Neural Networks

This solution of the dynamics indicates that many- neuron interacting neural networks are far better at pattern discrimination than two-neuron interacting networks, i.e.,

Neural networks : iterative unlearning algorithm converging to the projector rule

In the first part of this paper, we present numerical simulations on a model of long term memory, which is an irreversible version of learning within bounds : those

“Echo State Queueing Networks: a combination of Reservoir Computing and Random Neural Networks”, S. Rubino, in Probability in the Engineering and Informational

The Davis-Putnam-Logemann-Loveland (DPLL) algorithm formulates SAT as a search problem, resulting in a valuation of variables that satisfies the sentence, or a tree

As an application, from the universal approximation property of neural networks (NN), we de- velop a learning theory for the NN-based autoregressive functions of the model, where

chapter 1 is an introduction which is composed of four sections: Sec.1.1 presents machine learning backgrounds with more focus on regularization; Sec.1.2 presents an introduction

Using cross-lingual language model pretraining, we outperform by a large mar- gin the previous state of the art in supervised and unsupervised neural machine translation, where