224 citations · 614 across the 33 of their papers we have counts for
15 papers · 1 filter
DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chess
Eli David, Nathan S. Netanyahu, Lior Wolf
We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of ches…
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders
Ido Cohen, Eli David, Nathan S. Netanyahu +2
This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencod…
DeepPainter: Painter Classification Using Deep Convolutional Autoencoders
Eli David, Nathan S. Netanyahu
In this paper we describe the problem of painter classification, and propose a novel approach based on deep convolutional autoencoder neural networks. While previous approaches rel…
DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem
Dror Sholomon, Eli David, Nathan S. Netanyahu
This paper introduces the first deep neural network-based estimation metric for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network predicts whether or not…
DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
Eli David, Nathan S. Netanyahu
This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with…
Genetic Algorithms for Evolving Computer Chess Programs
Eli David, H. Jaap van den Herik, Moshe Koppel +1
This paper demonstrates the use of genetic algorithms for evolving: 1) a grandmaster-level evaluation function, and 2) a search mechanism for a chess program, the parameter values…