  including  the  relevant  error  measures, incorporating the old formalism  in the limit piN --t  00.
A Polygonal Line Algorithm for Constructing Principal Curves
Authors: Balázs Kégl, Adam Krzyzak, Tamás Linder, Kenneth Zeger
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Principal curves have  been  defined  as  "self consistent" smooth  curves  which pass through the "middle" of a d-dimensional probability distri(cid:173) bution or data cloud.  Recently, we  [1]  have offered a new approach by  defining principal curves as continuous curves of a  given length which  minimize the expected squared distance between the curve and points of  the space randomly chosen according to  a given distribution.  The new  definition made it possible to carry out a theoretical analysis of learning  principal curves from training data.  In this paper we propose a practical  construction based on the new definition.  Simulation results demonstrate  that the new algorithm compares favorably with previous methods both  in terms of performance and computational complexity.
Neuronal Regulation Implements Efficient Synaptic Pruning
Authors: Gal Chechik, Isaac Meilijson, Eytan Ruppin
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Human and animal studies show  that mammalian brain undergoes  massive synaptic pruning during childhood , removing about half of  the  synapses  until  puberty.  We  have  previously  shown  that main(cid:173) taining network  memory performance  while  synapses  are  deleted,  requires  that  synapses  are  properly  modified  and  pruned,  remov(cid:173) ing the weaker synapses.  We  now show that neuronal regulation , a  mechanism recently  observed  to maintain the average neuronal in(cid:173) put field , results in weight-dependent synaptic modification . Under  the  correct  range  of the  degradation  dimension  and  synaptic  up(cid:173) per  bound,  neuronal  regulation  removes  the  weaker  synapses  and  judiciously  modifies  the  remaining  synapses .  It implements  near  optimal synaptic modification, and  maintains the  memory perfor(cid:173) mance  of a  network  undergoing  massive  synaptic  pruning.  Thus ,  this  paper shows  that  in  addition to the  known effects  of Hebbian  changes,  neuronal  regulation  may  play  an  important  role  in  the  self-organization of brain networks during development.
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
Authors: Michael J. Kearns, Satinder P. Singh
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: In  this  paper,  we  address  two  issues  of long-standing  interest  in  the  re(cid:173) inforcement  learning  literature.  First,  what  kinds  of performance  guar(cid:173) antees  can  be  made  for  Q-learning  after  only  a  finite  number  of actions?  Second,  what  quantitative  comparisons  can  be  made  between  Q-learning  and  model-based  (indirect)  approaches,  which  use  experience  to estimate  next-state  distributions  for  off-line  value  iteration?  We  first  show  that  both  Q-learning  and  the  indirect  approach  enjoy  rather  rapid  convergence  to  the  optimal  policy  as  a  function  of the num(cid:173) ber  of  state  transitions  observed.  In  particular,  on  the  order  of  only  (Nlog(1/c)/c2 )(log(N) + loglog(l/c))  transitions  are  sufficient  for  both  algorithms  to  come  within  c  of the  optimal  policy,  in  an  idealized  model  that  assumes  the  observed  transitions  are  "well-mixed"  throughout  an  N-state  MDP.  Thus,  the  two  approaches  have  roughly  the  same  sample  complexity.  Perhaps  surprisingly,  this  sample  complexity  is  far less  than  what is required for the model-based approach to actually construct a good  approximation  to  the  next-state  distribution.  The  result  also  shows  that  the  amount  of memory required  by  the model-based  approach  is  closer  to  N  than  to N 2 •  For  either  approach,  to  remove  the  assumption  that  the  observed  tran(cid:173) sitions  are  well-mixed,  we  consider  a  model  in  which  the  transitions  are  determined by a fixed,  arbitrary exploration policy.  Bounds on the number  of transitions  required  in  order  to  achieve  a  desired  level  of performance  are  then  related  to  the  stationary  distribution  and  mixing  time  of  this  policy.
Global Optimisation of Neural Network Models via Sequential Sampling
Authors: João F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  propose a novel strategy for  training neural networks using se(cid:173) quential sampling-importance resampling algorithms.  This global  optimisation strategy allows  us  to learn the  probability distribu(cid:173) tion of the network  weights  in  a  sequential framework.  It is  well  suited to applications involving on-line, nonlinear, non-Gaussian or  non-stationary signal processing.
Linear Hinge Loss and Average Margin
Authors: Claudio Gentile, Manfred K. Warmuth
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: We  describe a unifying method for proving relative loss  bounds for on(cid:173) line linear threshold classification algorithms, such as the Perceptron and  the Winnow algorithms.  For classification problems the discrete loss  is  used,  i.e., the total  number of prediction mistakes.  We  introduce a con(cid:173) tinuous loss function, called the "linear hinge loss", that can be employed  to derive the updates of the algorithms.  We  first  prove bounds w.r.t.  the  linear hinge loss  and  then  convert them  to  the  discrete  loss.  We  intro(cid:173) duce a notion of "average margin" of a set of examples .  We  show how  relative loss  bounds based on the  linear hinge  loss can  be converted to  relative loss bounds i.t.o.  the discrete loss using the average margin.
Learning Instance-Independent Value Functions to Enhance Local Search
Authors: Robert Moll, Andrew G. Barto, Theodore J. Perkins, Richard S. Sutton
Published in: Advances in Neural Information Processing Systems 11  (NIPS 1998) (Main Conference Track), 1998

Abstract: Reinforcement learning methods can be used to improve the performance  of local  search  algorithms  for  combinatorial  optimization  by  learning  an  evaluation  function  that  predicts  the  outcome  of search.  The  eval(cid:173) uation  function  is  therefore  able  to  guide  search  to  low-cost  solutions  better than  can  the original  cost function.  We  describe a  reinforcement  learning method for enhancing local search that combines aspects of pre(cid:173) vious work by Zhang and Dietterich (1995) and Boyan and Moore (1997,  Boyan  1998).  In  an  off-line  learning  phase,  a  value function  is  learned  that is  useful for guiding search for multiple problem sizes and instances.  We illustrate our technique by  developing several  such functions for the  Dial-A-Ride Problem.  Our learning-enhanced local  search algorithm ex(cid:173) hibits  an  improvement of more  then  30%  over  a  standard  local  search  algorithm.