index int64 0 20.3k | text stringlengths 0 1.3M | year stringdate 1987-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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1,200 | Multi-Grid Methods for Reinforcement Learning in Controlled Diffusion Processes Stephan Pareigis stp@numerik.uni-kiel.de Lehrstuhl Praktische Mathematik Christian-Albrechts-U niversi tat Kiel Kiel, Germany Abstract Reinforcement learning methods for discrete and semi-Markov decision problems suc... | 1996 | 145 |
1,201 | Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons Wolfgang Maass Institute for Theoretical Computer Science Technische Universitaet Graz, Klosterwiesgasse 32/2 A-80lO Graz, Austria, e-mail: maass@igLtu-graz.ac.at Abstract We exhibit a novel way of si... | 1996 | 146 |
1,202 | Edges are the 'Independent Components' of Natural Scenes. Anthony J. Bell and Terrence J. Sejnowski Computational Neurobiology Laboratory The Salk Institute 10010 N. Torrey Pines Road La Jolla, California 92037 tony@salk.edu, terry@salk.edu Abstract Field (1994) has suggested that neurons wit... | 1996 | 147 |
1,203 | For valid generalization, the size of the weights is more important than the size of the network Peter L. Bartlett Department of Systems Engineering Research School of Information Sciences and Engineering Australian National University Canberra, 0200 Australia Peter .BartlettClanu .edu.au Abs... | 1996 | 148 |
1,204 | Neural Network Modeling of Speech and Music Signals Axel Robel Technical University Berlin, Einsteinufer 17, Sekr. EN-8, 10587 Berlin, Germany Tel: +49-30-31425699, FAX: +49-30-31421143, email: roebel@kgw.tu-berlin.de Abstract Time series prediction is one of the major applications of neural networks.... | 1996 | 149 |
1,205 | Unification of Information Maximization and Minimization Ryotaro Kamimura Information Science Laboratory Tokai University 1117 Kitakaname Hiratsuka Kanagawa 259-12, Japan E-mail: ryo@cc.u-tokaLac.jp Abstract In the present paper, we propose a method to unify information maximization and minim... | 1996 | 15 |
1,206 | Rapid Visual Processing using Spike Asynchrony Simon J. Thorpe & Jacques Gautrais Centre de Recherche Cerveau & Cognition F-31062 Toulouse France email thorpe@cerco.ups-tlseJr Abstract We have investigated the possibility that rapid processing in the visual system could be achieved by using the ... | 1996 | 150 |
1,207 | Visual Cortex Circuitry and Orientation Tuning Trevor M undel Department of Neurology University of Chicago Chicago, IL 60637 mundel@math.uchicago.edu Alexander Dimitrov Department of Mathematics University of Chicago Chicago, IL 60637 a-dimitrov@ucllicago.edu Jack D. Cowan Depart... | 1996 | 151 |
1,208 | Orientation contrast sensitivity from long-range interactions in visual cortex Klaus R. Pawelzik, Udo Ernst, Fred Wolf, Theo Geisel Institut fur Theoretische Physik and SFB 185 Nichtlineare Dynamik, Universitat Frankfurt, D-60054 Frankfurt/M., and MPI fur Stromungsforschung, D-37018 Gottingen, Germany ... | 1996 | 152 |
1,209 | Multi-Task Learning for Stock Selection Joumana Ghosn Dept. Informatique et Recherche Operationnelle Universite de Montreal Montreal, Qc H3C-3J7 ghosn~iro.umontreal.ca Yoshua Bengio * Dept. Informatique et Recherche Operationnelle Universite de Montreal Montreal, Qc H3C-3J7 bengioy~i... | 1996 | 16 |
1,210 | Cholinergic Modulation Preserves Spike Timing Under Physiologically Realistic Fluctuating Input Akaysha C. Tang The Salk Institute Howard Hughes Medical Institute Computational Neurobiology Laboratory La Jolla, CA 92037 Terrence J. Sejnowski The Salk Institute Andreas M. Bartels Zoologi... | 1996 | 17 |
1,211 | Analytical Mean Squared Error Curves in Temporal Difference Learning Satinder Singh Department of Computer Science University of Colorado Boulder, CO 80309-0430 baveja@cs.colorado.edu Abstract Peter Dayan Brain and Cognitive Sciences E25-210, MIT Cambridge, MA 02139 bertsekas@lids.mi... | 1996 | 18 |
1,212 | Dynamics of Training Siegfried Bos* Lab for Information Representation RIKEN, Hirosawa 2-1, Wako-shi Saitama 351-01, Japan Abstract Manfred Opper Theoretical Physics III University of Wiirzburg 97074 Wiirzburg, Germany A new method to calculate the full training process of a neural network... | 1996 | 19 |
1,213 | A variational principle for model-based morphing Lawrence K. Saul'" and Michael I. Jordan Center for Biological and Computational Learning Massachusetts Institute of Technology 79 Amherst Street, EI0-034D Cambridge, MA 02139 Abstract Given a multidimensional data set and a model of its density, ... | 1996 | 2 |
1,214 | A Constructive RBF Network for Writer Adaptation John C. Platt and Nada P. Matic Synaptics, Inc. 2698 Orchard Parkway San Jose, CA 95134 platt@synaptics.com, nada@synaptics.com Abstract This paper discusses a fairly general adaptation algorithm which augments a standard neural network to incr... | 1996 | 20 |
1,215 | MLP can provably generalise much better than VC-bounds indicate. A. Kowalczyk and H. Ferra Telstra Research Laboratories 770 Blackburn Road, Clayton, Vic. 3168, Australia ({ a.kowalczyk, h.ferra}@trl.oz.au) Abstract Results of a study of the worst case learning curves for a particular class of prob... | 1996 | 21 |
1,216 | 3D Object Recognition: A Model of View-Tuned Neurons Emanuela Bricolo Tomaso Poggio Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 {emanuela,tp}Gai.mit.edu Nikos Logothetis Baylor College of Medicine Houston, TX 77030 nikosGbcmvision... | 1996 | 22 |
1,217 | Learning Bayesian belief networks with neural network estimators Stefano Monti* *Intelligent Systems Program University of Pittsburgh 901M CL, Pittsburgh, PA - 15260 smonti~isp.pitt.edu Gregory F. Cooper*'''' "Center for Biomedical Informatics University of Pittsburgh 8084 Forbes Tower, Pi... | 1996 | 23 |
1,218 | Approximate Solutions to Optimal Stopping Problems John N. Tsitsiklis and Benjamin Van Roy Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge, MA 02139 e-mail: jnt@mit.edu, bvr@mit.edu Abstract We propose and analyze an algorithm that approximates sol... | 1996 | 24 |
1,219 | An Orientation Selective Neural Network for Pattern Identification in Particle Detectors Halina Abramowicz, David Horn, Ury Naftaly, Carmit Sahar- Pikielny School of Physics and Astronomy, Tel Aviv University Tel Aviv 69978, Israel halinaOpost.tau.ac.il, horn~neuron.tau.ac.il ury~ost.tau.ac.il, car... | 1996 | 25 |
1,220 | Early Brain Damage Volker Tresp, Ralph Neuneier and Hans Georg Zimmermann* Siemens AG, Corporate Technologies Otto-Hahn-Ring 6 81730 Miinchen, Germany Abstract Optimal Brain Damage (OBD) is a method for reducing the number of weights in a neural network. OBD estimates the increase in cost function ... | 1996 | 26 |
1,221 | Text-Based Information Retrieval Using Exponentiated Gradient Descent Ron Papka, James P. Callan, and Andrew G. Barto * Department of Computer Science University of Massachusetts Amherst, MA 01003 papka@cs.umass.edu, callan@cs.umass.edu, barto@cs.umass.edu Abstract The following investigates the... | 1996 | 27 |
1,222 | An Analog Implementation of the Constant Statistics Constraint For Sensor Calibration John G. Harris and Yu-Ming Chiang Computational Neuro-Engineering Laboratory Department of Computer and Electrical Engineering University of Florida Gainesville, FL 32611 Abstract We use the constant statist... | 1996 | 28 |
1,223 | A neural model of visual contour integration Zhaoping Li Computer Science, Hong Kong University of Science and Technology Clear Water Bay, Hong Kong zhaoping~uxmail.ust.hkl Abstract We introduce a neurobiologically plausible model of contour integration from visual inputs of individual oriented edg... | 1996 | 29 |
1,224 | ARTEX: A Self-Organizing Architecture for Classifying Image Regions Stephen Grossberg and James R. Williamson {steve, jrw}@cns.bu.edu Center for Adaptive Systems and Department of Cognitive and Neural Systems Boston University 677 Beacon Street, Boston, MA 02215 Abstract A self-organizing ... | 1996 | 3 |
1,225 | Unsupervised Learning by Convex and Conic Coding D. D. Lee and H. S. Seung Bell Laboratories, Lucent Technologies Murray Hill, NJ 07974 {ddlee I seung}Obell-labs. com Abstract Unsupervised learning algorithms based on convex and conic encoders are proposed. The encoders find the closest convex or c... | 1996 | 30 |
1,226 | Reconstructing Stimulus Velocity from Neuronal Responses in Area MT Wyeth Bair, James R. Cavanaugh, J. Anthony Movshon Howard Hughes Medical Institute, and Center for Neural Science New York University 4 Washington Place, Room 809 New York, NY 10003 wyeth@cns.nyu.edu, jamesc@cns.nyu.edu, tony@cn... | 1996 | 31 |
1,227 | Multidimensional Triangulation and Interpolation for Reinforcement Learning Scott Davies scottd@cs.cmu.edu Department of Computer Science, Carnegie Mellon University 5000 Forbes Ave, Pittsburgh, PA 15213 Abstract Dynamic Programming, Q-Iearning and other discrete Markov Decision Process solvers ... | 1996 | 32 |
1,228 | Viewpoint invariant face recognition using independent component analysis and attractor networks Marian Stewart Bartlett University of California San Diego The Salk Institute La Jolla, CA 92037 marni@salk.edu Terrence J. Sejnowski University of California San Diego Howard Hughes Medical In... | 1996 | 33 |
1,229 | On the Effect of Analog Noise in Discrete-Time Analog Computations Wolfgang Maass Institute for Theoretical Computer Science Technische Universitat Graz* Abstract Pekka Orponen Department of Mathematics University of Jyvaskylat We introduce a model for noise-robust analog computations with ... | 1996 | 34 |
1,230 | Combined Weak Classifiers Chuanyi Ji and Sheng Ma Department of Electrical, Computer and System Engineering Rensselaer Polytechnic Institute, Troy, NY 12180 chuanyi@ecse.rpi.edu, shengm@ecse.rpi.edu Abstract To obtain classification systems with both good generalization performance and efficiency in s... | 1996 | 35 |
1,231 | Reinforcement Learning for Dynamic C·hannel Allocation in Cellular Telephone Systems Satinder Singh Department of Computer Science University of Colorado Boulder, CO 80309-0430 bavej a@cs.colorado.edu Dimitri Bertsekas Lab. for Info. and Decision Sciences MIT Cambridge, MA 02139 bert... | 1996 | 36 |
1,232 | Neural Learning in Structured Parameter Spaces Natural Riemannian Gradient Shun-ichi Amari RIKEN Frontier Research Program, RIKEN, Hirosawa 2-1, Wako-shi 351-01, Japan amari@zoo.riken.go.jp Abstract The parameter space of neural networks has a Riemannian metric structure. The natural Riemannian ... | 1996 | 37 |
1,233 | Extraction of temporal features in the electrosensory system of weakly electric fish Fabrizio GabbianiDivision of Biology 139-74 Caltech Pasadena, CA 91125 RalfWessel Department of Biology Univ. of Cal. San Diego La J oBa, CA 92093-0357 Walter Metzner Department of Biology Univ. of C... | 1996 | 38 |
1,234 | A Model of Recurrent Interactions in Primary Visual Cortex ElDanuel Todorov, Athanassios Siapas and David SOlDers Dept. of Brain and Cognitive Sciences E25-526, MIT, Cambridge, MA 02139 Email: {emo, thanos,somers }@ai.mit.edu Abstract A general feature of the cerebral cortex is its massive intercon... | 1996 | 39 |
1,235 | The CONDENSATION algorithm conditional density propagation and applications to visual tracking A. Blake and M. IsardDepartment of Engineering Science, University of Oxford, Oxford OXI 3PJ, UK. Abstract The power of sampling methods in Bayesian reconstruction of noisy signals is well known. The exte... | 1996 | 4 |
1,236 | On a Modification to the Mean Field EM Algorithm in Factorial Learning A. P. Dunmur D. M. Titterington Department of Statistics Maths Building University of Glasgow Glasgow G12 8QQ, UK alan~stats.gla.ac.uk mike~stats.gla.ac.uk Abstract A modification is described to the use of mean fiel... | 1996 | 40 |
1,237 | VLSI Implementation of Cortical Visual Motion Detection Using an Analog Neural Computer Ralph Etienne-Cummings Electrical Engineering, Southern Illinois University, Carbondale, IL 62901 Jan Van der Spiegel The Moore School, University of Pennsylvania, Philadelphia, PA 19104 Naomi Takahashi... | 1996 | 41 |
1,238 | Local Bandit Approximation for Optimal Learning Problems Michael o. Duff Andrew G. Barto Department of Computer Science University of Massachusetts Amherst, MA 01003 {duff.barto}Ccs.umass.edu Abstract In general, procedures for determining Bayes-optimal adaptive controls for Markov decisio... | 1996 | 42 |
1,239 | Learning Appearance Based Models: Mixtures of Second Moment Experts Christoph 8regler and Jitendra Malik Computer Science Division University of California at Berkeley Berkeley, CA 94720 email: bregler@cs.berkeley.edu, malik@cs.berkeley.edu Abstract This paper describes a new technique for objec... | 1996 | 43 |
1,240 | Interpreting images by propagating Bayesian beliefs Yair Weiss Dept. of Brain and Cognitive Sciences Massachusetts Institute of Technology E10-120, Cambridge, MA 02139, USA yweiss<opsyche.mit.edu Abstract A central theme of computational vision research has been the realization that reliable est... | 1996 | 44 |
1,241 | Why did TD-Gammon Work? Jordan B. Pollack & Alan D. Blair Computer Science Department Brandeis University Waltham, MA 02254 {pollack,blair} @cs.brandeis.edu Abstract Although TD-Gammon is one of the major successes in machine learning, it has not led to similar impressive breakthroughs in temporal ... | 1996 | 45 |
1,242 | NeuroScale: Novel Topographic Feature Extraction using RBF Networks David Lowe D.LoweOaston.ac.uk Michael E. Tipping H.E.TippingOaston.ac.uk Neural Computing Research Group Aston University, Aston Triangle, Birmingam B4 7ET1 UK http://www.ncrg.aston.ac.uk/ . Abstract Dimension-reducing ... | 1996 | 46 |
1,243 | Time Series Prediction Using Mixtures of Experts Ron Meir Assaf J. Zeevi Information Systems Lab Department of Electrical Engineering Stanford University Stanford, CA. 94305 azeevi~isl.stanford.edu Department of Electrical Engineering Technion Haifa 32000, Israel rmeir~ee.technion.ac... | 1996 | 47 |
1,244 | MIMIC: Finding Optima by Estimating Probability Densities Jeremy S. De Bonet, Charles L. Isbell, Jr., Paul Viola Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract In many optimization problems, the structure of solutions reflects complex relati... | 1996 | 48 |
1,245 | Neural network models of chemotaxis the nematode Caenorhabditis elegans Thomas C. Ferree, Ben A. Marcotte, Shawn R. Lockery Institute of Neuroscience, University of Oregon, Eugene, Oregon 97403 Abstract We train recurrent networks to control chemotaxis in a computer model of the nematode C. elegans. T... | 1996 | 49 |
1,246 | A Silicon Model of Amplitude Modulation Detection in the Auditory Brainstem And~ van Schaik, Eric Fragniere, Eric Vittoz MANIRA Center for Neuromimetic Systems Swiss Federal Institute of Technology CH-lOlS Lausanne email: Andre.van_Schaik@di.epfl.ch Abstract Detectim of the periodicity of amp... | 1996 | 5 |
1,247 | GTM: A Principled Alternative to the Self-Organizing Map Christopher M. Bishop C.M .Bishop@aston.ac.uk Markus Svensen Christopher K. I. Williams svensjfm@aston.ac.uk C.K.r. Williams@aston.ac.uk Neural Computing Research Group Aston University, Birmingham, B4 7ET, UK http://www.ncrg.aston.a... | 1996 | 50 |
1,248 | Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing· Vladimir Vapnik AT&T Research 101 Crawfords Corner Holmdel, N J 07733 vlad@research.att.com Steven E. Golowich Bell Laboratories 700 Mountain Ave. Murray Hill, NJ 07974 golowich@bell-Iabs.... | 1996 | 51 |
1,249 | Smoothing Regularizers for Projective Basis Function Networks John E. Moody and Thorsteinn S. Rognvaldsson * Department of Computer Science, Oregon Graduate Institute PO Box 91000, Portland, OR 97291 moody@cse.ogi.edu denni@cca.hh.se Abstract Smoothing regularizers for radial basis functions hav... | 1996 | 52 |
1,250 | Bangs. Clicks, Snaps, Thuds and Whacks: an Architecture for Acoustic Transient Processing Fernando J. Pineda(l) fernando. pineda@jhuapl.edu Gert Cauwenberghs(2) gert@jhunix.hcf.jhu.edu R. Timothy Edwards(2) tim@bach.ece.jhu.edu (iThe Applied Physics Laboratory The Johns Hopkins University ... | 1996 | 53 |
1,251 | Salient Contour Extraction by Temporal Binding in a Cortically-Based Network Shih.Cheng Yen and Leif H. Finkel Department of Bioengineering and Institute of Neurological Sciences University of Pennsylvania Philadelphia, PA 19104, U. S. A. syen @jupiter.seas.upenn.edu leif@jupiter.seas.upenn.edu ... | 1996 | 54 |
1,252 | Learning Decision Theoretic Utilities Through Reinforcement Learning Magnus Stensmo Computer Science Division University of California Berkeley, CA 94720, U.S.A. magnus@cs.berkeley.edu Terrence J. Sejnowski Howard Hughes Medical Institute The Salk Institute 10010 North Torrey Pines Road ... | 1996 | 55 |
1,253 | Spatial Decorrelation in Orientation Tuned Cortical Cells Alexander Dimitrov Department of Mathematics University of Chicago Chicago, IL 60637 a-dimitrov@uchicago.edu Jack D. Cowan Department of Mathematics University of Chicago Chicago, IL 60637 cowan@math.uchicago.edu Abstract I... | 1996 | 56 |
1,254 | Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches Mahesan Niranjan Cambridge University Engineering Department Cambridge CB2 IPZ, England niranjan@eng.cam.ac.uk Abstract This paper shows how the prices of option contracts traded in financial markets... | 1996 | 57 |
1,255 | Are Hopfield Networks Faster Than Conventional Computers? Ian Parberry* and Hung-Li Tsengt Department of Computer Sciences University of North Texas P.O. Box 13886 Denton, TX 76203-6886 Abstract It is shown that conventional computers can be exponentiallx faster than planar Hopfield networks:... | 1996 | 58 |
1,256 | Learning From Demonstration Stefan Schaal sschaal @cc .gatech.edu; http://www.cc.gatech.edulfac/Stefan.Schaal College of Computing, Georgia Tech, 801 Atlantic Drive, Atlanta, GA 30332-0280 ATR Human Information Processing, 2-2 Hikaridai, Seiko-cho, Soraku-gun, 619-02 Kyoto Abstract By now it is widely... | 1996 | 59 |
1,257 | Adaptive On-line Learning in Changing Environments Noboru Murata, Klaus-Robert Miiller, Andreas Ziehe GMD-First, Rudower Chaussee 5, 12489 Berlin, Germany {mura.klaus.ziehe}~first.gmd.de Shun-ichi Amari Laboratory for Information Representation, RIKEN Hirosawa 2-1, Wako-shi, Saitama 351-01, Japan ... | 1996 | 6 |
1,258 | Clustering Sequences with Hidden Markov Models Padhraic Smyth Information and Computer Science University of California, Irvine CA 92697-3425 smyth~ics.uci.edu Abstract This paper discusses a probabilistic model-based approach to clustering sequences, using hidden Markov models (HMMs). The probl... | 1996 | 60 |
1,259 | Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs Rich Caruana JPRC and Carnegie Mellon University Pittsburgh, PA 15213 caruana@cs.cmu.edu Virginia R. de Sa Sloan Center for Theoretical Neurobiology and W. M. Keck Center for Integrative Neuroscience University of C... | 1996 | 61 |
1,260 | Hidden Markov decision trees Michael I. Jordan*, Zoubin Ghahramanit, and Lawrence K. Saul* {jordan.zoubin.lksaul}~psyche.mit.edu *Center for Biological and Computational Learning Massachusetts Institute of Technology Cambridge, MA USA 02139 t Department of Computer Science University of Toronto ... | 1996 | 62 |
1,261 | Representing Face Images for Emotion Classification Curtis Padgett Department of Computer Science University of California, San Diego La Jolla, CA 92034 Garrison Cottrell Department of Computer Science University of California, San Diego La Jolla, CA 92034 Abstract We compare the genera... | 1996 | 63 |
1,262 | Separating Style and Content Joshua B. Tenenbaum Dept. of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 jbtGpsyche.mit.edu William T. Freeman MERL, Mitsubishi Electric Res. Lab. 201 Broadway Cambridge, MA 02139 freemanOmerl.com Abstract We see... | 1996 | 64 |
1,263 | Contour Organisation with the EM Algorithm J. A. F. Leite and E. R. Hancock Department of Computer Science University of York, York, Y01 5DD, UK. Abstract This paper describes how the early visual process of contour organisation can be realised using the EM algorithm. The underlying computational r... | 1996 | 65 |
1,264 | Combining Neural Network Regression Estimate1s with Regularized Linear Weights Christopher J. Merz and Michael J. Pazzani Dept. of Information and Computer Science University of California, Irvine, CA 92717-3425 U.S.A. { cmerz,pazzani }@ics.uci.edu Category: Algorithms and Architectures. Abstrac... | 1996 | 66 |
1,265 | Triangulation by Continuous Embedding Marina MeiHl and Michael I. Jordan {mmp, jordan }@ai.mit.edu Center for Biological & Computational Learning Massachusetts Institute of Technology 45 Carleton St. E25-201 Cambridge, MA 02142 Abstract When triangulating a belief network we aim to obtain a junc... | 1996 | 67 |
1,266 | Bayesian Model Comparison by Monte Carlo Chaining David Barber D.Barber~aston.ac.uk Christopher M. Bishop C.M.Bishop~aston.ac.uk Neural Computing Research Group Aston University, Birmingham, B4 7ET, U.K. http://www.ncrg.aston.ac.uk/ Abstract The techniques of Bayesian inference have been a... | 1996 | 68 |
1,267 | Practical confidence and prediction intervals Tom Heskes RWCP Novel Functions SNN Laboratory; University of Nijmegen Geert Grooteplein 21, 6525 EZ Nijmegen, The Netherlands tom@mbfys.kun.nl Abstract We propose a new method to compute prediction intervals. Especially for small data sets the width of... | 1996 | 69 |
1,268 | Minimizing Statistical Bias with Queries David A. Cohn Adaptive Systems Group Harlequin, Inc. One Cambridge Center Cambridge, MA 02142 cOhnCharlequin.com Abstract I describe a querying criterion that attempts to minimize the error of a learner by minimizing its estimated squared bias. I descr... | 1996 | 7 |
1,269 | Consistent Classification, Firm and Soft Yoram Baram* Department of Computer Science Technion, Israel Institute of Technology Haifa 32000, Israel baram@cs.technion.ac.il Abstract A classifier is called consistent with respect to a given set of classlabeled points if it correctly classifies the set.... | 1996 | 70 |
1,270 | Neural Models for Part-Whole Hierarchies Maximilian Riesenhuber Peter Dayan Department of Brain & Cognitive Sciences Massachusetts Institute of Technology Cambridge, MA 02139 {max,dayan}~ai.mit.edu Abstract We present a connectionist method for representing images that explicitly addresses their... | 1996 | 71 |
1,271 | Bayesian Unsupervised Learning of Higher Order Structure Michael S. Lewicki Terrence J. Sejnowski levicki~salk.edu terry~salk.edu The Salk Institute Howard Hughes Medical Institute Computational Neurobiology Lab 10010 N. Torrey Pines Rd. La Jolla, CA 92037 Abstract Multilayer archite... | 1996 | 72 |
1,272 | An Architectural Mechanism for Direction-tuned Cortical Simple Cells: The Role of Mutual Inhibition Silvio P. Sabatini silvio@dibe.unige.it Fabio Solari fabio@dibe.unige.it Giacomo M. Bisio bisio@dibe.unige.it Department of Biophysical and Electronic Engineering PSPC Research Group Geno... | 1996 | 73 |
1,273 | Complex-Cell Responses Derived from Center-Surround Inputs: The Surprising Power of Intradendritic Computation Bartlett W. Mel and Daniel L. Ruderman Department of Biomedical Engineering University of Southern California Los Angeles, CA 90089 Kevin A. Archie Neuroscience Program University of... | 1996 | 74 |
1,274 | Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions Yoram Singer AT&T Laboratories 600 Mountain Avenue Murray Hill, NJ 07974 singer@research.att.com Manfred K. Warmuth Computer Science Department University of California Santa Cruz, CA 95064 manfred@cse.u... | 1996 | 75 |
1,275 | A Constructive Learning Algorithm for Discriminant Tangent Models Diego Sona Alessandro Sperduti Antonina Starita Dipartimento di Informatica, U niversita di Pisa Corso Italia, 40, 56125 Pisa, Italy email: {sona.perso.starita}di.unipi.it Abstract To reduce the computational complexity of clas... | 1996 | 76 |
1,276 | Effective Training of a Neural Network Character Classifier for Word Recognition Larry Yaeger Apple Computer 5540 Bittersweet Rd. Morgantown, IN 46160 larryy@apple.com Richard Lyon Apple Computer 1 Infinite Loop, MS301-3M Cupertino, CA 95014 lyon@apple.com Abstract Brandyn Webb ... | 1996 | 77 |
1,277 | Second-order Learning Algorithm with Squared Penalty Term Kazumi Saito Ryohei Nakano NTT Communication Science Laboratories 2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-02 Japan {saito,nakano }@cslab.kecl.ntt.jp Abstract This paper compares three penalty terms with respect to the efficiency of ... | 1996 | 78 |
1,278 | Multi-effect Decompositions for Financial Data Modeling Lizhong Wu & John Moody Oregon Graduate Institute, Computer Science Dept., PO Box 91000, Portland, OR 97291 also at: Nonlinear Prediction Systems, PO Box 681, University Station, Portland, OR 97207 Abstract High frequency foreign exchang... | 1996 | 79 |
1,279 | A Micropower Analog VLSI HMM State Decoder for Wordspotting John Lazzaro and John Wawrzynek CS Division, UC Berkeley Berkeley, CA 94720-1776 lazzaroGcs.berkeley.edu. johnwGcs.berkeley.edu Richard Lippmann MIT Lincoln Laboratory Room S4-121, 244 Wood Street Lexington, MA 02173-0073 rplGsst.... | 1996 | 8 |
1,280 | Microscopic Equations in Rough Energy Landscape for Neural Networks K. Y. Michael Wong Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong. E-mail: phkywong@usthk.ust.hk Abstract We consider the microscopic equations for learning problem... | 1996 | 80 |
1,281 | Dynamic features for visual speechreading: A systematic comparison Michael S. Grayl,a, Javier R. Movellanl , Terrence J. Sejnowski2,3 Departments of Cognitive Sciencel and Biology2 University of California, San Diego La Jolla, CA 92093 and Howard Hughes Medical Institute3 Computational Neurobiol... | 1996 | 81 |
1,282 | The effect of correlated input data on the dynamics of learning S~ren Halkjrer and Ole Winther CONNECT, The Niels Bohr Institute Blegdamsvej 17 2100 Copenhagen, Denmark halkjaer>winther~connect.nbi.dk Abstract The convergence properties of the gradient descent algorithm in the case of the lin... | 1996 | 82 |
1,283 | Learning temporally persistent hierarchical representations Suzanna Becker Department of Psychology McMaster University Hamilton, Onto L8S 4K1 becker@mcmaster.ca Abstract A biologically motivated model of cortical self-organization is proposed. Context is combined with bottom-up information via ... | 1996 | 83 |
1,284 | Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning Jeff G. Schneider The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 schneide@cs.cmu.edu Abstract Model learning combined with dynamic programming has been shown to be effective for learning control o... | 1996 | 84 |
1,285 | Spectroscopic Detection of Cervical Pre-Cancer through Radial Basis Function Networks Kagan Tumer kagan@pine.ece.utexas.edu Dept. of Electrical and Computer Engr. Nirmala Ramanujam nimmi@ccwf.cc.utexas.edu Biomedical Engineering Program The University of Texas at Austin The University of T... | 1996 | 85 |
1,286 | 10........,....·..... Basis Function Networks and Complexity --_.IIiIIIIIIIIlo............... JIIIL....'IIIoo4II• .,JIIIL'IIU"JIIILJIIIL in Function Learning Adam Krzyzak Department of Computer Science Concordia University Montreal, Canada krzyzak@cs.concordia.ca Tamas Linder Dept. of Math. & Comp. Sci.... | 1996 | 86 |
1,287 | Adaptively Growing Hierarchical Mixtures of Experts Jiirgen Fritsch, Michael Finke, Alex Waibel {fritsch+,finkem, waibel }@cs.cmu.edu Interactive Systems Laboratories Carnegie Mellon University Pittsburgh, PA 15213 Abstract We propose a novel approach to automatically growing and pruning Hier... | 1996 | 87 |
1,288 | On-line Policy Improvement using Monte-Carlo Search Gerald Tesauro IBM T. J. Watson Research Center P. O. Box 704 Yorktown Heights, NY 10598 Abstract Gregory R. Galperin MIT AI Lab 545 Technology Square Cambridge, MA 02139 We present a Monte-Carlo simulation algorithm for real-time poli... | 1996 | 88 |
1,289 | Blind separation of delayed and convolved sources. Te-Won Lee Max-Planck-Society, GERMANY, AND Interactive Systems Group Carnegie Mellon University Pittsburgh, PA 15213, USA tewonOes. emu. edu Anthony J. Bell Computational Neurobiology, The Salk Institute 10010 N. Torrey Pines Road L... | 1996 | 89 |
1,290 | Dual Kalman Filtering Methods for Nonlinear Prediction, Smoothing, and Estimation Eric A. Wan ericwan@ee.ogi.edu Alex T. Nelson atnelson@ee.ogi.edu Department of Electrical Engineering Oregon Graduate Institute P.O. Box 91000 Portland, OR 97291 Abstract Prediction, estimation, and smoot... | 1996 | 9 |
1,291 | A New Approach to Hybrid HMMJANN Speech Recognition Using Mutual Information Neural Networks G. Rigoll, c. Neukirchen Gerhard-Mercator-University Duisburg Faculty of Electrical Engineering Department of Computer Science Bismarckstr. 90, Duisburg, Germany ABSTRACT This paper presents a new app... | 1996 | 90 |
1,292 | Competition Among Networks Improves Committee Performance Paul W. Munro Department of Infonnation Science and Telecommunications University of Pittsburgh Pittsburgh PA 15260 munro@sis.pitt.edu Bambang Parman to Department of Health Infonnation Management University of Pittsburgh Pitt... | 1996 | 91 |
1,293 | Selective Integration: A Model for Disparity Estimation Michael S. Gray, Alexandre Pouget, Richard S. Zemel, Steven J. Nowlan, Terrence J. Sejnowski Departments of Biology and Cognitive Science University of California, San Diego La Jolla, CA 92093 and Howard Hughes Medical Institute Computat... | 1996 | 92 |
1,294 | The Neurothermostat: Predictive Optimal Control of Residential Heating Systems Michael C. Mozert, Lucky Vidmart, Robert H. Dodiert tDepartment of Computer Science tDepartment of Civil, Environmental, and Architectural Engineering University of Colorado, Boulder, CO 80309-0430 Abstract The Neurot... | 1996 | 93 |
1,295 | Softening Discrete Relaxation Andrew M. Finch, Richard C. Wilson and Edwin R. Hancock Department of Computer Science, University of York, York, Y01 5DD, UK Abstract This paper describes a new framework for relational graph matching. The starting point is a recently reported Bayesian consistency measur... | 1996 | 94 |
1,296 | A comparison between neural networks and other statistical techniques for modeling the relationship between tobacco and alcohol and cancer Tony Plate BC Cancer Agency 601 West 10th Ave, Epidemiology Vancouver BC Canada V5Z 1L3 tap@comp.vuw.ac.nz Joel Bert Dept of Chemical Engineering Un... | 1996 | 95 |
1,297 | An Apobayesian Relative of Winnow Nick Littlestone NEC Research Institute 4 Independence Way Princeton, NJ 08540 Abstract Chris Mesterharm NEC Research Institute 4 Independence Way Princeton, NJ 08540 We study a mistake-driven variant of an on-line Bayesian learning algorithm (similar to o... | 1996 | 96 |
1,298 | LSTM CAN SOLVE HARD LO G TIME LAG PROBLEMS Sepp Hochreiter Fakultat fur Informatik Technische Universitat Munchen 80290 Miinchen, Germany Abstract Jiirgen Schmidhuber IDSIA Corso Elvezia 36 6900 Lugano, Switzerland Standard recurrent nets cannot deal with long minimal time lags between relevan... | 1996 | 97 |
1,299 | 488 Solutions to the XOR Problem Frans M. Coetzee * eoetzee@eee.emu.edu Department of Electrical Engineering Carnegie Mellon University Pittsburgh, PA 15213 Virginia L. Stonick ginny@eee.emu.edu Department of Electrical Engineering Carnegie Mellon University Pittsburgh, PA 15213 Abstrac... | 1996 | 98 |
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