21 citations · 63 across the 8 of their papers we have counts for
8 papers · 1 filter
Learning to Identify Top Elo Ratings: A Dueling Bandits Approach
Xue Yan, Yali Du, Binxin Ru +3
The Elo rating system is widely adopted to evaluate the skills of (chess) game and sports players. Recently it has been also integrated into machine learning algorithms in evaluati…
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…
How Powerful are Performance Predictors in Neural Architecture Search?
Colin White, Arber Zela, Binxin Ru +2
Early methods in the rapidly developing field of neural architecture search (NAS) required fully training thousands of neural networks. To reduce this extreme computational cost, d…
A Bayesian Perspective on Training Speed and Model Selection
Clare Lyle, Lisa Schut, Binxin Ru +2
We take a Bayesian perspective to illustrate a connection between training speed and the marginal likelihood in linear models. This provides two major insights: first, that a measu…
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels
Binxin Ru, Xingchen Wan, Xiaowen Dong +1
Current neural architecture search (NAS) strategies focus only on finding a single, good, architecture. They offer little insight into why a specific network is performing well, or…