mtybilly/Instruct2DS huggingface dataset:
Accelerated Mirror Descent in Continuous and Discrete Time
Authors: Walid Krichene, Alexandre Bayen, Peter L Bartlett
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: We study accelerated mirror descent dynamics in continuous and discrete time. Combining the original continuous-time motivation of mirror descent with a recent ODE interpretation of Nesterov's accelerated method, we propose a family of continuous-time descent dynamics for convex functions with Lipschitz gradients, such that the solution trajectories are guaranteed to converge to the optimum at a $O(1/t^2)$ rate. We then show that a large family of first-order accelerated methods can be obtained as a discretization of the ODE, and these methods converge at a $O(1/k^2)$ rate. This connection between accelerated mirror descent and the ODE provides an intuitive approach to the design and analysis of accelerated first-order algorithms.
Accelerated Proximal Gradient Methods for Nonconvex Programming
Authors: Huan Li, Zhouchen Lin
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: Nonconvex and nonsmooth problems have recently received considerable attention in signal/image processing, statistics and machine learning. However, solving the nonconvex and nonsmooth optimization problems remains a big challenge. Accelerated proximal gradient (APG) is an excellent method for convex programming. However, it is still unknown whether the usual APG can ensure the convergence to a critical point in nonconvex programming. To address this issue, we introduce a monitor-corrector step and extend APG for general nonconvex and nonsmooth programs. Accordingly, we propose a monotone APG and a non-monotone APG. The latter waives the requirement on monotonic reduction of the objective function and needs less computation in each iteration. To the best of our knowledge, we are the first to provide APG-type algorithms for general nonconvex and nonsmooth problems ensuring that every accumulation point is a critical point, and the convergence rates remain $O(1/k^2)$ when the problems are convex, in which k is the number of iterations. Numerical results testify to the advantage of our algorithms in speed.
Monotone k-Submodular Function Maximization with Size Constraints
Authors: Naoto Ohsaka, Yuichi Yoshida
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: A $k$-submodular function is a generalization of a submodular function, where the input consists of $k$ disjoint subsets, instead of a single subset, of the domain.Many machine learning problems, including influence maximization with $k$ kinds of topics and sensor placement with $k$ kinds of sensors, can be naturally modeled as the problem of maximizing monotone $k$-submodular functions.In this paper, we give constant-factor approximation algorithms for maximizing monotone $k$-submodular functions subject to several size constraints.The running time of our algorithms are almost linear in the domain size.We experimentally demonstrate that our algorithms outperform baseline algorithms in terms of the solution quality.
Spherical Random Features for Polynomial Kernels
Authors: Jeffrey Pennington, Felix Xinnan X Yu, Sanjiv Kumar
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: Compact explicit feature maps provide a practical framework to scale kernel methods to large-scale learning, but deriving such maps for many types of kernels remains a challenging open problem. Among the commonly used kernels for nonlinear classification are polynomial kernels, for which low approximation error has thus far necessitated explicit feature maps of large dimensionality, especially for higher-order polynomials. Meanwhile, because polynomial kernels are unbounded, they are frequently applied to data that has been normalized to unit l2 norm. The question we address in this work is: if we know a priori that data is so normalized, can we devise a more compact map? We show that a putative affirmative answer to this question based on Random Fourier Features is impossible in this setting, and introduce a new approximation paradigm, Spherical Random Fourier (SRF) features, which circumvents these issues and delivers a compact approximation to polynomial kernels for data on the unit sphere. Compared to prior work, SRF features are less rank-deficient, more compact, and achieve better kernel approximation, especially for higher-order polynomials. The resulting predictions have lower variance and typically yield better classification accuracy.
A Dual Augmented Block Minimization Framework for Learning with Limited Memory
Authors: Ian En-Hsu Yen, Shan-Wei Lin, Shou-De Lin
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: In past few years, several techniques have been proposed for training of linear Support Vector Machine (SVM) in limited-memory setting, where a dual block-coordinate descent (dual-BCD) method was used to balance cost spent on I/O and computation. In this paper, we consider the more general setting of regularized \emph{Empirical Risk Minimization (ERM)} when data cannot fit into memory. In particular, we generalize the existing block minimization framework based on strong duality and \emph{Augmented Lagrangian} technique to achieve global convergence for ERM with arbitrary convex loss function and regularizer. The block minimization framework is flexible in the sense that, given a solver working under sufficient memory, one can integrate it with the framework to obtain a solver globally convergent under limited-memory condition. We conduct experiments on L1-regularized classification and regression problems to corroborate our convergence theory and compare the proposed framework to  algorithms adopted from online and distributed settings, which shows superiority of the proposed approach on data of size ten times larger than the memory capacity.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Authors: David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, Ryan P. Adams
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: We introduce a convolutional neural network that operates directly on graphs.These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape.The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints.We show that these data-driven features are more interpretable, and have better predictive performance on a variety of tasks.
Decomposition Bounds for Marginal MAP
Authors: Wei Ping, Qiang Liu, Alex Ihler
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: Marginal MAP inference involves making MAP predictions in systems defined with latent variables or missing information. It is significantly more difficult than pure marginalization and MAP tasks, for which a large class of efficient and convergent variational algorithms, such as dual decomposition, exist.  In this work, we generalize dual decomposition to a generic powered-sum inference task, which includes marginal MAP, along with pure marginalization and MAP, as special cases.  Our method is based on a block coordinate descent algorithm on a new convex decomposition bound, that is guaranteed to converge monotonically, and can be parallelized efficiently.  We demonstrate our approach on various inference queries over real-world problems from the UAI approximate inference challenge, showing that our framework is faster and more reliable than previous methods.
The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions
Authors: Sebastian Bitzer, Stefan Kiebel
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: In simple perceptual decisions the brain has to identify a stimulus based on noisy sensory samples from the stimulus. Basic statistical considerations state that the reliability of the stimulus information, i.e., the amount of noise in the samples, should be taken into account when the decision is made. However, for perceptual decision making experiments it has been questioned whether the brain indeed uses the reliability for making decisions when confronted with unpredictable changes in stimulus reliability. We here show that even the basic drift diffusion model, which has frequently been used to explain experimental findings in perceptual decision making, implicitly relies on estimates of stimulus reliability. We then show that only those variants of the drift diffusion model which allow stimulus-specific reliabilities are consistent with neurophysiological findings. Our analysis suggests that the brain estimates the reliability of the stimulus on a short time scale of at most a few hundred milliseconds.
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question
Authors: Haoyuan Gao, Junhua Mao, Jie Zhou, Zhiheng Huang, Lei Wang, Wei Xu
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: In this paper, we present the mQA model, which is able to answer questions about the content of an image. The answer can be a sentence, a phrase or a single word. Our model contains four components: a Long Short-Term Memory (LSTM) to extract the question representation, a Convolutional Neural Network (CNN) to extract the visual representation, an LSTM for storing the linguistic context in an answer, and a fusing component to combine the information from the first three components and generate the answer. We construct a Freestyle Multilingual Image Question Answering (FM-IQA) dataset to train and evaluate our mQA model. It contains over 150,000 images and 310,000 freestyle Chinese question-answer pairs and their English translations. The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test. Specifically, we mix the answers provided by humans and our model. The human judges need to distinguish our model from the human. They will also provide a score (i.e. 0, 1, 2, the larger the better) indicating the quality of the answer. We propose strategies to monitor the quality of this evaluation process. The experiments show that in 64.7% of cases, the human judges cannot distinguish our model from humans. The average score is 1.454 (1.918 for human). The details of this work, including the FM-IQA dataset, can be found on the project page: \url{http://idl.baidu.com/FM-IQA.html}.
On the Pseudo-Dimension of Nearly Optimal Auctions
Authors: Jamie H Morgenstern, Tim Roughgarden
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: This paper develops a general approach, rooted in statistical learning theory, to learning an approximately revenue-maximizing auction from data. We introduce t-level auctions to interpolate between simple auctions, such as welfare maximization with reserve prices, and optimal auctions, thereby balancing the competing demands of expressivity and simplicity. We prove that such auctions have small representation error, in the sense that for every product distribution F over bidders’ valuations, there exists a t-level auction with small t and expected revenue close to optimal. We show that the set of t-level auctions has modest pseudo-dimension (for polynomial t) and therefore leads to small learning error. One consequence of our results is that, in arbitrary single-parameter settings, one can learn a mechanism with expected revenue arbitrarily close to optimal from a polynomial number of samples.
Fast Second Order Stochastic Backpropagation for Variational Inference
Authors: Kai Fan, Ziteng Wang, Jeff Beck, James Kwok, Katherine A. Heller
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: We propose a second-order (Hessian or Hessian-free) based optimization method for variational inference inspired by Gaussian backpropagation, and argue that quasi-Newton optimization can be developed as well.  This is accomplished by generalizing the gradient computation in stochastic backpropagation via a reparametrization trick with lower complexity. As an illustrative example, we apply this approach to the problems of Bayesian logistic regression and variational auto-encoder (VAE). Additionally, we compute bounds on the estimator variance of intractable expectations for the family  of Lipschitz continuous function. Our method is practical, scalable and model free. We demonstrate our method on several real-world datasets and provide comparisons with other stochastic gradient methods to show substantial enhancement in convergence rates.
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions
Authors: Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada, Takeshi Yamada
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: We propose a kernel-based method for finding matching between instances across different domains, such as multilingual documents and images with annotations. Each instance is assumed to be represented as a multiset of features, e.g., a bag-of-words representation for documents. The major difficulty in finding cross-domain relationships is that the similarity between instances in different domains cannot be directly measured. To overcome this difficulty, the proposed method embeds all the features of different domains in a shared latent space, and regards each instance as a distribution of its own features in the shared latent space. To represent the distributions efficiently and nonparametrically, we employ the framework of the kernel embeddings of distributions. The embedding is estimated so as to minimize the difference between distributions of paired instances while keeping unpaired instances apart. In our experiments, we show that the proposed method can achieve high performance on finding correspondence between multi-lingual Wikipedia articles, between documents and tags, and between images and tags.
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring
Authors: Gunwoong Park, Garvesh Raskutti
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: In this paper, we address the question of identifiability and learning algorithms for large-scale Poisson Directed Acyclic Graphical (DAG) models. We define general Poisson DAG models as models where each node is a Poisson random variable with rate parameter depending on the values of the parents in the underlying DAG. First, we prove that Poisson DAG models are identifiable from observational data, and present a polynomial-time algorithm that learns the Poisson DAG model under suitable regularity conditions. The main idea behind our algorithm is based on overdispersion, in that variables that are conditionally Poisson are overdispersed relative to variables that are marginally Poisson. Our algorithms exploits overdispersion along with methods for learning sparse Poisson undirected graphical models for faster computation. We provide both theoretical guarantees and simulation results for both small and large-scale DAGs.
Local Causal Discovery of Direct Causes and Effects
Authors: Tian Gao, Qiang Ji
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: We focus on the discovery and identification of direct causes and effects of a target variable in a causal network. State-of-the-art algorithms generally need to find the global causal structures in the form of complete partial directed acyclic graphs in order to identify the direct causes and effects of a target variable. While these algorithms are effective, it is often unnecessary and wasteful to find the global structures when we are only interested in one target variable (such as class labels). We propose a new local causal discovery algorithm, called Causal Markov Blanket (CMB), to identify the direct causes and effects of a target variable based on Markov Blanket Discovery. CMB is designed to conduct causal discovery among multiple variables, but focuses only on finding causal relationships between a specific target variable and other variables. Under standard assumptions, we show both theoretically and experimentally that the proposed local causal discovery algorithm can obtain the comparable identification accuracy as global methods but significantly improve their efficiency, often by more than one order of magnitude.
Recognizing retinal ganglion cells in the dark
Authors: Emile Richard, Georges A Goetz, E. J. Chichilnisky
Published in: Advances in Neural Information Processing Systems 28  (NIPS 2015) (Main Conference Track), 2015

Abstract: Many neural circuits are composed of numerous distinct cell types that perform different operations on their inputs, and send their outputs to distinct targets.  Therefore, a key step in understanding neural systems is to reliably distinguish  cell types.  An important example is the retina, for which present-day techniques for identifying cell types are accurate, but very labor-intensive.  Here, we develop automated classifiers for functional identification of retinal ganglion cells, the output neurons of the retina, based solely on recorded voltage patterns on a large scale array.  We use per-cell classifiers based on features extracted from electrophysiological images (spatiotemporal voltage waveforms) and interspike intervals (autocorrelations). These classifiers achieve high performance in distinguishing between the major ganglion cell classes of the primate retina, but fail in achieving the same accuracy in predicting cell polarities (ON vs. OFF).  We then show how to use indicators of functional coupling within populations of ganglion cells (cross-correlation) to infer cell polarities with a matrix completion algorithm.  This can result in accurate, fully automated methods for cell type classification.
Backpropagation without Multiplication
Authors: Patrice Y. Simard, Hans Peter Graf
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: The back propagation algorithm has been modified to work with(cid:173) out any multiplications and to tolerate comput.ations with a low  resolution, which makes it. more attractive for a hardware imple(cid:173) mentatioll. Numbers are represented in float.ing point format with  1 bit mantissa and 3 bits in the exponent for the states, and 1 bit  mantissa and 5 bit exponent. for the gradients, while the weights are  16 bit fixed-point numbers. In this way, all the computations can  be executed with shift and add operations. Large nehvorks with  over 100,000 weights were t.rained and demonstrat.ed the same per(cid:173) formance as networks comput.ed with full precision. An estimate of  a circuit implementatioll shows that a large network can be placed  on a single chip , reaching more t.han 1 billion weight updat.es pel'  second. A speedup is also obtained on any machine where a mul(cid:173) tiplication is slower than a shift operat.ioJl.
Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation
Authors: Oded Maron, Andrew W. Moore
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Selecting  a good  model of a set  of input points by  cross  validation  is  a  computationally intensive  process,  especially  if the  number of  possible  models  or  the  number  of training  points  is  high.  Tech(cid:173) niques  such  as  gradient  descent  are  helpful  in  searching  through  the space of models,  but problems such  as  local minima, and more  importantly, lack  of a  distance  metric  between  various  models re(cid:173) duce  the applicability of these search  methods.  Hoeffding  Races  is  a  technique  for  finding  a  good  model  for  the  data by  quickly  dis(cid:173) carding bad models, and concentrating the computational effort  at  differentiating between  the better  ones.  This paper focuses  on  the  special  case  of leave-one-out  cross  validation  applied  to  memory(cid:173) based  learning  algorithms,  but  we  also  argue  that  it is  applicable  to any  class  of model selection  problems.
Cross-Validation Estimates IMSE
Authors: Mark Plutowski, Shinichi Sakata, Halbert White
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

Abstract: Integrated  Mean  Squared  Error  (IMSE)  is  a  version  of the  usual  mean  squared  error  criterion,  averaged  over  all  possible  training  If it  could  be  observed,  it  could  be  used  sets  of  a  given  size.  to  determine  optimal  network  complexity  or  optimal  data  sub(cid:173) sets for  efficient  training.  We  show  that two  common methods of  cross-validating  average  squared  error  deliver  unbiased  estimates  of IMSE,  converging  to  IMSE  with  probability  one.  These  esti(cid:173) mates thus make possible  approximate IMSE-based  choice  of net(cid:173) work complexity.  We also show that two variants of cross validation  measure provide unbiased IMSE-based estimates potentially useful  for  selecting optimal data subsets. 
1  Summary  To  begin,  assume  we  are  given  a  fixed  network  architecture.  (We  dispense  with  this  assumption  later.)  Let  zN  denote  a  given  set  of N  training  examples.  Let  QN(zN) denote the expected squared error  (the expectation taken over all possible  examples) of the network  after  being trained on  zN.  This measures  the  quality of  fit  afforded by  training on  a  given set of N  examples.  Let  IMSEN  denote  the  Integrated  Mean  Squared  Error  for  training  sets  of size  N.  Given  reasonable  assumptions,  it  is  straightforward  to  show  that  IMSEN  =  E[Q N(ZN)] - 0"2,  where  the expectation is  now  over  all training sets of size  N,  ZN  is  a  random training set of size  N, and 0"2  is the noise variance.  Let  CN  = CN(zN)  denote  the  "delete-one  cross-validation"  squared  error  measure  for  a network  trained on zN.  CN  is obtained by training networks on each of the N  training sets of size N -1 obtained by deleting a single example; the measure follows
The Power of Amnesia
Authors: Dana Ron, Yoram Singer, Naftali Tishby
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

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

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

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

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

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

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

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

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

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

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

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

Abstract: ,,"'e do not have a good understanding of how  theoretical principles  of learning are realized in neural systems.  To address this problem  we  built a  computational model of development in the owl's sound  localization  system.  The  structure  of  the  model  is  drawn  from  known  experimental data while  the  learning principles  come from  recent  work  in  the  field  of  brain  style  computation.  The  model  accounts  for  numerous  properties  of the  owl's  sound  localization  system,  makes specific  and  testable  predictions  for  future  experi(cid:173) ments, and provides a  theory of the  developmental process.
Grammatical Inference by Attentional Control of Synchronization in an Oscillating Elman Network
Authors: Bill Baird, Todd Troyer, Frank Eeckman
Published in: Advances in Neural Information Processing Systems 6  (NIPS 1993) (Main Conference Track), 1993

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

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

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

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

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

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

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

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

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

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

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

Abstract: The fundamental backpropagation (BP) algorithm for training ar(cid:173) tificial neural networks is cast as a deterministic nonmonotone per(cid:173) turbed gradient method. Under certain natural assumptions, such  as the series of learning rates diverging while the series of their  squares converging, it is established that every accumulation point  of the online BP iterates is a stationary point of the BP error func(cid:173) tion. The results presented cover serial and parallel online BP,  modified BP with a momentum term, and BP with weight decay.