11 citations · 18 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
On the Implicit Bias Towards Minimal Depth of Deep Neural Networks
Tomer Galanti, Liane Galanti, Ido Ben-Shaul
Recent results in the literature suggest that the penultimate (second-to-last) layer representations of neural networks that are trained for classification exhibit a clustering pro…
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…
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…
Solving the functional Eigen-Problem using Neural Networks
Ido Ben-Shaul, Leah Bar, Nir Sochen
In this work, we explore the ability of NN (Neural Networks) to serve as a tool for finding eigen-pairs of ordinary differential equations. The question we aime to address is wheth…