37 citations · 64 across the 6 of their papers we have counts for
6 papers
Advanced deep architecture pruning using single filter performance
Yarden Tzach, Yuval Meir, Ronit D. Gross +3
Pruning the parameters and structure of neural networks reduces the computational complexity, energy consumption, and latency during inference. Recently, a novel underlying mechani…
Role of Delay in Brain Dynamics
Yuval Meir, Ofek Tevet, Yarden Tzach +2
Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study…
Towards a universal mechanism for successful deep learning
Yuval Meir, Yarden Tzach, Shiri Hodassman +2
Recently, the underlying mechanism for successful deep learning (DL) was presented based on a quantitative method that measures the quality of a single filter in each layer of a DL…
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…