Binxin Ru | DeepAI

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∙ 06/08/2023

Bayesian Optimisation of Functions on Graphs

The increasing availability of graph-structured data motivates the task ...

14 Xingchen Wan, et al. ∙

∙ 03/15/2023

Bayesian Quadrature for Neural Ensemble Search

Ensembling can improve the performance of Neural Networks, but existing ...

0 Saad Hamid, et al. ∙

∙ 01/20/2023

Neural Architecture Search: Insights from 1000 Papers

In the past decade, advances in deep learning have resulted in breakthro...

0 Colin White, et al. ∙

∙ 11/03/2022

Towards Discovering Neural Architectures from Scratch

The discovery of neural architectures from scratch is the long-standing ...

0 Simon Schrodi, et al. ∙

∙ 07/19/2022

Bayesian Generational Population-Based Training

Reinforcement learning (RL) offers the potential for training generally ...

11 Xingchen Wan, et al. ∙

∙ 03/16/2022

On Redundancy and Diversity in Cell-based Neural Architecture Search

Searching for the architecture cells is a dominant paradigm in NAS. Howe...

2 Xingchen Wan, et al. ∙

∙ 01/12/2022

Learning to Identify Top Elo Ratings: A Dueling Bandits Approach

The Elo rating system is widely adopted to evaluate the skills of (chess...

5 Xue Yan, et al. ∙

∙ 12/24/2021

DARTS without a Validation Set: Optimizing the Marginal Likelihood

The success of neural architecture search (NAS) has historically been li...

0 Miroslav Fil, et al. ∙

∙ 11/08/2021

Approximate Neural Architecture Search via Operation Distribution Learning

The standard paradigm in Neural Architecture Search (NAS) is to search f...

0 Xingchen Wan, et al. ∙

∙ 11/05/2021

AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family

State-of-the-art results in deep learning have been improving steadily, ...

0 Roy Henha Eyono, et al. ∙

∙ 11/04/2021

Adversarial Attacks on Graph Classification via Bayesian Optimisation

Graph neural networks, a popular class of models effective in a wide ran...

14 Xingchen Wan, et al. ∙

∙ 09/13/2021

DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture

Automated machine learning (AutoML) usually involves several crucial com...

0 Kaichen Zhou, et al. ∙

∙ 02/14/2021

Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces

High-dimensional black-box optimisation remains an important yet notorio...

6 Xingchen Wan, et al. ∙

∙ 10/27/2020

A Bayesian Perspective on Training Speed and Model Selection

We take a Bayesian perspective to illustrate a connection between traini...

0 Clare Lyle, et al. ∙

∙ 06/13/2020

Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel

Bayesian optimisation (BO) has been widely used for hyperparameter optim...

0 Binxin Ru, et al. ∙

∙ 06/08/2020

Revisiting the Train Loss: an Efficient Performance Estimator for Neural Architecture Search

Reliable yet efficient evaluation of generalisation performance of a pro...

22 Binxin Ru, et al. ∙

∙ 04/03/2020

Neural Architecture Generator Optimization

Neural Architecture Search (NAS) was first proposed to achieve state-of-...

0 Binxin Ru, et al. ∙

∙ 06/20/2019

Bayesian Optimisation over Multiple Continuous and Categorical Inputs

Efficient optimisation of black-box problems that comprise both continuo...

5 Binxin Ru, et al. ∙

∙ 06/03/2019

MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning

Efficient approximation lies at the heart of large-scale machine learnin...

0 Diego Granziol, et al. ∙

∙ 01/29/2019

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

Batch Bayesian optimisation (BO) has been successfully applied to hyperp...

14 Ahsan S. Alvi, et al. ∙

∙ 04/18/2018

Entropic Spectral Learning in Large Scale Networks

We present a novel algorithm for learning the spectral density of large ...

0 Diego Granziol, et al. ∙

∙ 11/02/2017

Fast Information-theoretic Bayesian Optimisation

Information-theoretic Bayesian optimisation techniques have demonstrated...

0 Binxin Ru, et al. ∙

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