Nadav Cohen | DeepAI

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∙ 06/22/2023

Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions

Inertial sensing is used in many applications and platforms, ranging fro...

0 Nadav Cohen, et al. ∙

∙ 03/20/2023

What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement

The question of what makes a data distribution suitable for deep learnin...

0 Yotam Alexander, et al. ∙

∙ 12/22/2022

Set-Transformer BeamsNet for AUV Velocity Forecasting in Complete DVL Outage Scenarios

Autonomous underwater vehicles (AUVs) are regularly used for deep ocean ...

0 Nadav Cohen, et al. ∙

∙ 11/29/2022

On the Ability of Graph Neural Networks to Model Interactions Between Vertices

Graph neural networks (GNNs) are widely used for modeling complex intera...

0 Noam Razin, et al. ∙

∙ 10/25/2022

Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Network

Overparameterization in deep learning typically refers to settings where...

0 Edo Cohen-Karlik, et al. ∙

∙ 10/22/2022

Deep Linear Networks for Matrix Completion – An Infinite Depth Limit

The deep linear network (DLN) is a model for implicit regularization in ...

0 Nadav Cohen, et al. ∙

∙ 10/20/2022

LiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements

Autonomous underwater vehicles (AUVs) are employed for marine applicatio...

0 Nadav Cohen, et al. ∙

∙ 06/27/2022

BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation

Autonomous underwater vehicles (AUV) perform various applications such a...

0 Nadav Cohen, et al. ∙

∙ 03/07/2022

Semantic Segmentation in Art Paintings

Semantic segmentation is a difficult task even when trained in a supervi...

0 Nadav Cohen, et al. ∙

∙ 02/09/2022

On the Implicit Bias of Gradient Descent for Temporal Extrapolation

Common practice when using recurrent neural networks (RNNs) is to apply ...

0 Edo Cohen-Karlik, et al. ∙

∙ 01/27/2022

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

In the pursuit of explaining implicit regularization in deep learning, p...

0 Noam Razin, et al. ∙

∙ 07/14/2021

Continuous vs. Discrete Optimization of Deep Neural Networks

Existing analyses of optimization in deep learning are either continuous...

0 Omer Elkabetz, et al. ∙

∙ 02/19/2021

Implicit Regularization in Tensor Factorization

Implicit regularization in deep learning is perceived as a tendency of g...

0 Noam Razin, et al. ∙

∙ 05/13/2020

Implicit Regularization in Deep Learning May Not Be Explainable by Norms

Mathematically characterizing the implicit regularization induced by gra...

5 Noam Razin, et al. ∙

∙ 05/31/2019

Implicit Regularization in Deep Matrix Factorization

Efforts to understand the generalization mystery in deep learning have l...

4 Sanjeev Arora, et al. ∙

∙ 10/04/2018

A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks

We analyze speed of convergence to global optimum for gradient descent t...

8 Sanjeev Arora, et al. ∙

∙ 03/26/2018

Bridging Many-Body Quantum Physics and Deep Learning via Tensor Networks

The harnessing of modern computational abilities for many-body wave-func...

0 Yoav Levine, et al. ∙

∙ 02/19/2018

On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization

Conventional wisdom in deep learning states that increasing depth improv...

0 Sanjeev Arora, et al. ∙

∙ 12/17/2017

"Zero-Shot" Super-Resolution using Deep Internal Learning

Deep Learning has led to a dramatic leap in Super-Resolution (SR) perfor...

0 Assaf Shocher, et al. ∙

∙ 05/05/2017

Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions

The driving force behind convolutional networks - the most successful de...

0 Nadav Cohen, et al. ∙

∙ 04/05/2017

Deep Learning and Quantum Entanglement: Fundamental Connections with Implications to Network Design

Deep convolutional networks have witnessed unprecedented success in vari...

0 Yoav Levine, et al. ∙

∙ 03/20/2017

Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions

The driving force behind deep networks is their ability to compactly rep...

0 Nadav Cohen, et al. ∙

∙ 10/13/2016

Tractable Generative Convolutional Arithmetic Circuits

Casting neural networks in generative frameworks is a highly sought-afte...

0 Or Sharir, et al. ∙

∙ 05/22/2016

Inductive Bias of Deep Convolutional Networks through Pooling Geometry

Our formal understanding of the inductive bias that drives the success o...

0 Nadav Cohen, et al. ∙

∙ 03/01/2016

Convolutional Rectifier Networks as Generalized Tensor Decompositions

Convolutional rectifier networks, i.e. convolutional neural networks wit...

0 Nadav Cohen, et al. ∙

∙ 09/16/2015

On the Expressive Power of Deep Learning: A Tensor Analysis

It has long been conjectured that hypotheses spaces suitable for data th...

0 Nadav Cohen, et al. ∙

∙ 06/09/2015

Deep SimNets

We present a deep layered architecture that generalizes convolutional ne...

0 Nadav Cohen, et al. ∙

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