mtybilly/Instruct2DS huggingface dataset:
MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction
Authors: Flora Salim, Yongli Ren, Nuria Oliver Hao Xue
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Human mobility prediction is a core functionality in many location-based services and applications. However, due to the sparsity of mobility data, it is not an easy task to predict future POIs (place-of-interests) that are going to be visited. In this paper, we propose MobTCast, a Transformer-based context-aware network for mobility prediction. Specifically, we explore the influence of four types of context in mobility prediction: temporal, semantic, social, and geographical contexts. We first design a base mobility feature extractor using the Transformer architecture, which takes both the history POI sequence and the semantic information as input. It handles both the temporal and semantic contexts. Based on the base extractor and the social connections of a user, we employ a self-attention module to model the influence of the social context. Furthermore, unlike existing methods, we introduce a location prediction branch in MobTCast as an auxiliary task to model the geographical context and predict the next location. Intuitively, the geographical distance between the location of the predicted POI and the predicted location from the auxiliary branch should be as close as possible. To reflect this relation, we design a consistency loss to further improve the POI prediction performance. In our experimental results, MobTCast outperforms other state-of-the-art next POI prediction methods. Our approach illustrates the value of including different types of context in next POI prediction.
Early Convolutions Help Transformers See Better
Authors: Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollar, Ross B. Girshick Tete Xiao
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Vision transformer (ViT) models exhibit substandard optimizability. In particular, they are sensitive to the choice of optimizer (AdamW vs. SGD), optimizer hyperparameters, and training schedule length. In comparison, modern convolutional neural networks are easier to optimize. Why is this the case? In this work, we conjecture that the issue lies with the patchify stem of ViT models, which is implemented by a stride-p p×p convolution (p = 16 by default) applied to the input image. This large-kernel plus large-stride convolution runs counter to typical design choices of convolutional layers in neural networks. To test whether this atypical design choice causes an issue, we analyze the optimization behavior of ViT models with their original patchify stem versus a simple counterpart where we replace the ViT stem by a small number of stacked stride-two 3×3 convolutions. While the vast majority of computation in the two ViT designs is identical, we find that this small change in early visual processing results in markedly different training behavior in terms of the sensitivity to optimization settings as well as the final model accuracy. Using a convolutional stem in ViT dramatically increases optimization stability and also improves peak performance (by ∼1-2% top-1 accuracy on ImageNet-1k), while maintaining flops and runtime. The improvement can be observed across the wide spectrum of model complexities (from 1G to 36G flops) and dataset scales (from ImageNet-1k to ImageNet-21k). These findings lead us to recommend using a standard, lightweight convolutional stem for ViT models in this regime as a more robust architectural choice compared to the original ViT model design.
Error Compensated Distributed SGD Can Be Accelerated
Authors: Peter Richtarik, Tong Zhang Xun Qian
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Gradient compression is a recent and increasingly popular technique for reducing the communication cost in distributed training of large-scale machine learning models. In this work we focus on developing efficient distributed methods that can work for any compressor satisfying a certain contraction property, which includes both unbiased (after appropriate scaling) and biased compressors such as RandK and TopK. Applied naively, gradient compression introduces errors that either slow down convergence or lead to divergence. A popular technique designed to tackle this issue is error compensation/error feedback. Due to the difficulties associated with analyzing biased compressors, it is not known whether gradient compression with error compensation can be combined with acceleration. In this work, we show for the first time that error compensated gradient compression methods can be accelerated. In particular, we propose and study the error compensated loopless Katyusha method, and establish an accelerated linear convergence rate under standard assumptions. We show through numerical experiments that the proposed method converges with substantially fewer communication rounds than previous error compensated algorithms.
InfoGCL: Information-Aware Graph Contrastive Learning
Authors: Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang Dongkuan Xu
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Various graph contrastive learning models have been proposed to improve the performance of tasks on graph datasets in recent years. While effective and prevalent, these models are usually carefully customized. In particular, despite all recent work create two contrastive views, they differ in a variety of view augmentations, architectures, and objectives. It remains an open question how to build your graph contrastive learning model from scratch for particular graph tasks and datasets. In this work, we aim to fill this gap by studying how graph information is transformed and transferred during the contrastive learning process, and proposing an information-aware graph contrastive learning framework called InfoGCL. The key to the success of the proposed framework is to follow the Information Bottleneck principle to reduce the mutual information between contrastive parts while keeping task-relevant information intact at both the levels of the individual module and the entire framework so that the information loss during graph representation learning can be minimized. We show for the first time that all recent graph contrastive learning methods can be unified by our framework. Based on theoretical and empirical analysis on benchmark graph datasets, we show that InfoGCL achieves state-of-the-art performance in the settings of both graph classification and node classification tasks.
Meta-Learning for Relative Density-Ratio Estimation
Authors: Tomoharu Iwata, Yasuhiro Fujiwara Atsutoshi Kumagai
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: The ratio of two probability densities, called a density-ratio, is a vital quantity in machine learning. In particular, a relative density-ratio, which is a bounded extension of the density-ratio, has received much attention due to its stability and has been used in various applications such as outlier detection and dataset comparison. Existing methods for (relative) density-ratio estimation (DRE) require many instances from both densities. However, sufficient instances are often unavailable in practice. In this paper, we propose a meta-learning method for relative DRE, which estimates the relative density-ratio from a few instances by using knowledge in related datasets. Specifically, given two datasets that consist of a few instances, our model extracts the datasets' information by using neural networks and uses it to obtain instance embeddings appropriate for the relative DRE. We model the relative density-ratio by a linear model on the embedded space, whose global optimum solution can be obtained as a closed-form solution. The closed-form solution enables fast and effective adaptation to a few instances, and its differentiability enables us to train our model such that the expected test error for relative DRE can be explicitly minimized after adapting to a few instances. We empirically demonstrate the effectiveness of the proposed method by using three problems: relative DRE, dataset comparison, and outlier detection.
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
Authors: Loucas Pillaud-Vivien, Francis R. Bach, Alessandro Rudi Vivien Cabannes
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: As annotations of data can be scarce in large-scale practical problems, leveraging unlabelled examples is one of the most important aspects of machine learning. This is the aim of semi-supervised learning. To benefit from the access to unlabelled data, it is natural to diffuse smoothly knowledge of labelled data to unlabelled one. This induces to the use of Laplacian regularization. Yet, current implementations of Laplacian regularization suffer from several drawbacks, notably the well-known curse of dimensionality. In this paper, we design a new class of algorithms overcoming this issue, unveiling a large body of spectral filtering methods. Additionally, we provide a statistical analysis showing that our estimators exhibit desirable behaviors. They are implemented through (reproducing) kernel methods, for which we provide realistic computational guidelines in order to make our method usable with large amounts of data.
Unlabeled Principal Component Analysis
Authors: Liangzu Peng, Manolis Tsakiris Yunzhen Yao
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-defined algebraic problem in the sense that the only matrices of minimal rank that agree with the given data are row-permutations of the ground-truth matrix, arising as the unique solutions of a polynomial system of equations. Further, we propose an efficient two-stage algorithmic pipeline for UPCA suitable for the practically relevant case where only a fraction of the data have been permuted. Stage-I employs outlier-robust PCA methods to estimate the ground-truth column-space. Equipped with the column-space, Stage-II applies recent methods for unlabeled sensing to restore the permuted data. Experiments on synthetic data, face images, educational and medical records reveal the potential of UPCA for applications such as data privatization and record linkage.
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data
Authors: Panagiotis Tigas, Joost van Amersfoort, Andreas Kirsch, Uri Shalit, Yarin Gal Andrew Jesson
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Estimating personalized treatment effects from high-dimensional observational data is essential in situations where experimental designs are infeasible, unethical, or expensive. Existing approaches rely on fitting deep models on outcomes observed for treated and control populations. However, when measuring individual outcomes is costly, as is the case of a tumor biopsy, a sample-efficient strategy for acquiring each result is required. Deep Bayesian active learning provides a framework for efficient data acquisition by selecting points with high uncertainty. However, existing methods bias training data acquisition towards regions of non-overlapping support between the treated and control populations. These are not sample-efficient because the treatment effect is not identifiable in such regions. We introduce causal, Bayesian acquisition functions grounded in information theory that bias data acquisition towards regions with overlapping support to maximize sample efficiency for learning personalized treatment effects. We demonstrate the performance of the proposed acquisition strategies on synthetic and semi-synthetic datasets IHDP and CMNIST and their extensions, which aim to simulate common dataset biases and pathologies.
Scalable Rule-Based Representation Learning for Interpretable Classification
Authors: Wei Zhang, Ning Liu, Jianyong Wang Zhuo Wang
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. An improved design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on nine small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios. Our code is available at: https://github.com/12wang3/rrl.
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection
Authors: Yongqiang Zhang, Mingli Ding, Gim Hee Lee NA DONG
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: Deep networks have shown remarkable results in the task of object detection. However, their performance suffers critical drops when they are subsequently trained on novel classes without any sample from the base classes originally used to train the model. This phenomenon is known as catastrophic forgetting. Recently, several incremental learning methods are proposed to mitigate catastrophic forgetting for object detection. Despite the effectiveness, these methods require co-occurrence of the unlabeled base classes in the training data of the novel classes. This requirement is impractical in many real-world settings since the base classes do not necessarily co-occur with the novel classes. In view of this limitation, we consider a more practical setting of complete absence of co-occurrence of the base and novel classes for the object detection task. We propose the use of unlabeled in-the-wild data to bridge the non co-occurrence caused by the missing base classes during the training of additional novel classes. To this end, we introduce a blind sampling strategy based on the responses of the base-class model and pre-trained novel-class model to select a smaller relevant dataset from the large in-the-wild dataset for incremental learning. We then design a dual-teacher distillation framework to transfer the knowledge distilled from the base- and novel-class teacher models to the student model using the sampled in-the-wild data. Experimental results on the PASCAL VOC and MS COCO datasets show that our proposed method significantly outperforms other state-of-the-art class-incremental object detection methods when there is no co-occurrence between the base and novel classes during training.
A Regression Approach to Learning-Augmented Online Algorithms
Authors: Rong Ge, Amit Kumar, Debmalya Panigrahi Keerti Anand
Published in: Advances in Neural Information Processing Systems 34  (NeurIPS 2021) (Main Conference Track), 2021

Abstract: The emerging field of learning-augmented online algorithms uses ML techniques to predict future input parameters and thereby improve the performance of online algorithms. Since these parameters are, in general, real-valued functions, a natural approach is to use regression techniques to make these predictions. We introduce this approach in this paper, and explore it in the context of a general online search framework that captures classic problems like (generalized) ski rental, bin packing, minimum makespan scheduling, etc. We show nearly tight bounds on the sample complexity of this regression problem, and extend our results to the agnostic setting. From a technical standpoint, we show that the key is to incorporate online optimization benchmarks in the design of the loss function for the regression problem, thereby diverging from the use of off-the-shelf regression tools with standard bounds on statistical error.
Hebb Learning of Features based on their Information Content
Authors: Hideki Noda Ferdinand Peper
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: This  paper  investigates  the  stationary  points  of a  Hebb  learning  rule  with  a  sigmoid  nonlinearity  in  it.  We  show  mathematically  that  when  the  input  has  a  low  information  content,  as  measured  by  the  input's  variance,  this learning rule suppresses learning,  that is,  forces the weight  vector  to  converge  to  the  zero  vector.  When  the  information  content  exceeds  a  certain  value,  the  rule  will  automatically  begin  to  learn  a  feature in the input.  Our analysis suggests that under certain conditions  it  is  the  first  principal  component  that  is  learned.  The  weight  vector  length  remains  bounded,  provided  the  variance  of  the  input  is  finite.  Simulations confirm the theoretical results derived.
A Variational Principle for Model-based Morphing
Authors: Michael I. Jordan Lawrence K. Saul
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Given  a  multidimensional  data  set  and  a  model  of  its  density,  we  consider  how  to  define  the  optimal interpolation  between  two  points.  This is done by assigning a cost to each path through space,  based on two competing goals-one to interpolate through regions  of high  density,  the other  to minimize arc  length.  From this  path  functional,  we  derive  the  Euler-Lagrange  equations  for  extremal  motionj given two points, the desired  interpolation is found by solv(cid:173) ing a boundary value problem.  We show that this interpolation can  be done efficiently, in high dimensions, for Gaussian, Dirichlet, and  mixture models.
ARTEX: A Self-organizing Architecture for Classifying Image Regions
Authors: James R. Williamson Stephen Grossberg
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: A self-organizing architecture is  developed for  image region  classi(cid:173) fication.  The system  consists  of a  preprocessor  that utilizes multi(cid:173) scale filtering, competition, cooperation, and diffusion to compute a  vector  of image boundary  and surface  properties,  notably  texture  and  brightness  properties.  This  vector  inputs  to  a  system  that  incrementally  learns  noisy  multidimensional  mappings  and  their  probabilities.  The  architecture  is  applied  to  difficult  real-world  image  classification  problems,  including  classification  of synthet(cid:173) ic  aperture  radar  and  natural  texture  images,  and  outperforms a  recent state-of-the-art system at classifying natural textures.
The CONDENSATION Algorithm - Conditional Density Propagation and Applications to Visual Tracking
Authors: Michael Isard Andrew Blake
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: The power of sampling methods in Bayesian reconstruction of noisy  signals is well known.  The extension of sampling to temporal prob(cid:173) lems  is  discussed.  Efficacy  of sampling over  time is  demonstrated  with visual tracking.
A Silicon Model of Amplitude Modulation Detection in the Auditory Brainstem
Authors: Eric Fragnière, Eric A. Vittoz André van Schaik
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Detectim of the periodicity of amplitude modulatim is a major step in  the  determinatim  of  the  pitch  of  a  SOODd.  In  this  article  we  will  present  a silicm model  that uses  synchrroicity of spiking neurms  to  extract  the  fundamental  frequency  of  a  SOODd.  It  is  based  m  the  observatim that the so called  'Choppers'  in the mammalian  Cochlear  Nucleus  synchrmize well  for  certain  rates  of amplitude  modulatim,  depending  m  the  cell's  intrinsic  chopping  frequency.  Our  silicm  model uses  three different circuits, i.e.,  an  artificial cochlea,  an  Inner  Hair Cell circuit, and a spiking neuron circuit
Adaptive On-line Learning in Changing Environments
Authors: Klaus-Robert Müller, Andreas Ziehe, Shun-ichi Amari Noboru Murata
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: An adaptive on-line algorithm extending the learning of learning  idea is proposed and theoretically motivated. Relying only on gra(cid:173) dient flow information it can be applied to learning continuous  functions or distributions, even when no explicit loss function is gi(cid:173) ven and the Hessian is not available. Its efficiency is demonstrated  for a non-stationary blind separation task of acoustic signals.
Minimizing Statistical Bias with Queries
Authors: David A. Cohn
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: I describe  a  querying criterion that attempts to minimize the error  of a  learner  by  minimizing its estimated squared  bias.  I  describe  experiments  with  locally-weighted  regression  on  two  simple prob(cid:173) lems,  and observe  that this  "bias-only"  approach  outperforms the  more  common  "variance-only"  exploration  approach,  even  in  the  presence  of noise.
A Micropower Analog VLSI HMM State Decoder for Wordspotting
Authors: John Wawrzynek, Richard P Lippmann John Lazzaro
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: We  describe  the  implementation of a  hidden  Markov  model  state  decoding  system,  a component for  a  wordspotting speech  recogni(cid:173) tion system.  The key  specification for  this state decoder  design  is  microwatt power dissipation; this requirement led to a continuous(cid:173) time, analog circuit implementation.  We characterize the operation  of a  10-word (81  state)  state decoder  test chip.
Dual Kalman Filtering Methods for Nonlinear Prediction, Smoothing and Estimation
Authors: Alex T. Nelson Eric A. Wan
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Prediction,  estimation,  and smoothing  are  fundamental  to  signal  processing.  To perform  these  interrelated  tasks  given  noisy  data,  we  form  a  time  series  model  of  the  process  that  generates  the  data.  Taking noise in the system explicitly into account, maximum(cid:173) likelihood and Kalman frameworks are discussed  which involve the  dual process  of estimating both the model parameters and the un(cid:173) derlying state of the system.  We  review  several  established  meth(cid:173) ods in the linear case,  and propose severa! extensions utilizing dual  Kalman filters  (DKF)  and forward-backward  (FB)  filters  that  are  applicable  to  neural  networks.  Methods  are  compared on  several  simulations of noisy  time series.  We  also  include  an  example  of  nonlinear noise reduction in speech.
Source Separation and Density Estimation by Faithful Equivariant SOM
Authors: Jack D. Cowan, David G. Grier Juan K. Lin
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: We  couple  the  tasks  of source  separation  and  density  estimation  by  extracting  the  local  geometrical  structure  of distributions  ob(cid:173) tained  from  mixtures  of statistically  independent  sources.  Our  modifications of the self-organizing map  (SOM)  algorithm  results  in  purely digital learning rules which perform non-parametric his(cid:173) togram density estimation.  The non-parametric nature of the sep(cid:173) aration  allows  for  source  separation  of non-linear  mixtures.  An  anisotropic coupling  is  introduced  into  our  SOM  with  the  role  of  aligning the network locally with the independent component con(cid:173) tours.  This  approach  provides  an  exact  verification  condition  for  source separation with no prior on  the source distributions.
Continuous Sigmoidal Belief Networks Trained using Slice Sampling
Authors: Brendan J. Frey
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Real-valued  random hidden  variables  can  be  useful  for  modelling  latent  structure  that  explains  correlations  among  observed  vari(cid:173) ables.  I propose a simple unit that adds zero-mean Gaussian noise  to its input before passing it through  a  sigmoidal  squashing func(cid:173) tion.  Such units can produce a variety of useful  behaviors, ranging  from  deterministic to binary stochastic to continuous stochastic.  I  show  how  "slice sampling"  can  be  used  for  inference  and learning  in  top-down  networks of these  units and demonstrate learning on  two  simple problems.
A Mean Field Algorithm for Bayes Learning in Large Feed-forward Neural Networks
Authors: Ole Winther Manfred Opper
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: We present an algorithm which is expected to realise Bayes optimal  predictions in large feed-forward networks.  It is based on mean field  methods developed  within  statistical  mechanics  of disordered  sys(cid:173) tems.  We give a derivation for  the single layer perceptron and show  that  the  algorithm  also  provides  a  leave-one-out  cross-validation  test of the  predictions.  Simulations show excellent  agreement with  theoretical  results  of statistical mechanics.
Ordered Classes and Incomplete Examples in Classification
Authors: Mark Mathieson
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: The classes in classification tasks often have a natural ordering, and the  training and testing examples are often incomplete.  We  propose a non(cid:173) linear ordinal  model for  classification into ordered classes.  Predictive,  simulation-based approaches are used to learn from past and classify fu(cid:173) ture  incomplete examples.  These techniques are  illustrated  by  making  prognoses for patients who have suffered severe head injuries.
Unification of Information Maximization and Minimization
Authors: Ryotaro Kamimura
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: In  the  present  paper,  we  propose  a  method  to  unify  information  maximization and minimization  in  hidden  units.  The information  maximization and minimization are performed on two different lev(cid:173) els:  collective and individual level.  Thus, two kinds  of information:  collective and  individual  information  are  defined.  By  maximizing  collective  information  and  by  minimizing  individual  information,  simple  networks  can  be  generated  in  terms  of the  number  of con(cid:173) nections  and  the  number  of hidden  units.  Obtained  networks  are  expected  to give better generalization and improved interpretation  of internal representations.  This method  was applied  to the infer(cid:173) ence  of the  maximum onset  principle  of an artificial  language.  In  this  problem,  it  was  shown  that  the  individual  information  min(cid:173) imization  is  not  contradictory  to  the  collective  information  max(cid:173) imization.  In  addition,  experimental  results  confirmed  improved  generalization performance,  because over-training can significantly  be suppressed.
Multi-Task Learning for Stock Selection
Authors: Yoshua Bengio Joumana Ghosn
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Artificial  Neural  Networks  can  be  used  to  predict  future  returns  of stocks  in order to  take financial  decisions .  Should  one  build  a  separate network for  each stock or share the same network for  all  the  stocks?  In  this  paper  we  also  explore  other  alternatives,  in  which  some  layers  are  shared  and  others  are  not  shared.  When  the  prediction  of future  returns  for  different  stocks  are viewed  as  different  tasks,  sharing  some  parameters  across  stocks  is  a  form  of multi-task  learning.  In  a  series  of experiments  with  Canadian  stocks,  we obtain yearly returns that are more than 14% above  various benchmarks. 
1
Cholinergic Modulation Preserves Spike Timing Under Physiologically Realistic Fluctuating Input
Authors: Andreas M. Bartels, Terrence J. Sejnowski Akaysha C. Tang
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Neuromodulation  can  change  not  only  the  mean  firing  rate  of a  neuron,  but  also  its  pattern  of firing .  Therefore,  a  reliable  neu(cid:173) ral  coding  scheme,  whether  a  rate  coding  or  a  spike  time  based  coding,  must  be  robust  in  a  dynamic  neuromodulatory  environ(cid:173) ment.  The common observation that cholinergic modulation leads  to  a  reduction  in  spike  frequency  adaptation  implies  a  modifica(cid:173) tion  of spike  timing,  which  would  make  a  neural  code  based  on  precise spike timing difficult to maintain.  In this paper, the effects  of cholinergic modulation were  studied to test  the hypothesis that  precise  spike timing can serve  as a  reliable neural code.  Using  the  whole  cell  patch-clamp technique  in rat neocortical  slice  prepara(cid:173) tion and compartmental modeling techniques,  we show that cholin(cid:173) ergic  modulation, surprisingly,  preserved  spike  timing in response  to a fluctuating  inputs that resembles  in  vivo conditions.  This re(cid:173) sult suggests that in vivo spike timing may be much more resistant  to changes in neuromodulator concentrations than previous physi(cid:173) ological studies have  implied. 
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A. C.  Tang, A.  M.  Bartels and T.  J. Sejnowski
Analytical Mean Squared Error Curves in Temporal Difference Learning
Authors: Peter Dayan Satinder P. Singh
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: We  have  calculated  analytical  expressions  for  how  the  bias  and  variance of the estimators provided by various temporal difference  value estimation algorithms change with offline updates over trials  in absorbing Markov chains using lookup table representations.  We  illustrate  classes  of learning curve  behavior in various  chains,  and  show the manner in which TD is sensitive to the choice of its step(cid:173) size  and eligibility trace parameters.
Dynamics of Training
Authors: Manfred Opper Siegfried Bös
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: A new method to calculate the full training process of a neural net(cid:173) work is  introduced.  No sophisticated methods like the replica trick  are  used.  The results  are directly  related  to  the actual number of  training  steps.  Some  results  are  presented  here,  like  the  maximal  learning rate, an exact description of early stopping, and the neces(cid:173) sary number of training steps.  Further problems can be addressed  with  this approach.
A Constructive RBF Network for Writer Adaptation
Authors: Nada Matic John C. Platt
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: This  paper  discusses  a  fairly  general  adaptation  algorithm  which  augments a standard neural network  to increase  its recognition  ac(cid:173) curacy  for  a  specific  user.  The  basis for  the  algorithm is  that  the  output of a neural network is characteristic of the input, even when  the  output  is  incorrect.  We  exploit  this  characteristic  output  by  using  an  Output  Adaptation  Module  (OAM)  which  maps this out(cid:173) put  into  the  correct  user-dependent  confidence  vector.  The  OAM  is  a  simplified  Resource  Allocating  Network  which  constructs  ra(cid:173) dial  basis  functions  on-line.  We  applied  the  OAM  to  construct  a  writer-adaptive  character  recognition  system  for  on-line  hand(cid:173) printed  characters.  The  OAM  decreases  the  word  error  rate  on  a  test  set  by  an  average  of 45%,  while  creating  only  3  to  25  basis  functions for  each  writer  in  the  test  set.
MLP Can Provably Generalize Much Better than VC-bounds Indicate
Authors: Herman L. Ferrá Adam Kowalczyk
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Results  of a  study of the  worst  case  learning  curves  for  a  partic(cid:173) ular class of probability  distribution on input  space  to MLP  with  hard  threshold  hidden  units are  presented.  It is  shown in  partic(cid:173) ular,  that  in  the  thermodynamic  limit  for  scaling  by  the  number  of connections to the first  hidden layer, although the true learning  curve behaves as  ~ a-I for  a  ~ 1, its VC-dimension  based bound  is trivial  (= 1)  and  its VC-entropy bound is trivial for  a  ::;  6.2.  It  is also shown that bounds following the true learning curve can be  derived  from  a  formalism  based on the density of error patterns.
3D Object Recognition: A Model of View-Tuned Neurons
Authors: Tomaso Poggio, Nikos K. Logothetis Emanuela Bricolo
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: In  1990  Poggio and  Edelman proposed  a  view-based  model of ob(cid:173) ject recognition that accounts for several psychophysical properties  of certain  recognition  tasks.  The model predicted  the existence  of  view-tuned  and view-invariant units,  that were  later found  by  Lo(cid:173) gothetis  et  al.  (Logothetis  et  al.,  1995)  in  IT  cortex  of monkeys  trained  with views  of specific  paperclip  objects.  The model,  how(cid:173) ever,  does  not specify  the inputs to the view-tuned  units and their  internal  organization.  In  this  paper  we  propose  a  model of these  view-tuned  units  that  is  consistent  with  physiological  data from  single cell  responses.
Learning Bayesian Belief Networks with Neural Network Estimators
Authors: Gregory F. Cooper Stefano Monti
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: In  this  paper  we  propose  a  method  for  learning  Bayesian  belief  networks  from  data.  The  method  uses  artificial  neural  networks  as  probability estimators, thus  avoiding the need for  making prior  assumptions on the nature of the probability distributions govern(cid:173) ing  the relationships  among the  participating variables.  This new  method has  the potential for  being  applied to domains containing  both discrete  and continuous variables  arbitrarily distributed.  We  compare  the  learning  performance  of this  new  method  with  the  performance  of the  method  proposed  by  Cooper  and  Herskovits  in  [7].  The experimental results  show  that,  although  the  learning  scheme based on the use  of ANN  estimators is  slower, the learning  accuracy  of the two methods is  comparable.  Category:  Algorithms and Architectures.
Approximate Solutions to Optimal Stopping Problems
Authors: Benjamin Van Roy John N. Tsitsiklis
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: We  propose and analyze an algorithm that approximates solutions  to the problem of optimal stopping in a discounted irreducible ape(cid:173) riodic  Markov  chain.  The  scheme  involves  the  use  of linear  com(cid:173) binations  of  fixed  basis  functions  to  approximate  a  Q-function.  The weights  of the  linear  combination  are incrementally  updated  through  an iterative process similar  to  Q-Iearning,  involving sim(cid:173) ulation of the underlying  Markov chain.  Due to space limitations,  we  only provide an overview of a  proof of convergence  (with prob(cid:173) ability 1)  and bounds on the approximation error.  This is  the first  theoretical result  that establishes the soundness  of a  Q-Iearning(cid:173) like  algorithm  when  combined  with  arbitrary  linear  function  ap(cid:173) proximators  to  solve  a  sequential  decision  problem.  Though  this  paper focuses  on  the case of finite  state spaces, the results extend  naturally to continuous and unbounded state spaces, which are ad(cid:173) dressed in a forthcoming full-length  paper.
An Orientation Selective Neural Network for Pattern Identification in Particle Detectors
Authors: David Horn, Ury Naftaly, Carmit Sahar-Pikielny Halina Abramowicz
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: We  present  an  algorithm for  identifying linear  patterns  on  a  two(cid:173) dimensional lattice based on the concept of an orientation selective  cell,  a  concept  borrowed  from  neurobiology  of vision.  Construct(cid:173) ing  a  multi-layered  neural  network  with  fixed  architecture  which  implements orientation selectivity,  we  define  output elements cor(cid:173) responding  to different  orientations,  which  allow  us  to make  a  se(cid:173) lection  decision.  The  algorithm takes into account  the granularity  of the lattice as well  as the presence of noise and inefficiencies.  The  method  is  applied  to  a  sample  of data  collected  with  the  ZEUS  detector  at  HERA  in  order  to  identify  cosmic  muons  that  leave  a  linear  pattern  of signals  in  the  segmented  calorimeter.  A  two  dimensional  representation  of the  relevant  part  of the  detector  is  used.  The  algorithm performs  very  well.  Given  its  architecture,  this system  becomes a  good candidate for fast  pattern recognition  in  parallel processing  devices.
Early Brain Damage
Authors: Ralph Neuneier, Hans-Georg Zimmermann Volker Tresp
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Optimal Brain Damage  (OBD)  is  a  method for  reducing  the num(cid:173) ber of weights in a  neural network.  OBD estimates the increase  in  cost  function  if weights  are  pruned  and  is  a  valid  approximation  if the learning algorithm has converged  into a  local minimum.  On  the  other hand it is  often desirable  to terminate the learning pro(cid:173) cess  before  a  local  minimum is  reached  (early  stopping).  In  this  paper  we  show  that  OBD  estimates  the  increase  in cost  function  incorrectly  if the network is  not in a local minimum.  We also show  how  OBD  can  be  extended  such  that  it  can  be  used  in  connec(cid:173) tion  with  early  stopping.  We  call  this  new  approach  Early  Brain  Damage,  EBD.  EBD also allows to revive  already pruned weights.  We  demonstrate  the  improvements achieved  by  EBD  using  three  publicly available data sets.
Text-Based Information Retrieval Using Exponentiated Gradient Descent
Authors: James P. Callan, Andrew G. Barto Ron Papka
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: The following  investigates  the  use  of single-neuron  learning  algo(cid:173) rithms  to improve  the  performance of text-retrieval  systems  that  accept  natural-language  queries.  A  retrieval  process  is  explained  that transforms the natural-language query into the query syntax  of a real retrieval system:  the initial query is expanded using statis(cid:173) tical and learning techniques and is then used for document ranking  and binary classification.  The results  of experiments suggest that  Kivinen and Warmuth's Exponentiated Gradient Descent learning  algorithm works significantly better than previous approaches.
An Analog Implementation of the Constant Average Statistics Constraint For Sensor Calibration
Authors: Yu-Ming Chiang John G. Harris
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: We  use  the  constant  statistics  constraint  to calibrate an  array  of  sensors that contains gain and offset variations.  This algorithm has  been mapped to analog hardware and designed and fabricated with  a 2um CMOS technology.  Measured results from the chip show that  the system achieves invariance to gain and offset  variations of the  input signal.
A Neural Model of Visual Contour Integration
Authors: Zhaoping Li
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: We  introduce  a  neurobiologically plausible model  of contour inte(cid:173) gration from visual inputs of individual oriented edges.  The model  is  composed  of interacting  excitatory  neurons  and  inhibitory  in(cid:173) terneurons, receives visual inputs via oriented receptive fields  (RFs)  like those in VI.  The RF centers are distributed in space.  At each  location,  a  finite  number  of cells  tuned  to  orientations  spanning  1800  compose a  model hypercolumn.  Cortical interactions modify  neural activities  produced by  visual  inputs,  selectively amplifying  activities for edge elements belonging to smooth input contours.  El(cid:173) ements within one contour produce synchronized neural activities.  We  show  analytically  and  empirically  that  contour  enhancement  and  neural  synchrony  increase  with  contour  length,  smoothness  and closure, as  observed experimentally.  This model gives testable  predictions, and in  addition, introduces a  feedback  mechanism  al(cid:173) lowing  higher  visual  centers  to  enhance,  suppress,  and  segment  contours.
Unsupervised Learning by Convex and Conic Coding
Authors: H. Sebastian Seung Daniel D. Lee
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Unsupervised  learning algorithms  based  on  convex  and  conic  en(cid:173) coders are proposed.  The encoders find  the closest convex or conic  combination of basis vectors to the input.  The learning algorithms  produce basis vectors that minimize the reconstruction error of the  encoders.  The convex  algorithm develops  locally  linear models  of  the  input,  while  the  conic  algorithm  discovers  features.  Both  al(cid:173) gorithms are used to model handwritten digits and compared with  vector quantization and principal component analysis.  The neural  network implementations involve feedback connections that project  a  reconstruction back to the input layer.
Reconstructing Stimulus Velocity from Neuronal Responses in Area MT
Authors: James R. Cavanaugh, J. Anthony Movshon Wyeth Bair
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: We  employed  a  white-noise velocity  signal to study the dynamics  of the response of single neurons in the cortical area MT to visual  motion.  Responses were quantified using reverse correlation, opti(cid:173) mal linear reconstruction filters,  and reconstruction signal-to-noise  ratio  (SNR).  The  SNR and  lower  bound estimates  of information  rate were  lower  than we  expected.  Ninety percent of the informa(cid:173) tion was transmitted below  18 Hz, and the highest lower bound on  bit rate was  12  bits/so  A  simulated opponent motion energy sub(cid:173) unit with Poisson spike statistics was  able to out-perform the MT  neurons.  The temporal integration window, measured from  the re(cid:173) verse  correlation half-width,  ranged  from  30-90 ms.  The window  was  narrower  when  a  stimulus  moved  faster,  but  did  not  change  when temporal frequency  was  held constant.
Multidimensional Triangulation and Interpolation for Reinforcement Learning
Authors: Scott Davies
Published in: Advances in Neural Information Processing Systems 9  (NIPS 1996) (Main Conference Track), 1996

Abstract: Dynamic  Programming,  Q-Iearning  and  other  discrete  Markov  Decision  Process  solvers  can be -applied to continuous d-dimensional state-spaces  by  quantizing the state space into an array of boxes.  This is often problematic  above  two dimensions:  a  coarse  quantization can lead  to poor policies, and  fine  quantization is too expensive.  Possible solutions are variable-resolution  discretization,  or function  approximation  by  neural  nets.  A  third option,  which  has  been  little  studied  in  the  reinforcement  learning  literature,  is  interpolation on  a  coarse  grid.  In  this  paper we  study  interpolation tech(cid:173) niques  that  can  result  in  vast  improvements in  the  online  behavior of the  resulting  control  systems:  multilinear  interpolation,  and  an  interpolation  algorithm  based  on  an  interesting  regular  triangulation  of d-dimensional  space.  We  adapt  these  interpolators  under  three  reinforcement  learning  paradigms:  (i)  offline value iteration with  a  known  model,  (ii)  Q-Iearning,  and  (iii)  online  value  iteration  with  a  previously  unknown  model  learned  from data.  We  describe empirical results,  and the resulting implications for  practical learning of continuous non-linear dynamic control. 
1  GRID-BASED INTERPOLATION TECHNIQUES  Reinforcement  learning algorithms generate functions  that map states to  "cost-t
•  Fine grids may be used  in one or two dimensions.  Above  two dimensions,  fine  grids  are  too expensive.  Value  functions  can  be  discontinuous,  which  (as we will see) can lead to su boptimalities even with very fine  discretization  in two dimensions . 
•  Neural  nets have  been  used  in conjunction  with TD  [Sutton,  1988]  and  Q-Iearning [Watkins,  1989]  in very  high dimensional spaces [Tesauro,  1991,  Crites  and  Barto,  1996].  While promising, it  is  not  always  clear  that they  produce  the  accurate  value  functions  that  might  be  needed  for  fine  near(cid:173) optimal control of dynamic systems, and the most commonly used  methods  of applying value  iteration or policy iteration with a  neural-net  value func(cid:173) tion are often unstable.  [Boyan  and  Moore,  1995].