180 citations · 2.3k across the 85 of their papers we have counts for
86 papers
Statistical Mechanics of Learning via Reverberation in Bidirectional Associative Memories
Martino Salomone Centonze, Ido Kanter, Adriano Barra
We study bi-directional associative neural networks that, exposed to noisy examples of an extensive number of random archetypes, learn the latter (with or without the presence of a…
The mechanism underlying successful deep learning
Yarden Tzach, Yuval Meir, Ofek Tevet +4
Deep architectures consist of tens or hundreds of convolutional layers (CLs) that terminate with a few fully connected (FC) layers and an output layer representing the possible lab…
Enhancing the accuracies by performing pooling decisions adjacent to the output layer
Yuval Meir, Yarden Tzach, Ronit D. Gross +3
Learning classification tasks of (2^nx2^n) inputs typically consist of \le n (2x2) max-pooling (MP) operators along the entire feedforward deep architecture. Here we show, using th…
Efficient shallow learning as an alternative to deep learning
Yuval Meir, Ofek Tevet, Yarden Tzach +3
The realization of complex classification tasks requires training of deep learning (DL) architectures consisting of tens or even hundreds of convolutional and fully connected hidde…
Power-law Scaling to Assist with Key Challenges in Artificial Intelligence
Yuval Meir, Shira Sardi, Shiri Hodassman +4
Power-law scaling, a central concept in critical phenomena, is found to be useful in deep learning, where optimized test errors on handwritten digit examples converge as a power-la…
Learning on tree architectures outperforms a convolutional feedforward network
Yuval Meir, Itamar Ben-Noam, Yarden Tzach +2
Advanced deep learning architectures consist of tens of fully connected and convolutional hidden layers, currently extended to hundreds, are far from their biological realization.…