15 citations · 80 across the 20 of their papers we have counts for
4 papers · 1 filter
Loss Barcode: A Topological Measure of Escapability in Loss Landscapes
Serguei Barannikov, Daria Voronkova, Alexander Mironenko +4
Neural network training is commonly based on SGD. However, the understanding of SGD's ability to converge to good local minima, given the non-convex nature of loss functions and th…
Optimizing the Neural Architecture of Reinforcement Learning Agents
N. Mazyavkina, S. Moustafa, I. Trofimov +1
Reinforcement learning (RL) enjoyed significant progress over the last years. One of the most important steps forward was the wide application of neural networks. However, architec…
NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing
Nikita Klyuchnikov, Ilya Trofimov, Ekaterina Artemova +3
Neural Architecture Search (NAS) is a promising and rapidly evolving research area. Training a large number of neural networks requires an exceptional amount of computational power…
Multi-fidelity Neural Architecture Search with Knowledge Distillation
Ilya Trofimov, Nikita Klyuchnikov, Mikhail Salnikov +2
Neural architecture search (NAS) targets at finding the optimal architecture of a neural network for a problem or a family of problems. Evaluations of neural architectures are very…