Search Results for author: Niv Nayman

Found 6 papers, 5 papers with code

Diverse Imagenet Models Transfer Better

no code implementations19 Apr 2022 Niv Nayman, Avram Golbert, Asaf Noy, Tan Ping, Lihi Zelnik-Manor

Encouraged by the recent transferability results of self-supervised models, we propose a method that combines self-supervised and supervised pretraining to generate models with both high diversity and high accuracy, and as a result high transferability.

Multi-Label Classification

BINAS: Bilinear Interpretable Neural Architecture Search

1 code implementation24 Oct 2021 Niv Nayman, Yonathan Aflalo, Asaf Noy, Rong Jin, Lihi Zelnik-Manor

Practical use of neural networks often involves requirements on latency, energy and memory among others.

Neural Architecture Search

HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search

2 code implementations23 Feb 2021 Niv Nayman, Yonathan Aflalo, Asaf Noy, Lihi Zelnik-Manor

Realistic use of neural networks often requires adhering to multiple constraints on latency, energy and memory among others.

Neural Architecture Search

CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage Kernels

1 code implementation NeurIPS 2021 Jian Tan, Niv Nayman, Mengchang Wang

These virtual points, along with the means and variances of their unknown function values estimated using the simple kernel of the first stage, are fitted to a more sophisticated kernel model in the second stage.

Bayesian Optimization Computational Efficiency +1

XNAS: Neural Architecture Search with Expert Advice

2 code implementations NeurIPS 2019 Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik-Manor

This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice.

Image Classification Neural Architecture Search

ASAP: Architecture Search, Anneal and Prune

1 code implementation8 Apr 2019 Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, Lihi Zelnik-Manor

In this paper, we propose a differentiable search space that allows the annealing of architecture weights, while gradually pruning inferior operations.

Neural Architecture Search

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