Euan Ong
I am a final-year undergraduate at the University of Cambridge, studying Computer Science.
My ambition is to develop powerful, yet safe and interpretable
abstract reasoners, whose
internal
state and behaviour remain transparent to the end
user.
To this end, I'm particularly interested in exploring how the mathematical toolkits we use to
understand and structure programs ‒ such as formal methods,
types and category theory ‒ can
inspire new ways
to both reverse-engineer existing neural networks, and build scalable neurosymbolic systems.
Email /
CV /
Google Scholar
/
Twitter /
LinkedIn /
Github
|
|
Research
So far, my research has broadly focused on studying the behaviour of neural networks in
vitro:
understanding both how they generalise when learning to perform abstract tasks, and
what
this tells us about the algorithms they've learned in order to do so.
I'm currently probing the foundations of neural
algorithmic
reasoning, exploring attacks on vision-language models, and poking language model
representations
with a stick.
|
Published work
|
|
Image Hijacks: Adversarial Images can Control Generative Models at
Runtime
Luke Bailey*, Euan Ong*, Stuart Russell, Scott Emmons (* denotes equal contribution)
Preprint, 2023
arXiv
/
project page + demo
/
tweeprint
We discovered that adversarial images can hijack the behaviour of vision-language models (VLMs) at
runtime. We developed a general method for crafting these image hijacks,
and trained image hijacks that force VLMs to output arbitrary text, leak their context window and
comply with harmful instructions.
|
|
Learnable Commutative Monoids for Graph Neural Networks
Euan Ong, Petar Veličković
Learning on Graphs, 2022
arXiv
/
reviews
/
project page
/
tweeprint
Using ideas from abstract algebra and functional programming, we built a new GNN aggregator that
beats the state of the art on complex aggregation problems (especially out-of-distribution) while
remaining efficient and parallelisable on large graphs.
|
Informal projects
|
|
Dissecting Deep Learning for Systematic Generalisation
Euan Ong, Etaash
Katiyar, Kai-En Chong, Albert
Qiaochu Jiang
Informal research, 2021
We investigated the capabilities of transformers to systematically generalise when learning to
recognise formal languages (such as
Parity and 2-Dyck), empirically corroborating various theoretical claims about transformer generalisation.
Inspired
by our observations, we
derived a parallel, stackless algorithm for recognising 2-Dyck that could (in principle) be
implemented by a transformer with a constant number of attention layers.
|
|
Object Detection in Thermal Imagery via Convolutional Neural
Networks
Euan Ong, Niki Trigoni, Pedro Porto Barque de Gusmão
Technical report, 2019
We trained a Faster R-CNN object detection network to identify landmarks (e.g. doors and windows) in
thermal images of indoor environments, with applications in the development of navigational aids for
search and rescue operations.
|
|