109 citations · 165 across the 12 of their papers we have counts for
5 papers · 1 filter
Approximate Neural Architecture Search via Operation Distribution Learning
Xingchen Wan, Binxin Ru, Pedro M. Esperança +1
The standard paradigm in Neural Architecture Search (NAS) is to search for a fully deterministic architecture with specific operations and connections. In this work, we instead pro…
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family
Roy Henha Eyono, Fabio Maria Carlucci, Pedro M Esperança +2
State-of-the-art results in deep learning have been improving steadily, in good part due to the use of larger models. However, widespread use is constrained by device hardware limi…
OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression
Chen Zhang, Shifeng Zhang, Fabio Maria Carlucci +1
Explicit deep generative models (DGMs), e.g., VAEs and Normalizing Flows, have shown to offer an effective data modelling alternative for lossless compression. However, DGMs themse…
Neural Architecture Generator Optimization
Binxin Ru, Pedro Esperanca, Fabio Carlucci
Neural Architecture Search (NAS) was first proposed to achieve state-of-the-art performance through the discovery of new architecture patterns, without human intervention. An over-…
NAS evaluation is frustratingly hard
Antoine Yang, Pedro M. Esperança, Fabio M. Carlucci
Neural Architecture Search (NAS) is an exciting new field which promises to be as much as a game-changer as Convolutional Neural Networks were in 2012. Despite many great works lea…