activity
20192022
most citedMargin-Based Regularization and Selective Sampling in Deep Neural Networks

2 citations · 4 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2022

DDNeRF: Depth Distribution Neural Radiance Fields

David Dadon, Ohad Fried, Yacov Hel-Or

In recent years, the field of implicit neural representation has progressed significantly. Models such as neural radiance fields (NeRF), which uses relatively small neural networks…

cs.LG2021

Pairwise Margin Maximization for Deep Neural Networks

Berry Weinstein, Shai Fine, Yacov Hel-Or

The weight decay regularization term is widely used during training to constrain expressivity, avoid overfitting, and improve generalization. Historically, this concept was borrowe…

cs.LG20202 cited

Margin-Based Regularization and Selective Sampling in Deep Neural Networks

Berry Weinstein, Shai Fine, Yacov Hel-Or

We derive a new margin-based regularization formulation, termed multi-margin regularization (MMR), for deep neural networks (DNNs). The MMR is inspired by principles that were appl…

cs.LG2020

Autoencoder Image Interpolation by Shaping the Latent Space

Alon Oring, Zohar Yakhini, Yacov Hel-Or

Autoencoders represent an effective approach for computing the underlying factors characterizing datasets of different types. The latent representation of autoencoders have been st…

eess.IV2020

The Role of Redundant Bases and Shrinkage Functions in Image Denoising

Yacov Hel-Or, Gil Ben-Artzi

Wavelet denoising is a classical and effective approach for reducing noise in images and signals. Suggested in 1994, this approach is carried out by rectifying the coefficients of…

cs.LG2020

Proximity Preserving Binary Code using Signed Graph-Cut

Inbal Lav, Shai Avidan, Yoram Singer +1

We introduce a binary embedding framework, called Proximity Preserving Code (PPC), which learns similarity and dissimilarity between data points to create a compact and affinity-pr…