mtybilly/Instruct2DS huggingface dataset:
Recognizing Evoked Potentials in a Virtual Environment
Authors: Dana H. Ballard Jessica D. Bayliss
Published in: Advances in Neural Information Processing Systems 12  (NIPS 1999) (Main Conference Track), 1999

Abstract: Virtual  reality  (VR)  provides  immersive  and  controllable  experimen(cid:173) tal  environments.  It expands  the  bounds  of possible  evoked  potential  (EP)  experiments by  providing complex,  dynamic  environments in  or(cid:173) der  to  study  cognition  without  sacrificing  environmental  control.  VR  also serves as a safe dynamic testbed for brain-computer .interface (BCl)  research.  However, there has been some concern about detecting EP sig(cid:173) nals in a complex VR environment.  This paper shows that EPs exist at  red,  green, and yellow stop lights in  a virtual driving environment.  Ex(cid:173) perimental results show  the existence of the  P3  EP at  "go" and  "stop"  lights and  the contingent negative variation (CNY)  EP  at "slow down"  lights.  In order to  test the  feasibility  of on-line recognition in  VR,  we  looked at recognizing the P3 EP at red stop tights and the absence of this  signal at yellow slow down lights.  Recognition results show that the P3  may successfully be used to control the brakes of a VR car at stop lights.
Means, Correlations and Bounds
Authors: Bert Kappen Martijn Leisink
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The  partition function  for  a  Boltzmann  machine  can  be  bounded  from  above  and  below.  We  can  use  this  to  bound  the  means  and  the  correlations.  For  networks  with  small  weights,  the  values  of  these statistics can be restricted to non-trivial regions  (i.e.  a subset  of  [-1 , 1]).  Experimental  results  show  that  reasonable  bounding  occurs  for  weight sizes  where  mean field  expansions  generally  give  good  results.
Fragment Completion in Humans and Machines
Authors: Bas Rokers, Archisman Rudra, Zili Liu David Jacobs
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Partial information can trigger a complete memory. At the same time, human memory is not perfect. A cue can contain enough information to specify an item in memory, but fail to trigger that item. In the context of word memory, we present experiments that demonstrate some basic patterns in human memory errors. We use cues that consist of word frag- ments. We show that short and long cues are completed more accurately than medium length ones and study some of the factors that lead to this behavior. We then present a novel computational model that shows some of the ﬂexibility and patterns of errors that occur in human memory. This model iterates between bottom-up and top-down computations. These are tied together using a Markov model of words that allows memory to be accessed with a simple feature set, and enables a bottom-up process to compute a probability distribution of possible completions of word frag- ments, in a manner similar to models of visual perceptual completion.
Eye movements and the maturation of cortical orientation selectivity
Authors: Michele Rucci Antonino Casile
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Neural activity appears to be a crucial component for shaping the recep- tive ﬁelds of cortical simple cells into adjacent, oriented subregions alter- nately receiving ON- and OFF-center excitatory geniculate inputs. It is known that the orientation selective responses of V1 neurons are reﬁned by visual experience. After eye opening, the spatiotemporal structure of neural activity in the early stages of the visual pathway depends both on the visual environment and on how the environment is scanned. We have used computational modeling to investigate how eye movements might affect the reﬁnement of the orientation tuning of simple cells in the pres- ence of a Hebbian scheme of synaptic plasticity. Levels of correlation be- tween the activity of simulated cells were examined while natural scenes were scanned so as to model sequences of saccades and ﬁxational eye movements, such as microsaccades, tremor and ocular drift. The speciﬁc patterns of activity required for a quantitatively accurate development of simple cell receptive ﬁelds with segregated ON and OFF subregions were observed during ﬁxational eye movements, but not in the presence of saccades or with static presentation of natural visual input. These re- sults suggest an important role for the eye movements occurring during visual ﬁxation in the reﬁnement of orientation selectivity.
Orientation-Selective aVLSI Spiking Neurons
Authors: Jörg Kramer, Giacomo Indiveri, Tobi Delbrück, Rodney J. Douglas Shih-Chii Liu
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We describe a programmable multi-chip VLSI neuronal system that can be used for exploring spike-based information processing models. The system consists of a silicon retina, a PIC microcontroller, and a transceiver chip whose integrate-and-ﬁre neurons are connected in a soft winner-take-all architecture. The circuit on this multi-neuron chip ap- proximates a cortical microcircuit. The neurons can be conﬁgured for different computational properties by the virtual connections of a se- lected set of pixels on the silicon retina. The virtual wiring between the different chips is effected by an event-driven communication pro- tocol that uses asynchronous digital pulses, similar to spikes in a neu- ronal system. We used the multi-chip spike-based system to synthe- size orientation-tuned neurons using both a feedforward model and a feedback model. The performance of our analog hardware spiking model matched the experimental observations and digital simulations of continuous-valued neurons. The multi-chip VLSI system has advantages over computer neuronal models in that it is real-time, and the computa- tional time does not scale with the size of the neuronal network.
On the Generalization Ability of On-Line Learning Algorithms
Authors: Alex Conconi, Claudio Gentile Nicolò Cesa-bianchi
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: In this paper we show that on-line algorithms for classiﬁcation and re- gression can be naturally used to obtain hypotheses with good data- dependent tail bounds on their risk. Our results are proven without re- quiring complicated concentration-of-measure arguments and they hold for arbitrary on-line learning algorithms. Furthermore, when applied to concrete on-line algorithms, our results yield tail bounds that in many cases are comparable or better than the best known bounds.
Analysis of Sparse Bayesian Learning
Authors: Michael E. Tipping Anita C. Faul
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The recent introduction of the 'relevance vector machine' has effec(cid:173) tively  demonstrated  how  sparsity  may  be obtained  in  generalised  linear  models  within  a  Bayesian  framework.  Using  a  particular  form  of Gaussian  parameter prior,  'learning'  is  the  maximisation,  with respect  to hyperparameters, of the  marginal  likelihood  of the  data.  This  paper  studies  the  properties  of  that  objective  func(cid:173) tion,  and  demonstrates  that  conditioned  on  an  individual  hyper(cid:173) parameter,  the  marginal  likelihood  has  a  unique  maximum which  is  computable in closed form.  It is  further  shown that if a  derived  'sparsity criterion'  is  satisfied,  this maximum is  exactly  equivalent  to  'pruning' the corresponding parameter from  the model.
A Hierarchical Model of Complex Cells in Visual Cortex for the Binocular Perception of Motion-in-Depth
Authors: Fabio Solari, Giulia Andreani, Chiara Bartolozzi, Giacomo M. Bisio Silvio P. Sabatini
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: A cortical model for  motion-in-depth selectivity of complex cells in  the  visual  cortex  is  proposed.  The  model  is  based  on  a  time  ex(cid:173) tension of the phase-based techniques for  disparity estimation.  We  consider  the  computation  of the  total  temporal  derivative  of the  time-varying disparity through the combination of the responses of  disparity energy units.  To take into account the physiological plau(cid:173) sibility, the model  is  based on the  combinations of binocular cells  characterized by different  ocular dominance indices.  The resulting  cortical units  of the model show  a  sharp selectivity for  motion-in(cid:173) depth that has been compared with that reported in the literature  for  real cortical cells.
Motivated Reinforcement Learning
Authors: Peter Dayan
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The  standard reinforcement learning view  of the involvement  of neuromodulatory systems in instrumental conditioning in(cid:173) cludes  a  rather  straightforward  conception  of  motivation  as  prediction of sum future reward.  Competition between actions  is based on the motivating characteristics of their consequent  states in this sense. Substantial, careful, experiments reviewed  in Dickinson & Balleine, 12,13 into the neurobiology and psychol(cid:173) ogy of motivation shows that this view is incomplete. In many  cases, animals are faced with the choice not between many dif(cid:173) ferent actions  at a given state, but rather whether a single re(cid:173) sponse is  worth  executing  at  all.  Evidence  suggests  that  the  motivational process underlying  this choice has different psy(cid:173) chological  and neural properties from  that underlying  action  choice.  We  describe  and  model  these  motivational  systems,  and consider the way they interact.
Categorization by Learning and Combining Object Parts
Authors: Thomas Serre, Massimiliano Pontil, Thomas Vetter, Tomaso Poggio Bernd Heisele
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We describe an algorithm for automatically learning discriminative com- ponents of objects with SVM classiﬁers. It is based on growing image parts by minimizing theoretical bounds on the error probability of an SVM. Component-based face classiﬁers are then combined in a second stage to yield a hierarchical SVM classiﬁer. Experimental results in face classiﬁcation show considerable robustness against rotations in depth and suggest performance at signiﬁcantly better level than other face detection systems. Novel aspects of our approach are: a) an algorithm to learn component-based classiﬁcation experts and their combination, b) the use of 3-D morphable models for training, and c) a maximum operation on the output of each component classiﬁer which may be relevant for bio- logical models of visual recognition.
Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway
Authors: Amir Globerson, M. J. Anderson, E. D. Young, Israel Nelken, Naftali Tishby Gal Chechik
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The  way  groups  of auditory  neurons  interact  to  code  acoustic  in(cid:173) formation is  investigated using an information theoretic approach.  We develop measures of redundancy among groups of neurons, and  apply  them  to  the  study  of collaborative  coding  efficiency  in  two  processing stations in the auditory pathway:  the inferior colliculus  (IC)  and the primary auditory cortex (AI).  Under two schemes for  the  coding  of the  acoustic  content,  acoustic  segments  coding  and  stimulus  identity  coding,  we  show  differences  both in  information  content and group redundancies between IC and AI neurons.  These  results  provide for  the first  time  a  direct  evidence for  redundancy  reduction  along  the  ascending  auditory  pathway,  as  has  been  hy(cid:173) pothesized for  theoretical considerations  [Barlow  1959,2001].  The  redundancy effects under the single-spikes coding scheme are signif(cid:173) icant only for  groups larger than ten cells, and cannot be revealed  with  the  redundancy  measures  that  use  only  pairs  of cells.  The  results  suggest  that the  auditory system transforms low  level  rep(cid:173) resentations that contain redundancies due to the statistical struc(cid:173) ture of natural stimuli, into a  representation in which cortical neu(cid:173) rons extract rare and independent component of complex acoustic  signals,  that are useful  for  auditory scene analysis.
Probabilistic Inference of Hand Motion from Neural Activity in Motor Cortex
Authors: Michael J. Black, Elie Bienenstock, Shy Shoham, John P. Donoghue Yun Gao
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Statistical learning and probabilistic inference techniques are used to in- fer the hand position of a subject from multi-electrode recordings of neu- ral activity in motor cortex. First, an array of electrodes provides train- ing data of neural ﬁring conditioned on hand kinematics. We learn a non- parametric representation of this ﬁring activity using a Bayesian model and rigorously compare it with previous models using cross-validation. Second, we infer a posterior probability distribution over hand motion conditioned on a sequence of neural test data using Bayesian inference. The learned ﬁring models of multiple cells are used to deﬁne a non- Gaussian likelihood term which is combined with a prior probability for the kinematics. A particle ﬁltering method is used to represent, update, and propagate the posterior distribution over time. The approach is com- pared with traditional linear ﬁltering methods; the results suggest that it may be appropriate for neural prosthetic applications.
The Noisy Euclidean Traveling Salesman Problem and Learning
Authors: Joachim M. Buhmann Mikio L. Braun
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We  consider  noisy  Euclidean  traveling  salesman  problems  in  the  plane,  which  are  random combinatorial problems  with  underlying  structure. Gibbs sampling is  used to compute average trajectories,  which  estimate  the  underlying structure common  to all  instances.  This procedure requires identifying the exact relationship between  permutations and tours.  In  a  learning setting,  the  average trajec(cid:173) tory  is  used  as  a  model  to  construct  solutions  to  new  instances  sampled from the same source. Experimental results show that the  average trajectory can in fact estimate the underlying structure and  that overfitting effects occur if the trajectory adapts too closely to  a  single instance.
Incorporating Invariances in Non-Linear Support Vector Machines
Authors: Bernhard Schölkopf Olivier Chapelle
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The  choice  of an  SVM  kernel  corresponds  to  the  choice  of a  rep(cid:173) resentation  of  the  data  in  a  feature  space  and,  to  improve  per(cid:173) formance,  it  should  therefore incorporate prior knowledge  such  as  known  transformation invariances.  We  propose  a  technique which  extends earlier work and aims at incorporating invariances in  non(cid:173) linear  kernels.  We  show  on  a  digit  recognition  task that the  pro(cid:173) posed  approach is  superior to the Virtual Support Vector method,  which  previously had been the method of choice.
Sampling Techniques for Kernel Methods
Authors: Frank Mcsherry, Bernhard Schölkopf Dimitris Achlioptas
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained ap- proximations. Rather intriguingly, all three techniques can be viewed as instantiations of the following idea: replace the kernel function  by a “randomized kernel” which behaves like
Reinforcement Learning and Time Perception -- a Model of Animal Experiments
Authors: J. Wearden Jonathan L. Shapiro
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Animal  data on delayed-reward conditioning experiments  shows  a  striking property - the  data for  different  time intervals  collapses  into  a  single  curve  when  the  data is  scaled  by  the  time  interval.  This is  called the scalar property of interval timing.  Here a  simple  model of a  neural  clock is  presented and shown to give  rise  to the  scalar  property.  The model  is  an accumulator consisting of noisy,  linear  spiking  neurons.  It  is  analytically  tractable  and  contains  only three parameters.  When coupled with reinforcement learning  it simulates peak procedure experiments, producing both the scalar  property and the pattern of single  trial covariances.
Hyperbolic Self-Organizing Maps for Semantic Navigation
Authors: Helge Ritter Jorg Ontrup
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We introduce a new type of Self-Organizing Map (SOM) to navigate in the Semantic Space of large text collections. We propose a “hyper- bolic SOM” (HSOM) based on a regular tesselation of the hyperbolic plane, which is a non-euclidean space characterized by constant negative gaussian curvature. The exponentially increasing size of a neighborhood around a point in hyperbolic space provides more freedom to map the complex information space arising from language into spatial relations. We describe experiments, showing that the HSOM can successfully be applied to text categorization tasks and yields results comparable to other state-of-the-art methods.
Probabilistic Abstraction Hierarchies
Authors: Daphne Koller, Dirk Ormoneit Eran Segal
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Many domains are naturally organized in an abstraction hierarchy or taxonomy, where the instances in “nearby” classes in the taxonomy are similar. In this pa- per, we provide a general probabilistic framework for clustering data into a set of classes organized as a taxonomy, where each class is associated with a prob- abilistic model from which the data was generated. The clustering algorithm simultaneously optimizes three things: the assignment of data instances to clus- ters, the models associated with the clusters, and the structure of the abstraction hierarchy. A unique feature of our approach is that it utilizes global optimization algorithms for both of the last two steps, reducing the sensitivity to noise and the propensity to local maxima that are characteristic of algorithms such as hierarchi- cal agglomerative clustering that only take local steps. We provide a theoretical analysis for our algorithm, showing that it converges to a local maximum of the joint likelihood of model and data. We present experimental results on synthetic data, and on real data in the domains of gene expression and text.
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
Authors: Michael Jones Paul Viola
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: This paper develops a new approach for extremely fast detection in do- mains where the distribution of positive and negative examples is highly skewed (e.g. face detection or database retrieval). In such domains a cascade of simple classiﬁers each trained to achieve high detection rates and modest false positive rates can yield a ﬁnal detector with many desir- able features: including high detection rates, very low false positive rates, and fast performance. Achieving extremely high detection rates, rather than low error, is not a task typically addressed by machine learning al- gorithms. We propose a new variant of AdaBoost as a mechanism for training the simple classiﬁers used in the cascade. Experimental results in the domain of face detection show the training algorithm yields sig- niﬁcant improvements in performance over conventional AdaBoost. The ﬁnal face detection system can process 15 frames per second, achieves over 90% detection, and a false positive rate of 1 in a 1,000,000.
Agglomerative Multivariate Information Bottleneck
Authors: Nir Friedman, Naftali Tishby Noam Slonim
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The information bottleneck method  is  an  unsupervised  model  independent data  organization  technique.  Given  a joint distribution  peA, B),  this  method  con(cid:173) structs a new variable T  that extracts partitions, or clusters, over the values of A  that are informative about B.  In  a recent paper,  we introduced a general princi(cid:173) pled framework for multivariate extensions of the information bottleneck method  that allows us to consider multiple systems of data partitions that are inter-related.  In this  paper,  we  present  a  new  family  of simple  agglomerative  algorithms  to  construct such systems of inter-related clusters.  We analyze the behavior of these  algorithms and apply them to several real-life datasets.
K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms
Authors: Yoshua Bengio Pascal Vincent
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why K-Nearest Neighbor (KNN) algorithms often perform more poorly than SVMs on classiﬁcation tasks. We then propose modiﬁed K-Nearest Neighbor algorithms to overcome the per- ceived problem. The approach is similar in spirit to Tangent Distance, but with invariances inferred from the local neighborhood rather than prior knowledge. Experimental results on real world classiﬁcation tasks sug- gest that the modiﬁed KNN algorithms often give a dramatic improve- ment over standard KNN and perform as well or better than SVMs.
Using Vocabulary Knowledge in Bayesian Multinomial Estimation
Authors: Joshua B. Tenenbaum Thomas L. Griffiths
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Estimating the parameters of sparse multinomial distributions is an important component of many statistical learning tasks. Recent approaches have used uncertainty over the vocabulary of symbols in a multinomial distribution as a means of accounting for sparsity. We present a Bayesian approach that allows weak prior knowledge, in the form of a small set of approximate candidate vocabularies, to be used to dramatically improve the resulting estimates. We demonstrate these improvements in applications to text compres(cid:173) sion and estimating distributions over words in newsgroup data.
Bayesian time series classification
Authors: Stephen J. Roberts Peter Sykacek
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: This paper proposes an approach to classiﬁcation of adjacent segments of a time series as being either of  classes. We use a hierarchical model that consists of a feature extraction stage and a generative classiﬁer which is built on top of these features. Such two stage approaches are often used in signal and image processing. The novel part of our work is that we link these stages probabilistically by using a latent feature space. To use one joint model is a Bayesian requirement, which has the advantage to fuse information according to its certainty. The classiﬁer is implemented as hidden Markov model with Gaussian and Multinomial observation distributions deﬁned on a suitably chosen representation of autoregressive models. The Markov dependency is mo- tivated by the assumption that successive classiﬁcations will be corre- lated. Inference is done with Markov chain Monte Carlo (MCMC) tech- niques. We apply the proposed approach to synthetic data and to classi- ﬁcation of EEG that was recorded while the subjects performed different cognitive tasks. All experiments show that using a latent feature space results in a signiﬁcant improvement in generalization accuracy. Hence we expect that this idea generalizes well to other hierarchical models.
Computing Time Lower Bounds for Recurrent Sigmoidal Neural Networks
Authors: M. Schmitt
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Recurrent  neural networks of analog units are computers for  real(cid:173) valued functions.  We  study the time complexity of real computa(cid:173) tion  in  general  recurrent  neural  networks.  These  have  sigmoidal,  linear,  and  product  units  of unlimited  order  as  nodes  and  no  re(cid:173) strictions on the weights.  For networks operating in discrete time,  we  exhibit  a  family  of functions  with  arbitrarily high  complexity,  and we derive almost tight bounds on the time required to compute  these functions.  Thus, evidence is  given of the computational lim(cid:173) itations  that  time-bounded  analog  recurrent  neural  networks  are  subject to.
Playing is believing: The role of beliefs in multi-agent learning
Authors: Leslie Pack Kaelbling Yu-Han Chang
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We propose a new classiﬁcation for multi-agent learning algorithms, with each league of players characterized by both their possible strategies and possible beliefs. Using this classiﬁcation, we review the optimality of ex- isting algorithms, including the case of interleague play. We propose an incremental improvement to the existing algorithms that seems to achieve average payoffs that are at least the Nash equilibrium payoffs in the long- run against fair opponents.
Probabilistic principles in unsupervised learning of visual structure: human data and a model
Authors: Benjamin P. Hiles, Hwajin Yang, Nathan Intrator Shimon Edelman
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: To ﬁnd out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images with tight control exerted over (1) the condi- tional probabilities of the constituent fragments, and (2) the value of Bar- low’s criterion of “suspicious coincidence” (the ratio of joint probability to the product of marginals). We then compared the part veriﬁcation re- sponse times for various probe/target combinations before and after the exposure. For composite probes, the speedup was much larger for tar- gets that contained pairs of fragments perfectly predictive of each other, compared to those that did not. This effect was modulated by the sig- niﬁcance of their co-occurrence as estimated by Barlow’s criterion. For lone-fragment probes, the speedup in all conditions was generally lower than for composites. These results shed light on the brain’s strategies for unsupervised acquisition of structural information in vision.
Predictive Representations of State
Authors: Richard S. Sutton Michael L. Littman
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We show that states of a dynamical system can be usefully repre(cid:173) sented by multi-step, action-conditional predictions of future ob(cid:173) servations. State representations that are grounded in data in this way may be easier to learn, generalize better, and be less depen(cid:173) dent on accurate prior models than, for example, POMDP state representations. Building on prior work by Jaeger and by Rivest and Schapire, in this paper we compare and contrast a linear spe(cid:173) cialization of the predictive approach with the state representa(cid:173) tions used in POMDPs and in k-order Markov models. Ours is the first specific formulation of the predictive idea that includes both stochasticity and actions (controls). We show that any system has a linear predictive state representation with number of predictions no greater than the number of states in its minimal POMDP model.
In predicting or controlling a sequence of observations, the concepts of state and state estimation inevitably arise. There have been two dominant approaches. The generative-model approach, typified by research on partially observable Markov de(cid:173) cision processes (POMDPs), hypothesizes a structure for generating observations and estimates its state and state dynamics. The history-based approach, typified by k-order Markov methods, uses simple functions of past observations as state, that is, as the immediate basis for prediction and control. (The data flow in these two ap(cid:173) proaches are diagrammed in Figure 1.) Of the two, the generative-model approach is more general. The model's internal state gives it temporally unlimited memory(cid:173) the ability to remember an event that happened arbitrarily long ago--whereas a history-based approach can only remember as far back as its history extends. The bane of generative-model approaches is that they are often strongly dependent on a good model of the system's dynamics. Most uses of POMDPs, for example, assume a perfect dynamics model and attempt only to estimate state. There are algorithms for simultaneously estimating state and dynamics (e.g., Chrisman, 1992), analogous to the Baum-Welch algorithm for the uncontrolled case (Baum et al., 1970), but these are only effective at tuning parameters that are already approximately cor(cid:173) rect (e.g., Shatkay & Kaelbling, 1997).
observations (and actions)
Contextual Modulation of Target Saliency
Authors: Antonio Torralba
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: The most popular algorithms for object detection require the use of exhaustive spatial and scale search procedures. In such approaches, an object is defined by means of local features. fu this paper we show that including contextual information in object detection pro(cid:173) cedures provides an efficient way of cutting down the need for exhaustive search. We present results with real images showing that the proposed scheme is able to accurately predict likely object classes, locations and sizes.
On Kernel-Target Alignment
Authors: John Shawe-Taylor, André Elisseeff, Jaz S. Kandola Nello Cristianini
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We  introduce the notion of kernel-alignment, a  measure of similar(cid:173) ity  between two  kernel functions  or between a  kernel and a  target  function.  This quantity captures the degree of agreement between  a  kernel  and a  given  learning task,  and has  very  natural interpre(cid:173) tations  in  machine  learning,  leading also  to  simple  algorithms for  model selection and learning.  We analyse its theoretical properties,  proving that it  is  sharply concentrated around its expected value,  and  we  discuss  its  relation  with  other  standard  measures  of  per(cid:173) formance.  Finally we  describe  some  of the algorithms that  can be  obtained within this framework,  giving experimental results show(cid:173) ing that adapting the kernel  to improve  alignment  on the labelled  data  significantly  increases  the  alignment  on  the  test  set,  giving  improved  classification  accuracy.  Hence,  the  approach  provides  a  principled method of performing transduction. 
Keywords:  Kernels,  alignment, eigenvectors, eigenvalues, transduction
Escaping the Convex Hull with Extrapolated Vector Machines
Authors: Patrick Haffner
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Maximum  margin  classifiers  such  as  Support  Vector  Machines  (SVMs)  critically  depends  upon  the  convex  hulls  of the  training  samples  of each  class,  as  they  implicitly  search for  the  minimum  distance  between the convex hulls.  We  propose Extrapolated Vec(cid:173) tor  Machines  (XVMs)  which  rely  on  extrapolations  outside  these  convex hulls.  XVMs improve SVM generalization very significantly  on  the  MNIST  [7]  OCR  data.  They  share  similarities  with  the  Fisher  discriminant:  maximize  the  inter-class  margin  while  mini(cid:173) mizing the intra-class disparity.
Algorithmic Luckiness
Authors: Robert C. Williamson Ralf Herbrich
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: In  contrast  to  standard  statistical  learning  theory  which  studies  uniform bounds on the expected error we present a framework that  exploits  the  specific  learning  algorithm  used.  Motivated  by  the  luckiness framework  [8]  we  are also  able to exploit the serendipity  of the training sample.  The main difference to previous approaches  lies  in  the  complexity  measure;  rather  than covering  all  hypothe(cid:173) ses  in  a  given  hypothesis  space  it  is  only  necessary  to  cover  the  functions  which  could  have  been  learned  using  the  fixed  learning  algorithm.  We  show  how  the  resulting  framework  relates  to  the  VC, luckiness and compression frameworks.  Finally,  we  present an  application  of this  framework  to  the  maximum  margin  algorithm  for  linear classifiers which results in a  bound that exploits both the  margin and the distribution of the data in feature  space.
Optimising Synchronisation Times for Mobile Devices
Authors: Antony I. T. Rowstron, Christopher M. Bishop, Michael J. Taylor Neil D. Lawrence
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: With the increasing number of users of mobile computing devices  (e.g.  personal digital assistants) and the advent of third generation  mobile phones, wireless communications are becoming increasingly  important.  Many  applications  rely  on  the  device  maintaining  a  replica  of a  data-structure which  is  stored on  a  server,  for  exam(cid:173) ple news databases, calendars and e-mail.  ill this paper we explore  the question of the optimal strategy for synchronising such replicas.  We utilise probabilistic models to represent how the data-structures  evolve  and to model  user behaviour.  We  then formulate objective  functions which can be minimised with respect to the synchronisa(cid:173) tion timings.  We demonstrate, using two real world data-sets, that  a user can obtain more up-to-date information using our approach.
Spike timing and the coding of naturalistic sounds in a central auditory area of songbirds
Authors: Kamal Sen, William Bialek, A. J. Doupe B. D. Wright
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: In nature, animals encounter high dimensional sensory stimuli that have complex statistical and dynamical structure. Attempts to study the neu- ral coding of these natural signals face challenges both in the selection of the signal ensemble and in the analysis of the resulting neural responses. For zebra ﬁnches, naturalistic stimuli can be deﬁned as sounds that they encounter in a colony of conspeciﬁc birds. We assembled an ensemble of these sounds by recording groups of 10-40 zebra ﬁnches, and then ana- lyzed the response of single neurons in the songbird central auditory area (ﬁeld L) to continuous playback of long segments from this ensemble. Following methods developed in the ﬂy visual system, we measured the information that spike trains provide about the acoustic stimulus with- out any assumptions about which features of the stimulus are relevant. Preliminary results indicate that large amounts of information are carried by spike timing, with roughly half of the information accessible only at time resolutions better than 10 ms; additional information is still be- ing revealed as time resolution is improved to 2 ms. Information can be decomposed into that carried by the locking of individual spikes to the stimulus (or modulations of spike rate) vs. that carried by timing in spike patterns. Initial results show that in ﬁeld L, temporal patterns give at least
 % extra information. Thus, single central auditory neurons can pro-
vide an informative representation of naturalistic sounds, in which spike timing may play a signiﬁcant role.
A Variational Approach to Learning Curves
Authors: Manfred Opper Dörthe Malzahn
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We combine the replica approach from statistical physics with a varia- tional approach to analyze learning curves analytically. We apply the method to Gaussian process regression. As a main result we derive ap- proximative relations between empirical error measures, the generaliza- tion error and the posterior variance.
Why Neuronal Dynamics Should Control Synaptic Learning Rules
Authors: Ádám Kepecs Jesper Tegnér
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: Hebbian learning rules are generally formulated as static rules. Un(cid:173) der changing condition (e.g. neuromodulation, input statistics)  most rules are sensitive to parameters. In particular, recent work  has focused on two different formulations of spike-timing-dependent  plasticity rules. Additive STDP [1] is remarkably versatile but  also very fragile, whereas multiplicative STDP [2, 3] is more ro(cid:173) bust but lacks attractive features such as synaptic competition and  rate stabilization. Here we address the problem of robustness in  the additive STDP rule. We derive an adaptive control scheme,  where the learning function is under fast dynamic control by post(cid:173) synaptic activity to stabilize learning under a variety of conditions.  Such a control scheme can be implemented using known biophysical  mechanisms of synapses. We show that this adaptive rule makes  the addit ive STDP more robust. Finally, we give an example how  meta plasticity of the adaptive rule can be used to guide STDP  into different type of learning regimes.
Latent Dirichlet Allocation
Authors: Andrew Y. Ng, Michael I. Jordan David M. Blei
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We propose a generative model for text and other collections of dis(cid:173) crete data that generalizes or improves on several previous models  including naive Bayes/unigram, mixture of unigrams  [6],  and Hof(cid:173) mann's  aspect  model,  also  known  as  probabilistic latent  semantic  indexing  (pLSI)  [3].  In  the  context  of text  modeling,  our  model  posits  that  each  document  is  generated  as  a  mixture  of  topics,  where  the  continuous-valued  mixture  proportions  are  distributed  as  a  latent  Dirichlet  random  variable.  Inference  and  learning  are  carried out efficiently  via variational  algorithms.  We  present  em(cid:173) pirical  results  on  applications  of this  model  to  problems  in  text  modeling,  collaborative filtering,  and text classification.
A Bayesian Network for Real-Time Musical Accompaniment
Authors: Christopher Raphael
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We  describe  a  computer  system  that  provides  a  real-time  musi(cid:173) cal  accompaniment  for  a  live  soloist  in  a  piece  of non-improvised  music for soloist and accompaniment.  A Bayesian network is devel(cid:173) oped  that  represents  the joint  distribution  on  the  times  at  which  the  solo  and  accompaniment  notes  are  played,  relating  the  two  parts through a  layer of hidden variables.  The network is first  con(cid:173) structed using  the rhythmic  information  contained in the musical  score.  The network is then trained to capture the musical interpre(cid:173) tations of the soloist and accompanist in an off-line rehearsal phase.  During live accompaniment the learned distribution of the network  is  combined  with a  real-time  analysis  of the  soloist's  acoustic  sig(cid:173) nal,  performed with  a  hidden Markov  model,  to  generate  a  musi(cid:173) cally principled accompaniment  that  respects all available  sources  of knowledge.  A live  demonstration will  be provided.
Efficiency versus Convergence of Boolean Kernels for On-Line Learning Algorithms
Authors: Dan Roth, Rocco A. Servedio Roni Khardon
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: We study online learning in Boolean domains using kernels which cap- ture feature expansions equivalent to using conjunctions over basic fea- tures. We demonstrate a tradeoff between the computational efﬁciency with which these kernels can be computed and the generalization abil- ity of the resulting classiﬁer. We ﬁrst describe several kernel functions which capture either limited forms of conjunctions or all conjunctions. We show that these kernels can be used to efﬁciently run the Percep- tron algorithm over an exponential number of conjunctions; however we also prove that using such kernels the Perceptron algorithm can make an exponential number of mistakes even when learning simple func- tions. We also consider an analogous use of kernel functions to run the multiplicative-update Winnow algorithm over an expanded feature space of exponentially many conjunctions. While known upper bounds imply that Winnow can learn DNF formulae with a polynomial mistake bound in this setting, we prove that it is computationally hard to simulate Win- now’s behavior for learning DNF over such a feature set, and thus that such kernel functions for Winnow are not efﬁciently computable.
Prodding the ROC Curve: Constrained Optimization of Classifier Performance
Authors: Robert Dodier, Michael D. Colagrosso, Cesar Guerra-Salcedo, Richard Wolniewicz Michael Mozer
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: When designing a two-alternative classiﬁer, one ordinarily aims to maximize the classiﬁer’s ability to discriminate between members of the two classes. We describe a situation in a real-world business application of machine-learning prediction in which an additional constraint is placed on the nature of the solu- tion: that the classiﬁer achieve a speciﬁed correct acceptance or correct rejection rate (i.e., that it achieve a ﬁxed accuracy on members of one class or the other). Our domain is predicting churn in the telecommunications industry. Churn refers to customers who switch from one service provider to another. We pro- pose four algorithms for training a classiﬁer subject to this domain constraint, and present results showing that each algorithm yields a reliable improvement in performance. Although the improvement is modest in magnitude, it is nonethe- less impressive given the difﬁculty of the problem and the ﬁnancial return that it achieves to the service provider.
When designing a classiﬁer, one must specify an objective measure by which the classi- ﬁer’s performance is to be evaluated. One simple objective measure is to minimize the number of misclassiﬁcations. If the cost of a classiﬁcation error depends on the target and/ or response class, one might utilize a risk-minimization framework to reduce the expected loss. A more general approach is to maximize the classiﬁer’s ability to discriminate one class from another class (e.g., Chang & Lippmann, 1994).
An ROC curve (Green & Swets, 1966) can be used to visualize the discriminative performance of a two-alternative classiﬁer that outputs class posteriors. To explain the ROC curve, a classiﬁer can be thought of as making a positive/negative judgement as to whether an input is a member of some class. Two different accuracy measures can be obtained from the classiﬁer: the accuracy of correctly identifying an input as a member of the class (a correct acceptance or CA), and the accuracy of correctly identifying an input as a nonmember of the class (a correct rejection or CR). To evaluate the CA and CR rates, it is necessary to pick a threshold above which the classiﬁer’s probability estimate is inter- preted as an “accept,” and below which is interpreted as a “reject”—call this the criterion. The ROC curve plots CA against CR rates for various criteria (Figure 1a). Note that as the threshold is lowered, the CA rate increases and the CR rate decreases. For a criterion of 1, the CA rate approaches 0 and the CR rate 1; for a criterion of 0, the CA rate approaches 1
Natural Language Grammar Induction Using a Constituent-Context Model
Authors: Christopher D. Manning Dan Klein
Published in: Advances in Neural Information Processing Systems 14  (NIPS 2001) (Main Conference Track), 2001

Abstract: This paper presents a novel approach to the unsupervised learning of syn- tactic analyses of natural language text. Most previous work has focused on maximizing likelihood according to generative PCFG models. In con- trast, we employ a simpler probabilistic model over trees based directly on constituent identity and linear context, and use an EM-like iterative procedure to induce structure. This method produces much higher qual- ity analyses, giving the best published results on the ATIS dataset. 1 Overview
To enable a wide range of subsequent tasks, human language sentences are standardly given tree-structure analyses, wherein the nodes in a tree dominate contiguous spans of words called constituents, as in figure 1(a). Constituents are the linguistically coherent units in the sentence, and are usually labeled with a constituent category, such as noun phrase (NP) or verb phrase (VP). An aim of grammar induction systems is to figure out, given just the sentences in a corpus S, what tree structures correspond to them. In this sense, the grammar induction problem is an incomplete data problem, where the complete data is the corpus of trees T , but we only observe their yields S. This paper presents a new approach to this problem, which gains leverage by directly making use of constituent contexts. It is an open problem whether entirely unsupervised methods can produce linguistically accurate parses of sentences. Due to the difficulty of this task, the vast majority of statis- tical parsing work has focused on supervised learning approaches to parsing, where one uses a treebank of fully parsed sentences to induce a model which parses unseen sentences [7, 3]. But there are compelling motivations for unsupervised grammar induction. Building supervised training data requires considerable resources, including time and linguistic ex- pertise. Investigating unsupervised methods can shed light on linguistic phenomena which are implicit within a supervised parser's supervisory information (e.g., unsupervised sys- tems often have difficulty correctly attaching subjects to verbs above objects, whereas for a supervised parser, this ordering is implicit in the supervisory information). Finally, while the presented system makes no claims to modeling human language acquisition, results on whether there is enough information in sentences to recover their structure are important data for linguistic theory, where it has standardly been assumed that the information in the data is deficient, and strong innate knowledge is required for language acquisition [4].