210 citations · 984 across the 29 of their papers we have counts for
4 papers · 1 filter
"Zero-Shot" Super-Resolution using Deep Internal Learning
Assaf Shocher, Nadav Cohen, Michal Irani
Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance in the past few years. However, being supervised, these SR methods are restricted to specific training…
Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions
Nadav Cohen, Or Sharir, Yoav Levine +3
The driving force behind convolutional networks - the most successful deep learning architecture to date, is their expressive power. Despite its wide acceptance and vast empirical…
Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design
Yoav Levine, David Yakira, Nadav Cohen +1
Deep convolutional networks have witnessed unprecedented success in various machine learning applications. Formal understanding on what makes these networks so successful is gradua…
Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions
Nadav Cohen, Ronen Tamari, Amnon Shashua
The driving force behind deep networks is their ability to compactly represent rich classes of functions. The primary notion for formally reasoning about this phenomenon is express…