64 citations · 306 across the 23 of their papers we have counts for
5 papers · 1 filter
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…
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks
Asaf Noy, Yi Xu, Yonathan Aflalo +2
Deep neural networks' remarkable ability to correctly fit training data when optimized by gradient-based algorithms is yet to be fully understood. Recent theoretical results explai…
Knapsack Pruning with Inner Distillation
Yonathan Aflalo, Asaf Noy, Ming Lin +2
Neural network pruning reduces the computational cost of an over-parameterized network to improve its efficiency. Popular methods vary from -norm sparsification to Neural A…
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…