109 citations · 269 across the 10 of their papers we have counts for
10 papers
Long-tail Recognition via Compositional Knowledge Transfer
Sarah Parisot, Pedro M. Esperanca, Steven McDonagh +3
In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes' few-shot problem via training-free knowledge transfer. Our objective is to tr…
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…
Batch Group Normalization
Xiao-Yun Zhou, Jiacheng Sun, Nanyang Ye +6
Deep Convolutional Neural Networks (DCNNs) are hard and time-consuming to train. Normalization is one of the effective solutions. Among previous normalization methods, Batch Normal…
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…
MANAS: Multi-Agent Neural Architecture Search
Vasco Lopes, Fabio Maria Carlucci, Pedro M Esperança +6
The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a g…
Encrypted accelerated least squares regression
Pedro M. Esperança, Louis J. M. Aslett, Chris C. Holmes
Information that is stored in an encrypted format is, by definition, usually not amenable to statistical analysis or machine learning methods. In this paper we present detailed ana…
Encrypted statistical machine learning: new privacy preserving methods
Louis J. M. Aslett, Pedro M. Esperança, Chris C. Holmes
We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens u…
A review of homomorphic encryption and software tools for encrypted statistical machine learning
Louis J. M. Aslett, Pedro M. Esperança, Chris C. Holmes
Recent advances in cryptography promise to enable secure statistical computation on encrypted data, whereby a limited set of operations can be carried out without the need to first…