210 citations · 984 across the 29 of their papers we have counts for
30 papers
Φ-Noise: Training-Free Temporal Video Conditioning via Phase-Based Noise Manipulation
Ofir Abramovich, Nadav Z. Cohen, Adi Rosenthal +1
Latent video diffusion models generate videos by progressively transforming Gaussian noise into realistic samples conditioned on text or visual inputs. However, existing conditioni…
Colorful-Noise: Training-Free Low-Frequency Noise Manipulation for Color-Based Conditional Image Generation
Nadav Z. Cohen, Ofir Abramovich, Ariel Shamir
Text-to-image diffusion models generate images by gradually converting white Gaussian noise into a natural image. White Gaussian noise is well suited for producing diverse outputs…
Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation
Nadav Z. Cohen, Oron Nir, Ariel Shamir
Balancing content fidelity and artistic style is a pivotal challenge in image generation. While traditional style transfer methods and modern Denoising Diffusion Probabilistic Mode…
Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions
Nadav Cohen, Itzik Klein
Inertial sensing is used in many applications and platforms, ranging from day-to-day devices such as smartphones to very complex ones such as autonomous vehicles. In recent years,…
What Makes Data Suitable for a Locally Connected Neural Network? A Necessary and Sufficient Condition Based on Quantum Entanglement
Yotam Alexander, Nimrod De La Vega, Noam Razin +1
The question of what makes a data distribution suitable for deep learning is a fundamental open problem. Focusing on locally connected neural networks (a prevalent family of archit…
On the Ability of Graph Neural Networks to Model Interactions Between Vertices
Noam Razin, Tom Verbin, Nadav Cohen
Graph neural networks (GNNs) are widely used for modeling complex interactions between entities represented as vertices of a graph. Despite recent efforts to theoretically analyze…
LiBeamsNet: AUV Velocity Vector Estimation in Situations of Limited DVL Beam Measurements
Nadav Cohen, Itzik Klein
Autonomous underwater vehicles (AUVs) are employed for marine applications and can operate in deep underwater environments beyond human reach. A standard solution for the autonomou…
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets
Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes +2
Overparameterization in deep learning typically refers to settings where a trained neural network (NN) has representational capacity to fit the training data in many ways, some of…
Deep Linear Networks for Matrix Completion -- An Infinite Depth Limit
Nadav Cohen, Govind Menon, Zsolt Veraszto
The deep linear network (DLN) is a model for implicit regularization in gradient based optimization of overparametrized learning architectures. Training the DLN corresponds to a Ri…
BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation
Nadav Cohen, Itzik Klein
Autonomous underwater vehicles (AUV) perform various applications such as seafloor mapping and underwater structure health monitoring. Commonly, an inertial navigation system aided…
On the Implicit Bias of Gradient Descent for Temporal Extrapolation
Edo Cohen-Karlik, Avichai Ben David, Nadav Cohen +1
When using recurrent neural networks (RNNs) it is common practice to apply trained models to sequences longer than those seen in training. This "extrapolating" usage deviates from…
Semantic Segmentation in Art Paintings
Nadav Cohen, Yael Newman, Ariel Shamir
Semantic segmentation is a difficult task even when trained in a supervised manner on photographs. In this paper, we tackle the problem of semantic segmentation of artistic paintin…
Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
Noam Razin, Asaf Maman, Nadav Cohen
In the pursuit of explaining implicit regularization in deep learning, prominent focus was given to matrix and tensor factorizations, which correspond to simplified neural networks…
Continuous vs. Discrete Optimization of Deep Neural Networks
Omer Elkabetz, Nadav Cohen
Existing analyses of optimization in deep learning are either continuous, focusing on (variants of) gradient flow, or discrete, directly treating (variants of) gradient descent. Gr…
Implicit Regularization in Tensor Factorization
Noam Razin, Asaf Maman, Nadav Cohen
Recent efforts to unravel the mystery of implicit regularization in deep learning have led to a theoretical focus on matrix factorization -- matrix completion via linear neural net…
Implicit Regularization in Deep Learning May Not Be Explainable by Norms
Noam Razin, Nadav Cohen
Mathematically characterizing the implicit regularization induced by gradient-based optimization is a longstanding pursuit in the theory of deep learning. A widespread hope is that…
Implicit Regularization in Deep Matrix Factorization
Sanjeev Arora, Nadav Cohen, Wei Hu +1
Efforts to understand the generalization mystery in deep learning have led to the belief that gradient-based optimization induces a form of implicit regularization, a bias towards…
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
Sanjeev Arora, Nadav Cohen, Noah Golowich +1
We analyze speed of convergence to global optimum for gradient descent training a deep linear neural network (parameterized as ) by minimizing t…
Quantum Entanglement in Deep Learning Architectures
Yoav Levine, Or Sharir, Nadav Cohen +1
Modern deep learning has enabled unprecedented achievements in various domains. Nonetheless, employment of machine learning for wave function representations is focused on more tra…
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Sanjeev Arora, Nadav Cohen, Elad Hazan
Conventional wisdom in deep learning states that increasing depth improves expressiveness but complicates optimization. This paper suggests that, sometimes, increasing depth can sp…
"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…