Neural Architecture Search

We have done extensive work on the search, design, and training of Neural Architectures.


Relevant papers

  • N. Nayman, Y. Aflalo, A. Noy, L. Zelnik-Manor, BINAS: Bilinear Interpretable Neural Architecture Search ACML’2022, (arxiv)
  • N. Nayman, Y. Aflalo, A. Noy, L. Zelnik-Manor, HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search, ICML’2021. (arxiv, github)
  • A. Noy, Y. Xu, Y. Aflalo, L. Zelnik-Manor, R. Jin, A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks, Arxiv. (arxiv)
  • A Noy, N Nayman, T Ridnik, N Zamir, S Doveh, I Friedman, R Giryes, L. Zelnik-Manor, ASAP: Architecture Search, Anneal and Prune, AISTATS’2020. (arxiv)
  • N Nayman, A Noy, T Ridnik, I Friedman, R Jin, L. Zelnik-Manor, Xnas: Neural architecture search with expert advice, NeurIPS’2019. (arxiv, github)