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20142024
most citedSuper-resolution on the Sphere using Convex Optimization

54 citations · 105 across the 17 of their papers we have counts for

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7 papers · 1 filter

cs.LG2024

Wavelets Are All You Need for Autoregressive Image Generation

Wael Mattar, Idan Levy, Nir Sharon +1

In this paper, we take a new approach to autoregressive image generation that is based on two main ingredients. The first is wavelet image coding, which allows to tokenize the visu…

cs.LG2023★ 11 cited

Reverse Engineering Self-Supervised Learning

Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti +2

Self-supervised learning (SSL) is a powerful tool in machine learning, but understanding the learned representations and their underlying mechanisms remains a challenge. This paper…

cs.LG2023

Numerical Methods For PDEs Over Manifolds Using Spectral Physics Informed Neural Networks

Yuval Zelig, Shai Dekel

We introduce an approach for solving PDEs over manifolds using physics informed neural networks whose architecture aligns with spectral methods. The networks are trained to take in…

cs.LG2023★ 1 cited

Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions

Ido Ben-Shaul, Tomer Galanti, Shai Dekel

Multiplication layers are a key component in various influential neural network modules, including self-attention and hypernetwork layers. In this paper, we investigate the approxi…

cs.LG2022★ 2 cited

Nearest Class-Center Simplification through Intermediate Layers

Ido Ben-Shaul, Shai Dekel

Recent advances in theoretical Deep Learning have introduced geometric properties that occur during training, past the Interpolation Threshold -- where the training error reaches z…

cs.LG2021

Sparsity-Probe: Analysis tool for Deep Learning Models

Ido Ben-Shaul, Shai Dekel

We propose a probe for the analysis of deep learning architectures that is based on machine learning and approximation theoretical principles. Given a deep learning architecture an…

cs.LG2018★ 2 cited

Wavelet Decomposition of Gradient Boosting

Shai Dekel, Oren Elisha, Ohad Morgan

In this paper we introduce a significant improvement to the popular tree-based Stochastic Gradient Boosting algorithm using a wavelet decomposition of the trees. This approach is b…