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Aidan Gomez

PhD, started 2018

Aidan is a doctoral student of Yarin Gal and Yee Whye Teh at The University of Oxford. He leads a research group, called FOR.ai, focussing on providing resources, mentorship, and facilitating collaboration between academia and industry. Aidan’s research deals in understanding and improving neural networks and their applications. Previously, he worked with Geoffrey Hinton and Łukasz Kaiser on the Google Brain team. He obtained his Bachelors from The University of Toronto with supervision from Roger Grosse. He is an AI Fellow for Open Philanthropy and a Clarendon Scholar.

Publications

Wat heb je gezegd? Detecting Out-of-Distribution Translations with Variational Transformers

We use epistemic uncertainty to detect out-of-training-distribution sentences in Neural Machine Translation. For this, we develop a measure of uncertainty designed specifically for long sequences of discrete random variables, corresponding to the words in the output sentence. This measure is able to convey epistemic uncertainty akin to the Mutual Information (MI), which is used in the case of single discrete random variables such as in classification. Our new measure of uncertainty solves a major intractability in the naive application of existing approaches on long sentences. We train a Transformer model with dropout on the task of GermanEnglish translation using WMT 13 and Europarl, and show that using dropout uncertainty our measure is able to identify when Dutch source sentences, sentences which use the same word types as German, are given to the model instead of German.


Tim Xiao, Aidan Gomez, Yarin Gal
Spotlight talk, Workshop on Bayesian Deep Learning, NeurIPS 2019
[Paper]

Location Conditional Image Generation using Generative Adversarial Networks

Can an AI-artist instil the emotion of sense of place in its audience? Motivated by this thought, this paper presents our endeavours to make a GANs model learn the visual characteristics of locations to achieve creativity. The project’s novelty lies in addressing the problem of the hardness of GANs training for an extremely diverse dataset in a contextual setting. The project explores GANs as an impressionist artist who adds its perspective to the artwork without hampering photo realism.


Mayur Saxena, Aidan Gomez, Yarin Gal
Machine Learning for Creativity and Design NeurIPS 2019 Workshop
[Paper]

A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks

Evaluation of Bayesian deep learning (BDL) methods is challenging. We often seek to evaluate the methods’ robustness and scalability, assessing whether new tools give ‘better’ uncertainty estimates than old ones. These evaluations are paramount for practitioners when choosing BDL tools on-top of which they build their applications. Current popular evaluations of BDL methods, such as the UCI experiments, are lacking: Methods that excel with these experiments often fail when used in application such as medical or automotive, suggesting a pertinent need for new benchmarks in the field. We propose a new BDL benchmark with a diverse set of tasks, inspired by a real-world medical imaging application on diabetic retinopathy diagnosis. Visual inputs (512x512 RGB images of retinas) are considered, where model uncertainty is used for medical pre-screening—i.e. to refer patients to an expert when model diagnosis is uncertain. Methods are then ranked according to metrics derived from expert-domain to reflect real-world use of model uncertainty in automated diagnosis. We develop multiple tasks that fall under this application, including out-of-distribution detection and robustness to distribution shift. We then perform a systematic comparison of well-tuned BDL techniques on the various tasks. From our comparison we conclude that some current techniques which solve benchmarks such as UCI `overfit’ their uncertainty to the dataset—when evaluated on our benchmark these underperform in comparison to simpler baselines. The code for the benchmark, its baselines, and a simple API for evaluating new BDL tools are made available at https://github.com/oatml/bdl-benchmarks.


Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroon, Yarin Gal
Preprint, 2019
[Preprint] [BibTex] [Code]
arXiv, 2019
[arXiv]
Spotlight talk, Workshop on Bayesian Deep Learning, NeurIPS 2019
[Paper]

Targeted Dropout

Neural networks are extremely flexible models due to their large number of parameters, which is beneficial for learning, but also highly redundant. This makes it possible to compress neural networks without having a drastic effect on performance. We introduce targeted dropout, a strategy for post hoc pruning of neural network weights and units that builds the pruning mechanism directly into learning. At each weight update, targeted dropout selects a candidate set for pruning using a simple selection criterion, and then stochastically prunes the network via dropout applied to this set. The resulting network learns to be explicitly robust to pruning, comparing favourably to more complicated regularization schemes while at the same time being extremely simple to implement, and easy to tune.


Aidan Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton
Workshop on Compact Deep Neural Networks with industrial applications, NeurIPS 2018
[Paper] [BibTex]


Reproducibility and Code

Code for Bayesian Deep Learning Benchmarks

In order to make real-world difference with Bayesian Deep Learning (BDL) tools, the tools must scale to real-world settings. And for that we, the research community, must be able to evaluate our inference tools (and iterate quickly) with real-world benchmark tasks. We should be able to do this without necessarily worrying about application-specific domain knowledge, like the expertise often required in medical applications for example. We require benchmarks to test for inference robustness, performance, and accuracy, in addition to cost and effort of development. These benchmarks should be at a variety of scales, ranging from toy MNIST-scale benchmarks for fast development cycles, to large data benchmarks which are truthful to real-world applications, capturing their constraints.

Code
Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Yarin Gal


Blog Posts

25 OATML Conference and Workshop papers at NeurIPS 2019

We are glad to share the following 25 papers by OATML authors and collaborators to be presented at this NeurIPS conference and workshops. …

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Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Tom Rainforth, Panagiotis Tigas, Andreas Kirsch, Clare Lyle, Joost van Amersfoort, Yarin Gal,08 Dec 2019

Bayesian Deep Learning Benchmarks

In order to make real-world difference with Bayesian Deep Learning (BDL) tools, the tools must scale to real-world settings. And for that we, the research community, must be able to evaluate our inference tools (and iterate quickly) with real-world benchmark tasks. We should be able to do this without necessarily worrying about application-specific domain knowledge, like the expertise often required in medical applications for example. We require benchmarks to test for inference robustness, performance, and accuracy, in addition to cost and effort of development. These benchmarks should be at a variety of scales, ranging from toy MNIST-scale benchmarks for fast development cycles, to large data benchmarks which are truthful to real-world applications, capturing their constraints. …

Full post...


Angelos Filos, Sebastian Farquhar, Aidan Gomez, Tim G. J. Rudner, Zac Kenton, Lewis Smith, Milad Alizadeh, Yarin Gal,14 Jun 2019

Targeted Dropout

Neural networks can represent functions to solve complex tasks that are difficult — if not impossible — to write instructions for by hand, such as understanding language and recognizing objects. Conveniently, we’ve seen that task performance increases as we use larger networks. However, the increase in computational costs also increases dollars and time required to train and use models. Practitioners are plagued with networks that are too large to store in on-device memory, or too slow for real-world utility. …

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Aidan Gomez,05 Jun 2019

Contact

We are located at
Department of Computer Science, University of Oxford
Wolfson Building
Parks Road
OXFORD
OX1 3QD
UK
Twitter: @OATML_Oxford
Github: OATML
Email: oatml@cs.ox.ac.uk


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