mtybilly/Instruct2DS huggingface dataset:
An Incremental Nearest Neighbor Algorithm with Queries
Authors: Joel Ratsaby
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We consider the general problem of learning multi-category classifi(cid:173) cation from labeled  examples.  We  present  experimental results for  a  nearest  neighbor  algorithm which  actively  selects  samples from  different  pattern classes according to a querying rule instead of the  a  priori class  probabilities.  The  amount  of improvement  of this  query-based  approach over the passive batch approach  depends on  the  complexity of the  Bayes  rule.  The  principle  on  which  this  al(cid:173) gorithm is based is general enough to be used  in any learning algo(cid:173) rithm which  permits a  model-selection  criterion  and for  which  the  error  rate of the  classifier  is  calculable in  terms of the  complexity  of the model.
Competitive On-line Linear Regression
Authors: Volodya Vovk
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We apply a general algorithm for merging prediction strategies (the  Aggregating Algorithm) to the problem of linear regression with the  square loss;  our  main assumption  is  that the response variable is  bounded.  It turns out that for  this particular problem the Aggre(cid:173) gating Algorithm resembles, but is slightly different from,  the well(cid:173) known ridge estimation procedure.  From general results about the  Aggregating Algorithm we  deduce a  guaranteed bound on the dif(cid:173) ference between our algorithm's performance and the best, in some  sense,  linear regression function's  performance.  We  show that the  AA  attains the optimal constant  in  our  bound,  whereas  the con(cid:173) stant attained by the ridge regression procedure in general can be  4 times worse.
Nonlinear Markov Networks for Continuous Variables
Authors: Volker Tresp Reimar Hofmann
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We address the problem oflearning structure in nonlinear Markov networks  with continuous variables.  This  can  be  viewed  as  non-Gaussian multidi(cid:173) mensional density estimation exploiting certain conditional independencies  in the variables.  Markov networks are  a graphical way of describing con(cid:173) ditional independencies well suited to model relationships which do not ex(cid:173) hibit a natural causal ordering.  We use neural network structures to model  the quantitative relationships between variables.  The main focus  in this pa(cid:173) per will be on learning the structure for the purpose of gaining insight into  the underlying process.  Using two data sets we show that interesting struc(cid:173) tures can be found using our approach.  Inference will be briefly addressed.
On-line Learning from Finite Training Sets in Nonlinear Networks
Authors: David Barber Peter Sollich
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Online  learning  is  one  of the  most  common  forms  of neural  net(cid:173) work training.  We present an analysis of online learning from finite  training sets for  non-linear networks  (namely,  soft-committee ma(cid:173) chines),  advancing the  theory to more  realistic learning scenarios.  Dynamical  equations  are  derived  for  an  appropriate  set  of order  parameters;  these  are  exact  in  the  limiting  case  of  either  linear  networks  or  infinite  training  sets.  Preliminary  comparisons  with  simulations suggest that the theory captures some  effects  of finite  training sets, but may not yet account correctly for  the presence of  local minima.
Automated Aircraft Recovery via Reinforcement Learning: Initial Experiments
Authors: David G. Ward, Andrew G. Barto Jeffrey F. Monaco
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Initial experiments described here were directed toward using reinforce(cid:173) ment learning (RL) to develop an automated recovery system (ARS) for  high-agility aircraft. An ARS is an outer-loop flight-control system de(cid:173) signed to bring an aircraft from a range of out-of-control states to straight(cid:173) and-level flight in minimum time while satisfying physical and phys(cid:173) iological constraints. Here we report on results for a simple version  of the problem involving only single-axis (pitch) simulated recoveries.  Through simulated control experience using a medium-fidelity aircraft  simulation, the RL system approximates an optimal policy for pitch-stick  inputs to produce minimum-time transitions to straight-and-Ievel flight in  unconstrained cases while avoiding ground-strike. The RL system was  also able to adhere to a pilot-station acceleration constraint while execut(cid:173) ing simulated recoveries. 
Automated Aircraft Recovery via Reinforcement Learning
Adaptive Choice of Grid and Time in Reinforcement Learning
Authors: Stephan Pareigis
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We propose local error estimates together with algorithms for adap(cid:173) tive  a-posteriori  grid  and  time refinement  in  reinforcement  learn(cid:173) ing.  We  consider a  deterministic system with continuous state and  time with  infinite horizon  discounted  cost  functional.  For  grid  re(cid:173) finement  we  follow  the  procedure  of numerical  methods  for  the  Bellman-equation.  For time refinement we propose a new criterion,  based on consistency estimates of discrete solutions of the Bellman(cid:173) equation.  We  demonstrate,  that  an optimal ratio of time to space  discretization  is  crucial for  optimal learning rates  and accuracy  of  the  approximate optimal value function.
Graph Matching with Hierarchical Discrete Relaxation
Authors: Edwin R. Hancock Richard C. Wilson
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Our aim in this paper is to develop a Bayesian framework for match(cid:173) ing hierarchical relational models.  The goal is  to make discrete la(cid:173) bel assignments so as to optimise a  global cost function that draws  information concerning the consistency of match from different lev(cid:173) els  of the  hierarchy.  Our  Bayesian  development  naturally  distin(cid:173) guishes  between intra-level and inter-level constraints.  This allows  the  impact  of reassigning  a  match  to  be  assessed  not  only  at  its  own (or peer) level ofrepresentation, but also upon its parents and  children in  the hierarchy.
Hybrid NN/HMM-Based Speech Recognition with a Discriminant Neural Feature Extraction
Authors: Gerhard Rigoll Daniel Willett
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: In this paper, we present a novel hybrid architecture for continuous speech  recognition systems.  It consists of a continuous HMM system extended  by an  arbitrary neural network that is  used as  a preprocessor that takes  several frames  of the feature vector as  input to produce more discrimin(cid:173) ative feature vectors with respect to the underlying HMM system.  This  hybrid system is an extension of a state-of-the-art continuous HMM sys(cid:173) tem, and in fact, it is the first hybrid system that really is capable of outper(cid:173) forming these standard systems with respect to the recognition accuracy.  Experimental results show an relative error reduction of about 10% that  we achieved on a remarkably good recognition system based on continu(cid:173) ous HMMs for the Resource Management 1 OOO-word continuous speech  recognition task.
2D Observers for Human 3D Object Recognition?
Authors: Daniel Kersten Zili Liu
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Converging  evidence  has  shown  that  human  object  recognition  depends  on  familiarity  with  the  images  of  an  object.  Further,  the  greater  the  similarity  between  objects,  the  stronger  is  the  dependence  on  object  appearance,  and  the  more  important  two(cid:173) dimensional (2D) image information becomes.  These findings,  how(cid:173) ever, do not rule out the use of 3D structural information in recog(cid:173) nition,  and  the  degree  to  which  3D  information  is  used  in  visual  memory is an important issue.  Liu, Knill, & Kersten (1995) showed  that  any model  that  is  restricted  to  rotations  in  the  image  plane  of independent  2D  templates  could not  account for  human perfor(cid:173) mance in discriminating novel object views.  We now present results  from models of generalized radial basis functions  (GRBF), 2D near(cid:173) est  neighbor  matching that  allows  2D  affine  transformations,  and  a Bayesian statistical estimator that integrates over all possible 2D  affine  transformations.  The  performance  of the  human  observers  relative  to  each  of  the  models  is  better for  the  novel  views  than  for  the familiar  template views,  suggesting that humans generalize  better to novel  views  from  template views.  The  Bayesian estima(cid:173) tor yields the optimal performance with  2D  affine  transformations  and  independent  2D  templates.  Therefore,  models  of  2D  affine  matching  operations  with  independent  2D  templates  are unlikely  to account for  human recognition performance.
A Neural Network Based Head Tracking System
Authors: H. S. Seung Daniel D Lee
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We have constructed an inexpensive video based motorized tracking system that learns to track a head. It uses real time graphical user inputs or an auxiliary infrared detector as supervisory signals to train a convolutional neural network. The inputs to the neural network consist of normalized luminance and chrominance images and motion information from frame differences. Subsampled images are also used to provide scale invariance. During the online training phases the neural network rapidly adjusts the input weights depending up on the reliability of the different channels in the surrounding environment. This quick adaptation allows the system to robustly track a head even when other objects are moving within a cluttered background.
Perturbative M-Sequences for Auditory Systems Identification
Authors: Christoph E. Schreiner Mark Kvale
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: In  this  paper we  present a  new  method for  studying auditory sys(cid:173) tems  based  on  m-sequences.  The  method  allows  us  to  perturba(cid:173) tively  study  the  linear  response  of the  system  in  the  presence  of  various  other  stimuli,  such  as  speech  or  sinusoidal  modulations.  This allows one to construct linear kernels  (receptive fields)  at the  same time that other stimuli are being presented.  Using the method  we  calculate the modulation transfer function of single units in the  inferior colli cui us of the cat at different operating points and discuss  nonlinearities in the response.
Bach in a Box - Real-Time Harmony
Authors: Rodney M. Goodman, Jim Hawkins Randall R. Spangler
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We describe a system for  learning J. S.  Bach's rules of musical har(cid:173) mony.  These  rules  are  learned  from  examples  and  are  expressed  as  rule-based  neural networks.  The rules  are  then applied  in  real(cid:173) time to generate new  accompanying harmony for  a  live  performer.  Real-time  functionality  imposes  constraints  on  the  learning  and  harmonizing processes,  including limitations on the types of infor(cid:173) mation the system can use  as  input and the amount of processing  the  system  can  perform.  We  demonstrate  algorithms  for  gener(cid:173) ating  and  refining  musical  rules  from  examples  which  meet  these  constraints.  We  describe  a  method  for  including  a  priori knowl(cid:173) edge  into the rules  which  yields significant performance gains.  We  then describe  techniques  for  applying  these  rules  to generate new  music  in  real-time.  We  conclude  the  paper  with  an  analysis  of  experimental results.
Use of a Multi-Layer Perceptron to Predict Malignancy in Ovarian Tumors
Authors: Yves Moreau, Joos Vandewalle, Dirk Timmerman Herman Verrelst
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: This paper is concerned with the problem of Reinforcement Learn(cid:173) ing  (RL)  for  continuous  state  space  and  time  stocha.stic  control  problems.  We  state the Harnilton-Jacobi-Bellman  equation satis(cid:173) fied  by  the value  function  and use  a  Finite-Difference method for  designing a  convergent  approximation scheme.  Then we  propose a  RL  algorithm  based on this  scheme  and prove  its  convergence  to  the optimal solution. 
1 
Introduction to RL in the continuous, stochastic case 
The objective of RL is to find -thanks to a reinforcement signal- an optimal strategy  for  solving  a  dynamical  control problem.  Here we  sudy  the continuous  time,  con(cid:173) tinuous state-space stochastic case, which covers a wide variety of control problems  including target,  viability,  optimization  problems  (see  [FS93],  [KP95])}or  which  a  formalism  is  the following.  The evolution of the  current  state  x(t)  E  0  (the  state(cid:173) space, with 0  open subset of IRd ), depends on the control u(t)  E  U (compact subset)  by a  stochastic differential equation,  called the  state  dynamics: 
dx =  f(x(t), u(t))dt + a(x(t), u(t))dw 
(1)  where f  is the local drift  and a .dw  (with w  a brownian motion of dimension rand  (j  a d x r-matrix) the stochastic part (which appears for  several reasons such as lake  of precision,  noisy influence,  random fluctuations)  of the diffusion process.  For  initial state x  and control u(t),  (1)  leads  to  an infinity of possible  traj~tories  x(t).  For  some  trajectory  x(t)  (see  figure  I).,  let  T  be its  exit  time  from  0  (with  the convention  that  if x(t)  always  stays  in  0, then  T  =  00).  Then,  we  define  the  functional  J  of initial state x and control u(.) as the expectation for  all trajectories  of the discounted cumulative reinforcement  : 
J(x; u(.))  =  Ex,u( .) {loT '/r(x(t), u(t))dt +,,{ R(X(T))}
Receptive Field Formation in Natural Scene Environments: Comparison of Single Cell Learning Rules
Authors: Nathan Intrator, Harel Z. Shouval, Leon N. Cooper Brian S. Blais
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We  study  several  statistically and  biologically  motivated learning  rules  using  the same visual  environment,  one  made up  of natural  scenes,  and the same single cell  neuronal architecture.  This allows  us  to  concentrate  on  the  feature  extraction  and  neuronal  coding  properties  of these rules.  Included  in  these rules  are kurtosis  and  skewness  maximization,  the  quadratic form  of the  BCM  learning  rule,  and  single  cell  ICA.  Using  a  structure  removal  method,  we  demonstrate  that  receptive  fields  developed  using  these  rules  de(cid:173) pend  on  a  small  portion  of  the  distribution.  We  find  that  the  quadratic form  of the BCM rule behaves in  a manner similar to a  kurtosis maximization rule when the distribution contains kurtotic  directions,  although the  BCM  modification equations  are compu(cid:173) tationally simpler. 
424 
B.  S.  Blais, N.  Intrator, H.  Shouval and L  N.  Cooper
Reinforcement Learning for Call Admission Control and Routing in Integrated Service Networks
Authors: Oliver Mihatsch, Miriam Schulte, John N. Tsitsiklis Peter Marbach
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We provide a model of the standard watermaze task, and of a more  challenging task involving novel platform locations, in which rats  exhibit one-trial learning after a few days of training.  The model  uses hippocampal place cells to support reinforcement learning,  and also,  in an integrated manner,  to build  and use  allocentric  coordinates.
The Error Coding and Substitution PaCTs
Authors: Trevor Hastie Gareth James
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: A new class of plug in  classification techniques have recently been de(cid:173) veloped in the statistics and machine learning literature.  A plug in clas(cid:173) sification  technique (PaCT) is  a  method that takes  a standard classifier  (such  as  LDA or TREES)  and plugs  it into an  algorithm to produce a  new classifier.  The  standard classifier  is  known as  the Plug  in  Classi(cid:173) fier  (PiC). These methods often produce large improvements over using  a single classifier.  In this paper we investigate one of these methods and  give some motivation for its success.
Modeling Complex Cells in an Awake Macaque during Natural Image Viewing
Authors: Jack L. Gallant William E. Vinje
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We  model  the  responses  of cells  in  visual  area  VI  during  natural  vision.  Our model consists  of a  classical energy  mechanism whose  output is divided by nonclassical gain control and texture  contrast  mechanisms.  We  apply  this  model  to  review  movies,  a  stimulus  sequence  that replicates  the stimulation a  cell  receives  during free  viewing  of natural  images.  Data  were  collected  from  three  cells  using five  different  review movies, and the model was fit  separately  to  the data from  each  movie.  For  the  energy  mechanism alone  we  find  modest  but  significant  correlations  (rE  =  0.41,  0.43,  0.59,  0.35)  between  model  and  data.  These  correlations  are  improved  somewhat when we  allow for suppressive surround effects  (rE+G  =  0.42,  0.56,  0.60,  0.37).  In  one  case  the  inclusion  of a  delayed  suppressive  surround dramatically improves the  fit  to the  data by  modifying the  time course  of the model's response.
Generalization in Decision Trees and DNF: Does Size Matter?
Authors: Peter L. Bartlett, Wee Sun Lee, Llew Mason Mostefa Golea
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Recent  theoretical  results  for  pattern  classification  with  thresh(cid:173) olded real-valued  functions  (such  as  support  vector  machines,  sig(cid:173) moid  networks,  and  boosting)  give  bounds  on  misclassification  probability  that  do  not  depend  on  the size  of the  classifier,  and  hence can be considerably smaller than the bounds that follow from  the VC  theory.  In  this  paper,  we  show  that  these  techniques  can  be  more  widely  applied,  by  representing  other  boolean  functions  as  two-layer neural  networks  (thresholded convex  combinations of  boolean functions).  For example, we show that with high probabil(cid:173) ity any decision tree of depth no more than d that is consistent with  m training examples has misclassification probability no more than  o ( (~ (Neff VCdim(U)  log2 m log d)) 1/2),  where U is  the  class  of  node  decision  functions,  and  Neff  ::;  N  can  be  thought  of as  the  effective  number of leaves  (it  becomes small  as  the distribution on  the  leaves  induced  by  the  training  data gets  far  from  uniform).  This  bound  is  qualitatively different  from  the VC  bound  and  can  be considerably smaller.  We use the same technique to give similar results for DNF formulae. 
•  Author to whom correspondence should be addressed 
260 
M.  Golea,  P Bartlett,  W.  S. Lee and L  Mason
Local Dimensionality Reduction
Authors: Sethu Vijayakumar, Christopher G. Atkeson Stefan Schaal
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: If globally high dimensional data has locally only low dimensional distribu(cid:173) tions,  it is  advantageous to perform a local dimensionality reduction before  further processing the data.  In this paper we examine several techniques for  local  dimensionality reduction  in the  context of locally weighted linear re(cid:173) gression.  As possible candidates, we derive local versions of factor analysis  regression, principle component regression, principle component regression  on joint distributions, and partial least squares regression. After outlining the  statistical bases of these  methods,  we perform Monte Carlo  simulations to  evaluate  their  robustness  with  respect  to  violations  of their  statistical  as(cid:173) sumptions.  One  surprising  outcome  is  that  locally  weighted  partial  least  squares  regression offers the best average results,  thus outperforming even  factor analysis, the theoretically most appealing of our candidate techniques.
A Mathematical Model of Axon Guidance by Diffusible Factors
Authors: Geoffrey J. Goodhill
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: In the developing nervous system, gradients of target-derived dif(cid:173) fusible factors play an important role in guiding axons to  appro(cid:173) priate targets.  In this paper, the shape that such a gradient might  have is calculated as a function of distance from the target and the  time since  the start of factor production.  Using estimates  of the  relevant parameter values  from  the  experimental literature,  the  spatiotemporal domain in which a growth cone could detect such  a  gradient is derived.  For large times, a  value for the maximum  guidance range  of about 1 mm is  obtained.  This value fits  well  with experimental data.  For smaller times,  the  analysis predicts  that guidance over longer ranges may be possible. This prediction  remains to be tested.
Asymptotic Theory for Regularization: One-Dimensional Linear Case
Authors: Petri Koistinen
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: The  generalization  ability  of a  neural  network  can  sometimes  be  improved dramatically by regularization.  To  analyze the improve(cid:173) ment  one  needs  more  refined  results  than  the  asymptotic  distri(cid:173) bution  of  the  weight  vector.  Here  we  study  the  simple  case  of  one-dimensional  linear  regression  under  quadratic  regularization,  i.e.,  ridge  regression.  We  study  the  random  design,  misspecified  case, where we derive expansions for  the optimal regularization pa(cid:173) rameter and  the ensuing improvement.  It is  possible  to construct  examples  where it  is  best to use no regularization.
Instabilities in Eye Movement Control: A Model of Periodic Alternating Nystagmus
Authors: Thomas J. Anastasio Ernst R. Dow
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Nystagmus is  a pattern of eye movement characterized by  smooth rota(cid:173) tions  of the eye in one direction  and  rapid  rotations  in  the opposite di(cid:173) rection that reset eye position.  Periodic alternating nystagmus (PAN) is  a form  of uncontrollable  nystagmus  that  has  been  described  as  an  un(cid:173) stable but amplitude-limited oscillation.  PAN has been observed previ(cid:173) ously  only  in  subjects  with  vestibulo-cerebellar damage.  We describe  results in  which  PAN can be produced  in normal  subjects by prolonged  rotation in darkness.  We propose a new model  in  which the neural  cir(cid:173) cuits  that control eye movement are  inherently  unstable,  but  this  insta(cid:173) bility  is  kept  in  check  under  normal  circumstances  by  the  cerebellum.  Circumstances  which  alter  this  cerebellar  restraint,  such  as  vestibulo(cid:173) cerebellar damage or plasticity  due  to  rotation  in  darkness,  can  lead  to  PAN.
Neural Basis of Object-Centered Representations
Authors: Alexandre Pouget Sophie Denève
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We  present  a  neural  model  that  can  perform eye movements to a  particular side of an object regardless of the position and orienta(cid:173) tion  of the  object  in  space,  a  generalization  of a  task  which  has  been recently used by Olson and Gettner [4]  to investigate the neu(cid:173) ral structure of object-centered representations.  Our model uses an  intermediate representation in which units have oculocentric recep(cid:173) tive fields- just like collicular neurons- whose gain is modulated by  the side of the object to which the movement is directed, as  well as  the orientation of the object.  We show that these gain modulations  are consistent with Olson and Gettner's single cell recordings in the  supplementary eye  field.  This  demonstrates  that it  is  possible to  perform  an  object-centered  task  without  a  representation  involv(cid:173) ing an object-centered map, viz.,  without neurons whose receptive  fields are defined in object-centered coordinates.  We also show that  the same approach can account for  object-centered neglect, a  situ(cid:173) ation in which  patients with a  right parietal lesion neglect the left  side of objects regardless of the orientation of the objects. 
Several authors have argued that tasks such as object recognition [3]  and manipula(cid:173) tion  [4]  are easier to perform if the object is  represented in object-centered coordi(cid:173) nates, a representation in which the subparts of the object are encoded with respect  to a frame of reference centered on the object.  Compelling evidence for the existence  of such  representations in  the  cortex comes  from  experiments  on  hemineglect- a  neurological syndrome resulting from  unilateral lesions  of the parietal cortex such  that a  right lesion, for  example, leads  patients to ignore stimuli located on the left  side of their egocentric space.  Recently,  Driver et al.  (1994)  showed that the deficit  can  also  be  object-centered.  Hence,  hemineglect  patients can  detect  a  gap  in  the  upper edge of a triangle when this gap is associated with the right side of the object 
Neural Basis of Object-Centered Representations
Modelling Seasonality and Trends in Daily Rainfall Data
Authors: Peter M. Williams
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: This paper presents a new approach to the problem of modelling daily  rainfall using neural networks. We first model the conditional distribu(cid:173) tions of rainfall amounts, in such a way that the model itself determines  the order of the process, and the time-dependent shape and scale of the  conditional distributions. After integrating over particular weather pat(cid:173) terns, we are able to extract seasonal variations and long-term trends.
The Storage Capacity of a Fully-Connected Committee Machine
Authors: Chulan Kwon, Jong-Hoon Oh Yuansheng Xiong
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We study the storage capacity of a fully-connected  committee ma(cid:173) chine  with a  large number  K  of hidden  nodes.  The storage capac(cid:173) ity is obtained by analyzing the geometrical structure of the weight  space  related  to the internal representation .  By examining the as(cid:173) ymptotic behavior of order  parameters in the limit of large K, the  storage capacity Q c  is found to be proportional to ]{ Jln ]{ up to the  leading order.  This  result  satisfies  the  mathematical bound given  by  Mitchison  and  Durbin , whereas  the  replica-symmetric solution  in  a conventional Gardner's  approach  violates this bound.
Selecting Weighting Factors in Logarithmic Opinion Pools
Authors: Tom Heskes
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: A  simple  linear  averaging  of the  outputs  of several  networks  as  e.g.  in  bagging  [3],  seems  to follow  naturally from  a  bias/variance  decomposition of the sum-squared error.  The sum-squared error of  the  average model  is  a  quadratic function  of the weighting factors  assigned to the networks in the ensemble [7], suggesting a quadratic  programming algorithm for finding the "optimal" weighting factors.  If we  interpret  the output of a  network as a probability statement,  the  sum-squared  error  corresponds  to  minus  the  loglikelihood  or  the  Kullback-Leibler  divergence,  and  linear  averaging  of the  out(cid:173) puts  to  logarithmic  averaging  of the  probability  statements:  the  logarithmic opinion pool.  The crux of this  paper  is  that this whole  story  about model aver(cid:173) aging,  bias/variance decompositions,  and  quadratic  programming  to find  the  optimal weighting  factors,  is  not specific  for  the sum(cid:173) squared error,  but applies to the combination of probability state(cid:173) ments  of any  kind  in  a  logarithmic opinion  pool,  as  long  as  the  Kullback-Leibler divergence plays the role of the error measure.  As  examples we  treat model averaging for  classification  models under  a cross-entropy error measure and models for estimating variances.
Reinforcement Learning with Hierarchies of Machines
Authors: Stuart J. Russell Ronald Parr
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We present a new approach to reinforcement learning in which the poli(cid:173) cies considered by the learning process are constrained by hierarchies of  partially specified machines.  This allows for the  use of prior knowledge  to reduce the search space and provides a framework in which knowledge  can  be  transferred  across  problems  and  in  which  component solutions  can be recombined to solve larger and more complicated problems.  Our  approach can be seen  as providing a link between reinforcement learn(cid:173) ing and "behavior-based" or "teleo-reactive" approaches to  control.  We  present provably  convergent algorithms  for  problem-solving and learn(cid:173) ing with hierarchical machines and demonstrate their effectiveness on a  problem with several thousand states.
Multiplicative Updating Rule for Blind Separation Derived from the Method of Scoring
Authors: Howard Hua Yang
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: For blind source separation, when the Fisher information matrix is  used as the Riemannian metric tensor for the parameter space, the  steepest descent algorithm to maximize the likelihood function in  this Riemannian parameter space becomes the serial updating rule  with equivariant property. This algorithm can be further simplified  by using the asymptotic form of the Fisher information matrix  around the equilibrium.
On the Separation of Signals from Neighboring Cells in Tetrode Recordings
Authors: John S. Pezaris, Richard A. Andersen Maneesh Sahani
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We  discuss a solution to the problem of separating waveforms pro(cid:173) duced  by  multiple  cells  in  an  extracellular  neural  recording.  We  take an explicitly probabilistic approach, using latent-variable mod(cid:173) els  of varying  sophistication  to  describe  the distribution  of wave(cid:173) forms  produced  by  a  single  cell.  The  models  range from  a  single  Gaussian  distribution  of waveforms  for  each  cell  to  a  mixture  of  hidden  Markov models.  We  stress the  overall statistical structure  of the approach, allowing the details of the generative model chosen  to depend on the specific  neural preparation.
Task and Spatial Frequency Effects on Face Specialization
Authors: Garrison W. Cottrell Matthew N. Dailey
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: There is  strong  evidence that face  processing is  localized in  the brain.  The  double  dissociation  between  prosopagnosia,  a  face  recognition  deficit occurring after brain damage, and visual object agnosia, difficulty  recognizing otber kinds of complex objects, indicates tbat face and non(cid:173) face  object recognition may be served by partially independent mecha(cid:173) nisms in the brain.  Is neural specialization innate or learned?  We sug(cid:173) gest  that this  specialization  could be tbe result  of a competitive learn(cid:173) ing mechanism that, during development, devotes neural resources to the  tasks they are best at performing. Furtber, we suggest that the specializa(cid:173) tion arises as an interaction between task requirements and developmen(cid:173) tal  constraints.  In  this paper,  we present a feed-forward computational  model  of visual processing, in which  two  modules compete to  classify  input stimuli.  When  one module receives low  spatial  frequency infor(cid:173) mation  and  the  other  receives high  spatial  frequency  information,  and  the task is to identify the faces while simply classifying the objects, the  low frequency network shows a strong specialization for faces.  No otber  combination  of tasks  and  inputs  shows  this  strong  specialization.  We  take these results as support for  the idea that an  innately-specified face  processing module is unnecessary.
Extended ICA Removes Artifacts from Electroencephalographic Recordings
Authors: Colin Humphries, Te-Won Lee, Scott Makeig, Martin J. McKeown, Vicente Iragui, Terrence J. Sejnowski Tzyy-Ping Jung
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Severe  contamination  of  electroencephalographic  (EEG)  activity  by  eye  movements, blinks, muscle,  heart  and line noise is  a  serious  problem for  EEG  interpretation  and  analysis.  Rejecting  contami(cid:173) nated  EEG  segments  results  in  a  considerable  loss  of information  and may be impractical for  clinical data.  Many methods have been  proposed  to  remove  eye  movement  and  blink  artifacts  from  EEG  recordings.  Often  regression  in  the  time  or  frequency  domain  is  performed  on  simultaneous  EEG  and  electrooculographic  (EOG)  recordings  to derive  parameters characterizing the appearance and  spread  of  EOG  artifacts  in  the  EEG  channels.  However,  EOG  records  also contain brain signals [1,  2],  so  regressing  out EOG ac(cid:173) tivity inevitably involves subtracting a portion of the relevant EEG  signal  from each  recording  as  well.  Regression  cannot  be  used  to  remove  muscle  noise  or  line  noise,  since  these  have  no  reference  channels.  Here , we  propose a new and generally applicable method  for  removing  a  wide  variety  of artifacts  from  EEG  records.  The  method  is  based  on  an  extended  version  of  a  previous  Indepen(cid:173) dent  Component  Analysis  (lCA)  algorithm  [3,  4]  for  performing  blind  source  separation  on  linear  mixtures of independent  source  signals  with  either  sub-Gaussian  or  super-Gaussian  distributions.  Our  results  show  that ICA  can  effectively  detect,  separate and  re(cid:173) move  activity  in  EEG  records  from  a  wide  variety  of artifactual  sources,  with  results  comparing favorably  to  those  obtained  using  regression-based  methods. 
Extended leA Removes Artifacts from EEG Recordings
Radial Basis Functions: A Bayesian Treatment
Authors: Bernhard Schottky David Barber
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Bayesian methods have been successfully applied to regression and  classification  problems  in  multi-layer  perceptrons.  We  present  a  novel  application of Bayesian  techniques to Radial Basis Function  networks  by  developing a  Gaussian approximation to the posterior  distribution  which,  for  fixed  basis  function  widths,  is  analytic  in  the  parameters.  The setting of regularization  constants  by  cross(cid:173) validation is  wasteful  as  only  a  single optimal  parameter estimate  is  retained.  We  treat this  issue  by  assigning  prior distributions to  these constants, which are then adapted in light of the data under  a  simple re-estimation formula.
Blind Separation of Radio Signals in Fading Channels
Authors: Kari Torkkola
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We  apply  information  maximization  /  maximum  likelihood  blind  source separation [2,  6)  to complex valued signals mixed with com(cid:173) plex valued nonstationary matrices.  This case arises in radio com(cid:173) munications with  baseband signals.  We  incorporate known source  signal distributions in the adaptation, thus making the algorithms  less  "blind".  This results in drastic reduction of the amount of data  needed for successful convergence.  Adaptation to rapidly changing  signal  mixing conditions,  such as  to fading  in  mobile  communica(cid:173) tions,  becomes now feasible  as demonstrated by simulations.
New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit
Authors: Aapo Hyvärinen
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We  derive  a  first-order  approximation of the density of maximum  entropy for  a  continuous  1-D  random variable,  given  a  number of  simple  constraints.  This  results  in  a  density  expansion  which  is  somewhat  similar  to  the  classical  polynomial  density  expansions  by  Gram-Charlier  and  Edgeworth.  Using  this  approximation  of  density,  an  approximation  of  1-D  differential  entropy  is  derived.  The  approximation  of entropy  is  both  more  exact  and  more  ro(cid:173) bust  against  outliers  than  the  classical  approximation  based  on  the polynomial density expansions, without being computationally  more expensive.  The approximation has applications, for  example,  in  independent component analysis and projection pursuit.
Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings
Authors: Veikko Jousmäki, Matti Hämäläinen, Riitta Hari, Erkki Oja Ricardo Vigário
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We  have studied the application of an independent component analysis  (ICA)  approach  to  the  identification  and  possible  removal  of artifacts  from a magnetoencephalographic (MEG) recording. This statistical tech(cid:173) nique separates components according to the kurtosis of their amplitude  distributions  over  time,  thus  distinguishing  between  strictly  periodical  signals,  and regularly and irregularly occurring signals.  Many artifacts  belong to  the  last  category.  In order to  assess  the  effectiveness  of the  method, controlled artifacts were produced, which included saccadic eye  movements and blinks, increased muscular tension due to biting and the  presence of a digital watch inside  the  magnetically shielded room.  The  results demonstrate the  capability of the method to  identify and clearly  isolate the produced artifacts.
Classification by Pairwise Coupling
Authors: Robert Tibshirani Trevor Hastie
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We discuss a strategy for  polychotomous classification that involves  estimating class probabilities for  each pair of classes,  and then cou(cid:173) pling the estimates together.  The coupling model is similar to  the  Bradley-Terry  method  for  paired  comparisons.  We  study  the  na(cid:173) ture  of the  class  probability estimates that  arise,  and examine  the  performance of the procedure in simulated datasets.  The classifiers  used  include  linear  discriminants  and  nearest  neighbors:  applica(cid:173) tion  to support vector  machines is  also briefly  described.
Hybrid Reinforcement Learning and Its Application to Biped Robot Control
Authors: Akira Watanabe, Michio Nakashima Satoshi Yamada
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: A  learning  system  composed of linear  control  modules,  reinforce(cid:173) ment  learning modules  and selection  modules  (a hybrid  reinforce(cid:173) ment learning system) is proposed for the fast learning of real-world  control  problems.  The  selection  modules  choose  one  appropriate  control module  dependent  on the  state.  This  hybrid learning sys(cid:173) tem was applied to the control of a stilt-type biped robot.  It learned  the control on a sloped floor  more quickly than the usual reinforce(cid:173) ment  learning  because  it  did  not  need  to learn  the  control  on  a  flat  floor,  where  the  linear  control  module  can  control the  robot.  When it was trained by a  2-step learning (during the first  learning  step, the selection module was trained by a training procedure con(cid:173) trolled  only  by  the  linear  controller),  it  learned  the  control  more  quickly.  The average  number of trials  (about  50)  is  so small  that  the learning system is  applicable to real robot control.
Synaptic Transmission: An Information-Theoretic Perspective
Authors: Christof Koch Amit Manwani
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Here  we  analyze  synaptic  transmission  from  an  infonnation-theoretic  perspective. We derive c1osed-fonn expressions for the lower-bounds on  the capacity of a simple model of a cortical synapse under two explicit  coding paradigms.  Under the "signal estimation" paradigm, we assume  the signal to be encoded in the mean firing rate of a Poisson neuron.  The  perfonnance of an optimal linear estimator of the  signal  then  provides  a lower bound on the  capacity for signal estimation.  Under the  "signal  detection" paradigm, the presence or absence of the signal has to be de(cid:173) tected.  Perfonnance of the optimal spike detector allows us to compute  a lower bound on the capacity for signal detection.  We  find  that single  synapses (for empirically measured parameter values) transmit infonna(cid:173) tion poorly but  significant  improvement can be  achieved  with  a  small  amount of redundancy.
Just One View: Invariances in Inferotemporal Cell Tuning
Authors: Tomaso Poggio Maximilian Riesenhuber
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: In  macaque  inferotemporal cortex  (IT),  neurons have  been found to re(cid:173) spond  selectively to complex  shapes  while  showing broad  tuning ("in(cid:173) variance")  with  respect  to stimulus transformations such as  translation  and  scale  changes  and  a  limited  tuning to rotation  in  depth.  Training  monkeys with novel,  paperclip-like objects, Logothetis et al. 9  could in(cid:173) vestigate whether these invariance properties are due to experience with  exhaustively many transformed instances of an object or if there are mech(cid:173) anisms that allow the cells to show response invariance also to previously  unseen instances of that object.  They found object-selective cells in an(cid:173) terior IT which exhibited limited invariance  to various transformations  after training with single object views.  While previous models accounted  for  the tuning of the  cells  for  rotations  in  depth and  for  their selectiv(cid:173) ity  to a  specific  object relative to a population of distractor objects,14,1  the model described here attempts to explain in a  biologically plausible  way  the additional properties of translation  and  size  invariance.  Using  the  same  stimuli  as  in  the  experiment,  we  find  that model  IT neurons  exhibit invariance properties which closely parallel those of real neurons.  Simulations show that the model  is capable of unsupervised learning of  view-tuned neurons. 
We thank Peter Dayan, Marcus Dill, Shimon Edelman, Nikos Logothetis, Jonathan Mumick and 
Randy O'Reilly for useful discussions and comments. 
216
Agnostic Classification of Markovian Sequences
Authors: Shai Fine, Naftali Tishby Ran El-Yaniv
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Classification of finite sequences without explicit knowledge of their  statistical nature is  a  fundamental  problem  with  many  important  applications.  We  propose  a  new  information  theoretic  approach  to this  problem which is  based on the following  ingredients:  (i)  se(cid:173) quences are similar when they are likely to be generated by the same  source;  (ii) cross entropies can be estimated via "universal compres(cid:173) sion";  (iii)  Markovian  sequences  can  be  asymptotically-optimally  merged.  With these ingredients we  design a  method for  the classification of  discrete sequences whenever they can be compressed.  We introduce  the method and illustrate its application for hierarchical clustering  of languages and for  estimating similarities of protein sequences. 
1
Visual Navigation in a Robot Using Zig-Zag Behavior
Authors: M. Anthony Lewis
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: We implement a model of obstacle avoidance in flying insects on a small,  monocular robot. The result is a system that is capable of rapid navigation  through a dense obstacle field. The key to the system is the use of zigzag  behavior to articulate the body during movement. It is shown that this  behavior  compensates for a parallax blind spot surrounding the focus of expansion nor(cid:173) mally found in systems without parallax behavior. The system models the coop(cid:173) eration of several behaviors: halteres-ocular response (similar to VOR),  optomotor response, and the parallax field computation and mapping to motor  system. The resulting system is neurally plausible, very simple, and should be  easily hosted on a VLSI hardware.
Generalized Prioritized Sweeping
Authors: Nir Friedman, Ronald Parr David Andre
Published in: Advances in Neural Information Processing Systems 10  (NIPS 1997) (Main Conference Track), 1997

Abstract: Prioritized sweeping  is  a  model-based  reinforcement learning  method  that  attempts  to  focus  an  agent's  limited  computational  resources  to  achieve a good estimate of the value of environment states.  To choose ef(cid:173) fectively where to spend a costly planning step, classic prioritized sweep(cid:173) ing  uses  a simple  heuristic  to  focus  computation on  the  states  that are  likely to have the largest errors.  In this paper, we  introduce generalized  prioritized sweeping, a principled method for generating such estimates  in a representation-specific manner.  This allows us to extend prioritized  sweeping beyond an explicit, state-based representation to deal with com(cid:173) pact representations that are necessary for dealing with large state spaces.  We  apply  this  method  for  generalized  model  approximators  (such  as  Bayesian networks), and describe preliminary experiments that compare  our approach with classical prioritized sweeping.