1 citations · 1 across the 4 of their papers we have counts for
4 papers
Gefen: Optimized Stochastic Optimizer
Nadav Benedek, Tomer Koren, Ohad Fried
AdamW is a default optimizer for deep learning, but its moment states add two parameter-sized buffers to training memory, increasing the cost of large-scale pretraining. We propose…
NIV: Neural Axis Variations for Variable Font Generation
Nadav Benedek, Ariel Shamir, Ohad Fried
Variable fonts enable continuous variation of glyph geometry along semantic design axes such as weight, width, slant, and optical size. However, constructing a variable font from a…
Impactful Bit-Flip Search on Full-precision Models
Nadav Benedek, Matan Levy, Mahmood Sharif
Neural networks have shown remarkable performance in various tasks, yet they remain susceptible to subtle changes in their input or model parameters. One particularly impactful vul…
PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation
Nadav Benedek, Lior Wolf
With the proliferation of large pre-trained language models (PLMs), fine-tuning all model parameters becomes increasingly inefficient, particularly when dealing with numerous downs…