mtybilly/Instruct2DS huggingface dataset:
Orientational and Geometric Determinants of Place and Head-direction
Authors: Neil Burgess, Tom Hartley
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We present a model of the firing of place and head-direction cells in rat hippocampus. The model can predict the response of individual cells and populations to parametric manipulations of both geomet(cid:173) ric (e.g. O'Keefe & Burgess, 1996) and orientational (Fenton et aI., 2000a) cues, extending a previous geometric model (Hartley et al., 2000). It provides a functional description of how these cells' spatial responses are derived from the rat's environment and makes easily testable quantitative predictions. Consideration of the phenomenon of remapping (Muller & Kubie, 1987; Bostock et aI., 1991) indicates that the model may also be consistent with non(cid:173) parametric changes in firing, and provides constraints for its future development.
Modeling Temporal Structure in Classical Conditioning
Authors: Aaron C. Courville, David S. Touretzky
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The Temporal Coding Hypothesis of Miller  and colleagues  [7]  sug(cid:173) gests  that  animals  integrate  related  temporal  patterns  of stimuli  into  single  memory  representations.  We  formalize  this  concept  using  quasi-Bayes  estimation  to  update  the  parameters  of a  con(cid:173) strained hidden Markov model.  This approach allows us to account  for  some surprising temporal effects  in the second order condition(cid:173) ing  experiments  of Miller  et  al.  [1 ,  2,  3],  which  other  models  are  unable to explain.
Modeling the Modulatory Effect of Attention on Human Spatial Vision
Authors: Laurent Itti, Jochen Braun, Christof Koch
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We present new simulation results, in which a computational model  of interacting visual neurons simultaneously predicts the modula(cid:173) tion of spatial vision thresholds by focal visual attention, for five  dual-task human psychophysics experiments. This new study com(cid:173) plements our previous findings that attention activates a winner(cid:173) take-all competition among early visual neurons within one cortical  hypercolumn. This "intensified competition" hypothesis assumed  that attention equally affects all neurons, and yielded two single(cid:173) unit predictions: an increase in gain and a sharpening of tuning  with attention. While both effects have been separately observed  in electrophysiology, no single-unit study has yet shown them si(cid:173) multaneously. Hence, we here explore whether our model could still  predict our data if attention might only modulate neuronal gain,  but do so non-uniformly across neurons and tasks. Specifically, we  investigate whether modulating the gain of only the neurons that  are loudest, best-tuned, or most informative about the stimulus,  or of all neurons equally but in a task-dependent manner, may ac(cid:173) count for the data. We find that none of these hypotheses yields  predictions as plausible as the intensified competition hypothesis,  hence providing additional support for our original findings.
Approximate Dynamic Programming via Linear Programming
Authors: Daniela Farias, Benjamin V. Roy
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The curse of dimensionality gives rise to prohibitive computational  requirements that render infeasible the exact solution of large- scale  stochastic  control  problems.  We  study  an  efficient  method  based  on  linear  programming for  approximating  solutions  to  such  prob(cid:173) lems.  The  approach  "fits"  a  linear  combination  of  pre- selected  basis  functions  to the  dynamic  programming cost- to- go  function.  We develop bounds on the approximation error and present experi(cid:173) mental results in the domain of queueing network control, providing  empirical support for  the methodology.
A New Discriminative Kernel From Probabilistic Models
Authors: Koji Tsuda, Motoaki Kawanabe, Gunnar Rätsch, Sören Sonnenburg, Klaus-Robert Müller
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Recently, Jaakkola and Haussler proposed a  method for  construct(cid:173) ing  kernel  functions  from  probabilistic  models.  Their  so  called  "Fisher  kernel"  has  been  combined  with  discriminative  classifiers  such  as  SVM  and  applied  successfully  in  e.g.  DNA  and  protein  analysis.  Whereas  the  Fisher  kernel  (FK)  is  calculated  from  the  marginal  log-likelihood,  we  propose  the  TOP  kernel  derived  from  Tangent  vectors  Of  Posterior  log-odds.  Furthermore  we  develop  a  theoretical  framework  on  feature  extractors  from  probabilistic  models  and use  it  for  analyzing  FK  and TOP.  In experiments our  new  discriminative  TOP  kernel  compares  favorably  to  the  Fisher  kernel.
Kernel Feature Spaces and Nonlinear Blind Souce Separation
Authors: Stefan Harmeling, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: In kernel based learning the data is mapped to a kernel feature space of a dimension that corresponds to the number of training data points. In practice, however, the data forms a smaller submanifold in feature space, a fact that has been used e.g. by reduced set techniques for SVMs. We propose a new mathematical construction that permits to adapt to the in- trinsic dimension and to ﬁnd an orthonormal basis of this submanifold. In doing so, computations get much simpler and more important our theoretical framework allows to derive elegant kernelized blind source separation (BSS) algorithms for arbitrary invertible nonlinear mixings. Experiments demonstrate the good performance and high computational efﬁciency of our kTDSEP algorithm for the problem of nonlinear BSS.
The Unified Propagation and Scaling Algorithm
Authors: Yee W. Teh, Max Welling
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: In this paper we will show that a restricted class of constrained mini- mum divergence problems, named generalized inference problems, can be solved by approximating the KL divergence with a Bethe free energy. The algorithm we derive is closely related to both loopy belief propaga- tion and iterative scaling. This uniﬁed propagation and scaling algorithm reduces to a convergent alternative to loopy belief propagation when no constraints are present. Experiments show the viability of our algorithm.
Receptive field structure of flow detectors for heading perception
Authors: J. A. Beintema, M. Lappe, Alexander C. Berg
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Observer translation relative to the world  creates image flow  that  expands from the observer's direction of translation (heading)  from  which  the  observer  can  recover  heading  direction.  Yet,  the  image  flow  is often more complex, depending on rotation of the eye,  scene  layout  and  translation  velocity.  A  number  of  models  [1-4]  have  been  proposed  on  how  the human  visual  system extracts  heading  from  flow  in  a  neurophysiologic ally  plausible  way.  These  models  represent heading by a  set of neurons  that respond to large image  flow  patterns and receive input from motion sensed at different im(cid:173) age  locations.  We  analysed  these  models  to  determine  the  exact  receptive  field  of these  heading  detectors.  We  find  most  models  predict that, contrary to widespread believe,  the contribut ing mo(cid:173) tion sensors have a preferred motion directed circularly rather than  radially around the detector's preferred heading.  Moreover, the re(cid:173) sults suggest to look for  more refined structure within the circular  flow,  such as bi-circularity or local motion-opponency.
Audio-Visual Sound Separation Via Hidden Markov Models
Authors: John R. Hershey, Michael Casey
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: It  is  well  known  that  under  noisy  conditions  we  can  hear  speech  much  more  clearly  when  we  read  the  speaker's  lips.  This  sug(cid:173) gests  the utility of audio-visual information for  the task of speech  enhancement.  We  propose  a  method  to exploit  audio-visual  cues  to  enable  speech  separation  under  non-stationary  noise  and  with  a  single  microphone.  We  revise  and  extend  HMM-based  speech  enhancement techniques, in which signal and noise models are fac(cid:173) tori ally  combined,  to  incorporate  visual  lip  information  and  em(cid:173) ploy  novel  signal  HMMs  in  which  the  dynamics  of  narrow-band  and  wide  band  components  are  factorial.  We  avoid  the  combina(cid:173) torial  explosion  in  the  factorial  model  by  using  a  simple  approxi(cid:173) mate  inference  technique  to  quickly  estimate  the  clean  signals  in  a  mixture.  We  present  a  preliminary  evaluation  of this  approach  using a small-vocabulary audio-visual database, showing promising  improvements in  machine  intelligibility for  speech  enhanced using  audio and visual information.
Spectral Relaxation for K-means Clustering
Authors: Hongyuan Zha, Xiaofeng He, Chris Ding, Ming Gu, Horst D. Simon
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The popular K-means  clustering partitions a  data set by minimiz(cid:173) ing  a  sum-of-squares cost function.  A  coordinate descend  method  is  then used to find  local minima.  In  this  paper we  show that the  minimization can be reformulated as a trace maximization problem  associated with the Gram matrix of the data vectors.  Furthermore,  we  show that a  relaxed version of the trace maximization problem  possesses  global  optimal solutions  which  can be obtained by  com(cid:173) puting a  partial eigendecomposition  of the  Gram matrix,  and the  cluster assignment for  each data vectors  can be found  by  comput(cid:173) ing  a  pivoted  QR decomposition  of the  eigenvector  matrix.  As  a  by-product  we  also  derive  a  lower bound  for  the  minimum  of the  sum-of-squares cost function.
Kernel Machines and Boolean Functions
Authors: Adam Kowalczyk, Alex J. Smola, Robert C. Williamson
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We give results about the learnability and required complexity of logical formulae to solve classiﬁcation problems. These results are obtained by linking propositional logic with kernel machines. In particular we show that decision trees and disjunctive normal forms (DNF) can be repre- sented by the help of a special kernel, linking regularized risk to separa- tion margin. Subsequently we derive a number of lower bounds on the required complexity of logic formulae using properties of algorithms for generation of linear estimators, such as perceptron and maximal percep- tron learning.
Information Geometrical Framework for Analyzing Belief Propagation Decoder
Authors: Shiro Ikeda, Toshiyuki Tanaka, Shun-ichi Amari
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The mystery of belief propagation (BP) decoder, especially of the turbo decoding, is studied from information geometrical viewpoint. The loopy belief network (BN) of turbo codes makes it difﬁcult to obtain the true “belief” by BP, and the characteristics of the algorithm and its equilib- rium are not clearly understood. Our study gives an intuitive understand- ing of the mechanism, and a new framework for the analysis. Based on the framework, we reveal basic properties of the turbo decoding.
Speech Recognition using SVMs
Authors: N. Smith, Mark Gales
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: An important issue in applying SVMs  to speech recognition is  the  ability  to  classify  variable length  sequences.  This  paper  presents  extensions to a  standard scheme  for  handling this  variable length  data, the Fisher score.  A more useful mapping is introduced based  on  the  likelihood-ratio.  The  score-space  defined  by  this  mapping  avoids some limitations of the Fisher score.  Class-conditional gen(cid:173) erative models  are  directly  incorporated into the  definition  of the  score-space.  The mapping, and appropriate normalisation schemes,  are evaluated on a  speaker-independent  isolated letter  task where  the  new  mapping  outperforms  both  the  Fisher  score  and  HMMs  trained to maximise likelihood.
Information-Geometrical Significance of Sparsity in Gallager Codes
Authors: Toshiyuki Tanaka, Shiro Ikeda, Shun-ichi Amari
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We report a result of perturbation analysis on decoding error of the belief propagation decoder for Gallager codes. The analysis is based on infor- mation geometry, and it shows that the principal term of decoding error at equilibrium comes from the m-embedding curvature of the log-linear submanifold spanned by the estimated pseudoposteriors, one for the full marginal, and K for partial posteriors, each of which takes a single check into account, where K is the number of checks in the Gallager code. It is then shown that the principal error term vanishes when the parity-check matrix of the code is so sparse that there are no two columns with overlap greater than 1.
An Efficient Clustering Algorithm Using Stochastic Association Model and Its Implementation Using Nanostructures
Authors: Takashi Morie, Tomohiro Matsuura, Makoto Nagata, Atsushi Iwata
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: This paper describes a clustering algorithm for vector quantizers using a “stochastic association model”. It offers a new simple and powerful soft- max adaptation rule. The adaptation process is the same as the on-line K-means clustering method except for adding random ﬂuctuation in the distortion error evaluation process. Simulation results demonstrate that the new algorithm can achieve efﬁcient adaptation as high as the “neural gas” algorithm, which is reported as one of the most efﬁcient clustering methods. It is a key to add uncorrelated random ﬂuctuation in the simi- larity evaluation process for each reference vector. For hardware imple- mentation of this process, we propose a nanostructure, whose operation is described by a single-electron circuit. It positively uses ﬂuctuation in quantum mechanical tunneling processes.
Grammar Transfer in a Second Order Recurrent Neural Network
Authors: Michiro Negishi, Stephen J. Hanson
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: It  has  been  known  that  people,  after  being  exposed  to  sentences  generated  by  an  artificial  grammar,  acquire  implicit  grammatical  knowledge and are able to transfer the knowledge to inputs that are  generated  by  a  modified  grammar.  We  show  that  a  second  order  recurrent neural network is able to transfer grammatical knowledge  from one language (generated by a Finite State Machine) to another  language which differ  both in vocabularies and syntax.  Representa(cid:173) tion of the grammatical knowledge in the network is analyzed using  linear  discriminant analysis.
Product Analysis: Learning to Model Observations as Products of Hidden Variables
Authors: Brendan J. Frey, Anitha Kannan, Nebojsa Jojic
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Factor analysis  and principal  components  analysis  can be  used  to  model linear relationships between observed variables  and linearly  map  high-dimensional  data to  a  lower-dimensional  hidden  space.  In  factor  analysis,  the  observations  are  modeled  as  a  linear  com(cid:173) bination  of normally  distributed  hidden  variables.  We  describe  a  nonlinear  generalization of factor  analysis,  called  "product analy(cid:173) sis",  that  models  the  observed  variables  as  a  linear  combination  of products  of normally  distributed  hidden  variables.  Just  as  fac(cid:173) tor  analysis  can  be  viewed  as  unsupervised  linear  regression  on  unobserved,  normally  distributed  hidden  variables,  product  anal(cid:173) ysis  can  be  viewed  as  unsupervised  linear  regression  on  products  of unobserved,  normally  distributed  hidden  variables.  The  map(cid:173) ping  between  the  data  and  the  hidden  space  is  nonlinear,  so  we  use  an  approximate variational  technique  for  inference  and learn(cid:173) ing.  Since  product  analysis  is  a  generalization  of factor  analysis,  product  analysis  always  finds  a  higher  data likelihood than factor  analysis.  We  give results on pattern recognition  and illumination(cid:173) invariant image clustering.
Multiplicative Updates for Classification by Mixture Models
Authors: Lawrence K. Saul, Daniel D. Lee
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We investigate a learning algorithm for the classiﬁcation of nonnegative data by mixture models. Multiplicative update rules are derived that directly optimize the performance of these models as classiﬁers. The update rules have a simple closed form and an intuitive appeal. Our algorithm retains the main virtues of the Expectation-Maximization (EM) algorithm—its guarantee of monotonic im- provement, and its absence of tuning parameters—with the added advantage of optimizing a discriminative objective function. The algorithm reduces as a spe- cial case to the method of generalized iterative scaling for log-linear models. The learning rate of the algorithm is controlled by the sparseness of the training data. We use the method of nonnegative matrix factorization (NMF) to discover sparse distributed representations of the data. This form of feature selection greatly accelerates learning and makes the algorithm practical on large problems. Ex- periments show that discriminatively trained mixture models lead to much better classiﬁcation than comparably sized models trained by EM.
The Infinite Hidden Markov Model
Authors: Matthew J. Beal, Zoubin Ghahramani, Carl E. Rasmussen
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We show that it is possible to extend hidden Markov models to have a countably inﬁnite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the inﬁnitely many transition parameters, leaving only three hyperparameters which can be learned from data. These three hyperparameters deﬁne a hierarchical Dirichlet process capable of capturing a rich set of transition dynamics. The three hyperparameters control the time scale of the dynamics, the sparsity of the underlying state-transition matrix, and the expected num- ber of distinct hidden states in a ﬁnite sequence. In this framework it is also natural to allow the alphabet of emitted symbols to be inﬁnite— consider, for example, symbols being possible words appearing in En- glish text.
EM-DD: An Improved Multiple-Instance Learning Technique
Authors: Qi Zhang, Sally A. Goldman
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We  present  a  new  multiple-instance  (MI)  learning technique  (EM(cid:173) DD)  that  combines  EM  with  the  diverse  density  (DD)  algorithm.  EM-DD is a general-purpose MI algorithm that can be applied with  boolean  or  real-value  labels  and  makes  real-value  predictions.  On  the boolean Musk benchmarks, the EM-DD algorithm without any  tuning  significantly  outperforms  all  previous  algorithms.  EM-DD  is  relatively  insensitive to the number of relevant  attributes  in  the  data set  and  scales  up  well  to  large  bag  sizes.  Furthermore,  EM(cid:173) DD  provides  a  new  framework  for  MI  learning,  in  which  the  MI  problem  is  converted  to  a  single-instance  setting  by  using  EM  to  estimate the instance  responsible  for  the  label of the bag.
Fast Parameter Estimation Using Green's Functions
Authors: K. Wong, F. Li
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We  propose  a  method  for  the  fast  estimation  of hyperparameters  in large networks, based on the linear response relation in the cav(cid:173) ity  method,  and  an  empirical  measurement  of  the  Green's  func(cid:173) tion.  Simulation results  show  that it is  efficient  and precise,  when  compared with cross-validation and other techniques which require  matrix inversion.
Information-Geometric Decomposition in Spike Analysis
Authors: Hiroyuki Nakahara, Shun-ichi Amari
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We  present  an  information-geometric  measure  to  systematically  investigate  neuronal  firing  patterns,  taking  account  not  only  of  the  second-order  but  also  of higher-order  interactions.  We  begin  with the case of two  neurons for  illustration and show  how to test  whether or not any pairwise correlation in one period is significantly  different from  that in the other period.  In order to test such a  hy(cid:173) pothesis  of different  firing  rates,  the  correlation term needs  to  be  singled out 'orthogonally' to the firing rates, where the null hypoth(cid:173) esis might not be of independent firing.  This method is  also shown  to  directly  associate  neural  firing  with  behavior  via their  mutual  information,  which  is  decomposed  into  two  types  of  information,  conveyed  by  mean  firing  rate  and  coincident  firing,  respectively.  Then, we  show that these results, using the 'orthogonal' decompo(cid:173) sition,  are  naturally  extended  to  the  case  of three  neurons  and  n  neurons in general.
Learning Body Pose via Specialized Maps
Authors: Rómer Rosales, Stan Sclaroff
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: A  nonlinear  supervised learning model,  the  Specialized  Mappings  Architecture  (SMA),  is  described and applied to the estimation of  human  body  pose  from  monocular  images.  The  SMA  consists  of  several specialized forward mapping functions and an inverse map(cid:173) ping  function.  Each  specialized  function  maps  certain  domains  of the  input  space  (image  features)  onto  the  output  space  (body  pose parameters).  The key algorithmic problems faced are those of  learning the specialized  domains  and mapping functions  in an op(cid:173) timal way,  as well  as performing inference given inputs and knowl(cid:173) edge  of the  inverse  function.  Solutions  to  these  problems  employ  the EM  algorithm and alternating choices  of conditional  indepen(cid:173) dence assumptions.  Performance of the approach is  evaluated with  synthetic and real video sequences of human motion.
Citcuits for VLSI Implementation of Temporally Asymmetric Hebbian Learning
Authors: A. Bofill, D. P. Thompson, Alan F. Murray
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Experimental data has  shown  that synaptic  strength  modification  in some types of biological neurons depends upon precise spike tim(cid:173) ing  differences  between  presynaptic  and  postsynaptic  spikes.  Sev(cid:173) eral  temporally-asymmetric  Hebbian  learning  rules  motivated  by  this  data have  been  proposed.  We  argue  that  such  learning  rules  are  suitable  to  analog  VLSI  implementation.  We  describe  an  eas(cid:173) ily  tunable circuit  to modify the weight of a  silicon spiking neuron  according to those  learning rules.  Test results from the fabrication  of the circuit using  a  O.6J.lm  CMOS  process  are  given.
Model Based Population Tracking and Automatic Detection of Distribution Changes
Authors: Igor V. Cadez, P. S. Bradley
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Probabilistic mixture models are used for a broad range of data anal- ysis tasks such as clustering, classiﬁcation, predictive modeling, etc. Due to their inherent probabilistic nature, mixture models can easily be combined with other probabilistic or non-probabilistic techniques thus forming more complex data analysis systems. In the case of online data (where there is a stream of data available) models can be constantly up- dated to reﬂect the most current distribution of the incoming data. How- ever, in many business applications the models themselves represent a parsimonious summary of the data and therefore it is not desirable to change models frequently, much less with every new data point. In such a framework it becomes crucial to track the applicability of the mixture model and detect the point in time when the model fails to adequately represent the data. In this paper we formulate the problem of change detection and propose a principled solution. Empirical results over both synthetic and real-life data sets are presented.
1 Introduction and Notation
Consider a data set D = fx1; x2; : : : ; xng consisting of n independent, identically dis- tributed (iid) data points. In context of this paper the data points could be vectors, se- quences, etc. Further, consider a probabilistic mixture model that maps each data set to a real number, the probability of observing the data set:
A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing
Authors: S. Narayanan, Daniel Jurafsky
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Narayanan and Jurafsky (1998) proposed that human language compre- hension can be modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension process with Bayesian de- cision trees. In this paper we extend the Narayanan and Jurafsky model to make further predictions about reading time given the probability of difference parses or interpretations, and test the model against reading time data from a psycholinguistic experiment.
MIME: Mutual Information Minimization and Entropy Maximization for Bayesian Belief Propagation
Authors: Anand Rangarajan, Alan L. Yuille
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Bayesian belief propagation in graphical models has been recently shown to have very close ties to inference methods based in statis- tical physics. After Yedidia et al. demonstrated that belief prop- agation (cid:12)xed points correspond to extrema of the so-called Bethe free energy, Yuille derived a double loop algorithm that is guar- anteed to converge to a local minimum of the Bethe free energy. Yuille’s algorithm is based on a certain decomposition of the Bethe free energy and he mentions that other decompositions are possi- ble and may even be fruitful. In the present work, we begin with the Bethe free energy and show that it has a principled interpre- tation as pairwise mutual information minimization and marginal entropy maximization (MIME). Next, we construct a family of free energy functions from a spectrum of decompositions of the original Bethe free energy. For each free energy in this family, we develop a new algorithm that is guaranteed to converge to a local min- imum. Preliminary computer simulations are in agreement with this theoretical development.
Rates of Convergence of Performance Gradient Estimates Using Function Approximation and Bias in Reinforcement Learning
Authors: Gregory Z. Grudic, Lyle H. Ungar
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract:  
We address two open theoretical questions in Policy Gradient Reinforce- ment Learning. The ﬁrst concerns the efﬁcacy of using function approx- imation to represent the state action value function,  . Theory is pre- sented showing that linear function approximation representations of  can degrade the rate of convergence of performance gradient estimates by a factor of relative to when no function approximation of  is used, where is the number of basis functions in the function approximation representation. The sec- ond concerns the use of a bias term in estimating the state action value function. Theory is presented showing that a non-zero bias term can improve the rate of convergence of performance gradient estimates by is the number of possible actions. Experimen- 
  tal evidence is presented showing that these theoretical results lead to signiﬁcant improvement in the convergence properties of Policy Gradi- ent Reinforcement Learning algorithms.
is the number of possible actions and
A Generalization of Principal Components Analysis to the Exponential Family
Authors: Michael Collins, S. Dasgupta, Robert E. Schapire
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Principal component analysis (PCA) is a commonly applied technique for dimensionality reduction. PCA implicitly minimizes a squared loss function, which may be inappropriate for data that is not real-valued, such as binary-valued data. This paper draws on ideas from the Exponen- tial family, Generalized linear models, and Bregman distances, to give a generalization of PCA to loss functions that we argue are better suited to other data types. We describe algorithms for minimizing the loss func- tions, and give examples on simulated data.
Minimax Probability Machine
Authors: Gert Lanckriet, Laurent E. Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: When  constructing  a  classifier,  the  probability  of correct  classifi(cid:173) cation of future  data points  should  be maximized.  In  the  current  paper  this  desideratum is  translated  in  a  very  direct  way  into  an  optimization  problem,  which  is  solved  using  methods  from  con(cid:173) vex  optimization.  We  also  show  how  to exploit  Mercer  kernels  in  this setting to obtain nonlinear decision  boundaries.  A  worst-case  bound  on the  probability of misclassification  of future  data is  ob(cid:173) tained explicitly.
Duality, Geometry, and Support Vector Regression
Authors: J. Bi, Kristin P. Bennett
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We develop an intuitive geometric framework for support vector regression (SVR). By examining when (cid:15)-tubes exist, we show that SVR can be regarded as a classi(cid:12)cation problem in the dual space. Hard and soft (cid:15)-tubes are constructed by separating the convex or reduced convex hulls respectively of the training data with the response variable shifted up and down by (cid:15). A novel SVR model is proposed based on choosing the max-margin plane between the two shifted datasets. Maximizing the margin corresponds to shrinking the e(cid:11)ective (cid:15)-tube. In the proposed approach the e(cid:11)ects of the choices of all parameters become clear geometrically.
Blind Source Separation via Multinode Sparse Representation
Authors: Michael Zibulevsky, Pavel Kisilev, Yehoshua Y. Zeevi, Barak A. Pearlmutter
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We  consider a problem of blind  source  separation from  a set  of instan(cid:173) taneous  linear mixtures,  where  the  mixing  matrix  is  unknown.  It was  discovered recently, that  exploiting the  sparsity of sources  in  an  appro(cid:173) priate  representation  according  to  some  signal  dictionary,  dramatically  improves the quality  of separation.  In this work  we  use the property of  multi scale transforms, such as wavelet or wavelet packets, to decompose  signals  into  sets  of local  features  with  various  degrees  of sparsity.  We  use this intrinsic property  for selecting the  best (most sparse) subsets of  features  for further separation.  The performance of the algorithm is ver(cid:173) ified  on noise-free and noisy  data.  Experiments with simulated signals,  musical sounds and images demonstrate significant improvement of sep(cid:173) aration quality over previously reported results.
Estimating the Reliability of ICA Projections
Authors: Frank C. Meinecke, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: When applying unsupervised learning techniques like  ICA or tem(cid:173) poral decorrelation,  a  key  question  is  whether the  discovered  pro(cid:173) jections are reliable.  In other words:  can we  give  error  bars or can  we  assess  the  quality of our separation?  We  use  resampling meth(cid:173) ods  to  tackle  these  questions  and  show  experimentally  that  our  proposed variance estimations are strongly correlated to the sepa(cid:173) ration  error.  We  demonstrate that  this  reliability  estimation  can  be  used  to  choose  the  appropriate ICA-model,  to enhance signifi(cid:173) cantly the  separation performance, and,  most  important,  to mark  the components that have a  actual physical meaning.  Application  to  49-channel-data from  an  magneto encephalography  (MEG)  ex(cid:173) periment underlines the usefulness of our approach.
The g Factor: Relating Distributions on Features to Distributions on Images
Authors: James M. Coughlan, Alan L. Yuille
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We  describe  the  g-factor,  which  relates  probability  distributions  on image features  to  distributions on the  images  themselves.  The  g-factor  depends  only  on  our choice  of features  and  lattice  quanti(cid:173) zation and is  independent of the training image data.  We  illustrate  the importance  of the g-factor by analyzing how the parameters of  Markov Random Field (i.e.  Gibbs or log-linear) probability models  of images are learned from data by maximum likelihood estimation.  In particular, we study homogeneous MRF models which learn im(cid:173) age distributions in terms of clique potentials corresponding to fea(cid:173) ture  histogram  statistics  (d.  Minimax  Entropy  Learning  (MEL)  by  Zhu,  Wu  and  Mumford  1997  [11]) .  We  first  use  our  analysis  of the  g-factor  to  determine  when  the  clique  potentials  decouple  for  different  features .  Second,  we  show  that  clique  potentials  can  be  computed  analytically  by  approximating  the  g-factor.  Third,  we  demonstrate a  connection between this  approximation and the  Generalized Iterative Scaling algorithm (GIS),  due to Darroch and  Ratcliff  1972  [2],  for  calculating  potentials.  This  connection  en(cid:173) ables  us  to  use  GIS  to  improve  our  multinomial  approximation,  using  Bethe-Kikuchi[8]  approximations to simplify  the  GIS  proce(cid:173) dure.  We  support our analysis by computer simulations.
A Maximum-Likelihood Approach to Modeling Multisensory Enhancement
Authors: H. Colonius, A. Diederich
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Multisensory response enhancement (MRE) is  the augmentation of  the  response  of a  neuron  to  sensory  input  of one  modality  by  si(cid:173) multaneous input from another modality.  The maximum likelihood  (ML)  model presented here modifies  the Bayesian model for  MRE  (Anastasio et al.)  by incorporating a decision strategy to maximize  the number of correct decisions.  Thus the ML model can also deal  with  the  important  tasks  of stimulus  discrimination  and  identifi(cid:173) cation in  the presence  of incongruent  visual  and auditory cues.  It  accounts  for  the  inverse  effectiveness  observed  in  neurophysiolog(cid:173) ical  recording  data,  and  it  predicts  a  functional  relation  between  uni- and bimodal levels  of discriminability that is  testable both in  neurophysiological and behavioral experiments.
Model-Free Least-Squares Policy Iteration
Authors: Michail G. Lagoudakis, Ronald Parr
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. We are motivated by the least squares temporal difference learning algorithm (LSTD), which is known for its efﬁcient use of sample experiences compared to pure temporal difference algorithms. LSTD is ideal for prediction problems, however it heretofore has not had a straightforward application to control problems. Moreover, approximations learned by LSTD are strongly inﬂuenced by the visitation distribution over states. Our new algorithm, Least Squares Policy Iteration (LSPI) addresses these issues. The result is an off-policy method which can use (or reuse) data collected from any source. We have tested LSPI on several problems, including a bicycle simulator in which it learns to guide the bicycle to a goal efﬁciently by merely observing a relatively small number of completely random trials.
Real Time Image Saliency for Black Box Classifiers
Authors: Piotr Dabkowski, Yarin Gal
Published in: Advances in Neural Information Processing Systems 30  (NIPS 2017) (Main Conference Track), 2017

Abstract: In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier. We train a masking model to manipulate the scores of the classifier by masking salient parts of the input image. Our model generalises well to unseen images and requires a single forward pass to perform saliency detection, therefore suitable for use in real-time systems. We test our approach on CIFAR-10 and ImageNet datasets and show that the produced saliency maps are easily interpretable, sharp, and free of artifacts. We suggest a new metric for saliency and test our method on the ImageNet object localisation task. We achieve results outperforming other weakly supervised methods.
Joint distribution optimal transportation for domain adaptation
Authors: Nicolas Courty, Rémi Flamary, Amaury Habrard, Alain Rakotomamonjy
Published in: Advances in Neural Information Processing Systems 30  (NIPS 2017) (Main Conference Track), 2017

Abstract: This paper deals with the unsupervised domain adaptation problem, where one wants to estimate a prediction function $f$ in a given target domain without any labeled sample by exploiting the knowledge available from a source domain where labels are known. Our work makes the following assumption: there exists a  non-linear transformation between the joint feature/label space distributions of the two domain $\ps$ and $\pt$. We propose a solution of this problem with optimal transport, that allows to recover an estimated target $\pt^f=(X,f(X))$ by optimizing simultaneously the optimal coupling and $f$. We show that our method corresponds to the minimization of a bound on the target error, and provide an efficient algorithmic solution, for which convergence is proved. The versatility of our approach, both in terms of class of hypothesis or loss functions is demonstrated with real world classification and regression problems, for which we reach or surpass state-of-the-art results.
Learning A Structured Optimal Bipartite Graph for Co-Clustering
Authors: Feiping Nie, Xiaoqian Wang, Cheng Deng, Heng Huang
Published in: Advances in Neural Information Processing Systems 30  (NIPS 2017) (Main Conference Track), 2017

Abstract: Co-clustering methods have been widely applied to document clustering and gene expression analysis. These methods make use of the duality between features and samples such that the co-occurring structure of sample and feature clusters can be extracted. In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples. Most existing co-clustering methods conduct clustering on the graph achieved from the original data matrix, which doesn’t have explicit cluster structure, thus they require a post-processing step to obtain the clustering results. In this paper, we propose a novel co-clustering method to learn a bipartite graph with exactly k connected components, where k is the number of clusters. The new bipartite graph learned in our model approximates the original graph but maintains an explicit cluster structure, from which we can immediately get the clustering results without post-processing. Extensive empirical results are presented to verify the effectiveness and robustness of our model.
Learning to Inpaint for Image Compression
Authors: Mohammad Haris Baig, Vladlen Koltun, Lorenzo Torresani
Published in: Advances in Neural Information Processing Systems 30  (NIPS 2017) (Main Conference Track), 2017

Abstract: We study the design of deep architectures for lossy image compression. We present two architectural recipes in the context of multi-stage progressive encoders  and empirically demonstrate their importance on compression performance. Specifically, we show that: 1) predicting the original image data from residuals in a multi-stage progressive architecture facilitates learning and leads to improved performance at approximating the original content and 2) learning to inpaint (from neighboring image pixels) before performing compression reduces the amount of information that must be stored to achieve a high-quality approximation. Incorporating these design choices in a baseline progressive encoder yields an average reduction of over 60% in file size with similar quality compared to the original residual encoder.
Inverse Filtering for Hidden Markov Models
Authors: Robert Mattila, Cristian Rojas, Vikram Krishnamurthy, Bo Wahlberg
Published in: Advances in Neural Information Processing Systems 30  (NIPS 2017) (Main Conference Track), 2017

Abstract: This paper considers a number of related inverse filtering problems for hidden Markov models (HMMs). In particular, given a sequence of state posteriors and the system dynamics; i) estimate the corresponding sequence of observations, ii) estimate the observation likelihoods, and iii) jointly estimate the observation likelihoods and the observation sequence. We show how to avoid a computationally expensive mixed integer linear program (MILP) by exploiting the algebraic structure of the HMM filter using simple linear algebra operations, and provide conditions for when the quantities can be uniquely reconstructed. We also propose a solution to the more general case where the posteriors are noisily observed. Finally, the proposed inverse filtering algorithms are evaluated on real-world polysomnographic data used for automatic sleep segmentation.
On clustering network-valued data
Authors: Soumendu Sundar Mukherjee, Purnamrita Sarkar, Lizhen Lin
Published in: Advances in Neural Information Processing Systems 30  (NIPS 2017) (Main Conference Track), 2017

Abstract: Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a great deal of attention in the  research community. While being able to cluster within a network is important, there are emerging needs to be able to \emph{cluster multiple networks}. This is largely motivated by the routine collection of network data that are generated from potentially different populations. These networks may or may not have node correspondence. When node correspondence is present, we cluster networks by summarizing a network by its graphon estimate, whereas when node correspondence is not present, we propose a novel solution for clustering such networks by associating a computationally feasible feature vector to each network based on trace of powers of the adjacency matrix. We illustrate our methods using both simulated and real data sets, and theoretical justifications are provided in terms of consistency.
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks
Authors: Nanyang Ye, Zhanxing Zhu, Rafal Mantiuk
Published in: Advances in Neural Information Processing Systems 30  (NIPS 2017) (Main Conference Track), 2017

Abstract: Minimizing non-convex and high-dimensional objective functions is challenging, especially when training modern deep neural networks.  In this paper, a novel approach is proposed which divides the training process into two consecutive phases to obtain better generalization performance: Bayesian sampling and stochastic optimization. The first phase is to explore the energy landscape and to capture the `fat'' modes; and the second one is to fine-tune the parameter learned from the first phase. In the Bayesian learning phase, we apply continuous tempering and stochastic approximation into the Langevin dynamics to create an efficient and effective sampler, in which the temperature is adjusted automatically according to the designed ``temperature dynamics''.  These strategies can overcome the challenge of early trapping into bad local minima and have achieved remarkable improvements in various types of neural networks as shown in our theoretical analysis and empirical experiments.
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization
Authors: Omar El Housni, Vineet Goyal
Published in: Advances in Neural Information Processing Systems 30  (NIPS 2017) (Main Conference Track), 2017

Abstract: Affine policies (or control) are widely used as a solution approach in dynamic optimization where computing an optimal adjustable solution is usually intractable. While the worst case performance of affine policies can be significantly bad, the empirical performance is observed to be near-optimal for a large class of problem instances. For instance, in the two-stage dynamic robust optimization problem with linear covering constraints and uncertain right hand side, the worst-case approximation bound for affine policies is $O(\sqrt m)$ that is also tight (see Bertsimas and Goyal (2012)), whereas observed empirical performance is near-optimal. In this paper, we aim to address this stark-contrast between the worst-case and the empirical performance of affine policies. In particular, we  show that affine policies give a good approximation for the two-stage adjustable robust optimization problem with high probability on random instances where the constraint coefficients are generated i.i.d. from a large class of distributions; thereby, providing a theoretical justification of the observed empirical performance. On the other hand, we also present a distribution such that the performance bound for affine policies on instances generated according to that distribution is $\Omega(\sqrt m)$ with high probability; however, the constraint coefficients are not i.i.d.. This demonstrates that the empirical performance of affine policies can depend on the generative model for instances.