64 citations · 109 across the 10 of their papers we have counts for
10 papers
From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs
Omer Nahum, Niv Nayman, Jonathan Fhima +4
A key challenge in reliable LLM deployment is recognizing when uncertainty reflects irreducible variability in the task rather than limitations in the model's knowledge. In languag…
DODO: Discrete OCR Diffusion Models
Sean Man, Gilad Deutch, Roy Ganz +4
Optical Character Recognition (OCR) is a fundamental task for digitizing information, serving as a critical bridge between visual data and textual understanding. While modern Visio…
FreeAugment: Data Augmentation Search Across All Degrees of Freedom
Tom Bekor, Niv Nayman, Lihi Zelnik-Manor
Data augmentation has become an integral part of deep learning, as it is known to improve the generalization capabilities of neural networks. Since the most effective set of image…
VisFocus: Prompt-Guided Vision Encoders for OCR-Free Dense Document Understanding
Ofir Abramovich, Niv Nayman, Sharon Fogel +7
In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language mo…
Diverse Imagenet Models Transfer Better
Niv Nayman, Avram Golbert, Asaf Noy +2
A commonly accepted hypothesis is that models with higher accuracy on Imagenet perform better on other downstream tasks, leading to much research dedicated to optimizing Imagenet a…
BINAS: Bilinear Interpretable Neural Architecture Search
Niv Nayman, Yonathan Aflalo, Asaf Noy +2
Practical use of neural networks often involves requirements on latency, energy and memory among others. A popular approach to find networks under such requirements is through cons…
HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search
Niv Nayman, Yonathan Aflalo, Asaf Noy +1
Realistic use of neural networks often requires adhering to multiple constraints on latency, energy and memory among others. A popular approach to find fitting networks is through…
CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage Kernels
Jian Tan, Niv Nayman, Mengchang Wang
Bayesian optimization is a popular method for optimizing expensive black-box functions. Yet it oftentimes struggles in high dimensions where the computation could be prohibitively…
XNAS: Neural Architecture Search with Expert Advice
Niv Nayman, Asaf Noy, Tal Ridnik +3
This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice. Its optimization criterion is w…
ASAP: Architecture Search, Anneal and Prune
Asaf Noy, Niv Nayman, Tal Ridnik +5
Automatic methods for Neural Architecture Search (NAS) have been shown to produce state-of-the-art network models. Yet, their main drawback is the computational complexity of the s…