aam9n@uvaee.ee.virginia.EDU (Ali Minai) (03/22/89)
I am looking for some papers presented at the first INNS meeting last year. I have the abstracts, of course (Neural Networks, Vol 1), but is there any way to get the full papers except by contacting the authors directly (e.g. from INNS)? Just in case, I include a list of the papers I need most, though there are a few others too that I could use. ---------------------------------------------------------------------- M. Pavel, H. Jimison and R.T. Moore; CONSTRAINTS ON GENERALIZATION BY ADAPTIVE NETWORKS. A. Bergman; VARIATION AND SELECTION: AN EVOLUTIONARY MODEL OF LEARNING IN NEURAL NETWORKS. C.L. Giles, R.D. Griffin and T. Maxwell; COMPUTATIONAL ADVANTAGES OF HIGHER ORDER NEURAL NETWORKS. M.T. Musavi, A. Rajavelu, S. Sahai, J. Zhao; ANALYSIS AND GENERALIZATION OF BACK PROPAGATION IN NEURAL NETWORKS. W.A. Sethares; A CONVERGENCE THEOREM FOR THE MODIFIED DELTA RULE. M.A. Styblinski and T. - S. Tang; STOCHASTIC APPROXIMATION ALGORITHM WITH FUNCTION SMOOTHING VS. SIMULATED ANNEALING. M. Van Alstyne; REMAKING THE NEURAL NET: A PERCEPTRON LOGIC UNIT. M. Caudill; BENCHMARKING THE PERFORMANCE OF BACKPROPAGATION AND COUTERPROPAGATION NETWORKS. Yann le Cun; USING CURVATURE INFORMATION TO IMPROVE BACKPROPAGATION. T. Poggio; LEARNING, REGULARIZATION AND SPLINES. T. Samad; BACK-PROPAGATION IS SIGNIFICANTLY FASTER IF THE EXPECTED VALUE OF THE SOURCE UNIT IS USED FOR UPDATE. F.J. Smieja; THE SIGNIFICANCE OF UNDERLYING CORRELATIONS IN THE TRAINING OF A LAYERED NET. D.J. Sobajic, D.T. Lee and Yoh-Han Pao; INCREASED EFFECTIVENESS OF LEARNING BY LOCAL NEURAL FEEDBACK. D. Whitley; APPLYING GENETIC ALGORITHMS TO NEURAL NETWORK PROBLEMS. A. Wieland and R. Leighton; SHAPING SCHEDULES AS A METHOD FOR ACCELERATED LEARNING. E. Levin and R. Gewirtzman; ARTIFICIAL NEURAL NETWORKS FOR MODELING DISCRETE DYNAMICAL SYSTEMS. R. Hecht-Nielsen; THEORY OF THE BACKPROPAGATION NEURAL NETWORK. J.J. Kaufman; NEURAL NETS AS MODELS OF GLOBAL SOCIO-POLITICAL BEHAVIOR. P.A. Ramamoorthy, G. Govind and V.K. Iyer; SIGNAL MODELING AND PREDICTION USING NEURAL NETWORKS. D. Wolpert; ALTERNATIVE GENERALIZERS TO NEURAL NETS. J.G. Cailton, B. Angeniol and E. Markade; CONSTRAINED BACK-PROPAGATION. A.J. Surkan; APL IMPLEMENTATION OF A NEURAL NETWORK WITH DYNAMICALLY GENERATED MIDDLE LAYERS OF ARBITRARY NUMBER AND LENGTH. P.D. Wasserman; COMBINED BACKPROPAGATION/CAUCHY MACHINE. ----------------------------------------------------------------- Any information or material will be greatly appreciated. Ali Minai Dept. of Electrical Engg. Thornton Hall University of Virginia Charlottesville, VA 22901 aam9n@uvaee.ee.Virginia.EDU