37 citations · 115 across the 11 of their papers we have counts for
11 papers
Unified CNNs and transformers underlying learning mechanism reveals multi-head attention modus vivendi
Ella Koresh, Ronit D. Gross, Yuval Meir +3
Convolutional neural networks (CNNs) evaluate short-range correlations in input images which progress along the layers, whereas vision transformer (ViT) architectures evaluate long…
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…
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.…
Brain inspired neuronal silencing mechanism to enable reliable sequence identification
Shiri Hodassman, Yuval Meir, Karin Kisos +5
Real-time sequence identification is a core use-case of artificial neural networks (ANNs), ranging from recognizing temporal events to identifying verification codes. Existing meth…
Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms
Shira Sardi, Roni Vardi, Yuval Meir +4
Attempting to imitate the brain functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not d…