no code implementations • 11 Oct 2021 • Jesse Michael Han, Igor Babuschkin, Harrison Edwards, Arvind Neelakantan, Tao Xu, Stanislas Polu, Alex Ray, Pranav Shyam, Aditya Ramesh, Alec Radford, Ilya Sutskever
We show how to derive state-of-the-art unsupervised neural machine translation systems from generatively pre-trained language models.
no code implementations • 8 Mar 2021 • Christopher Bendkowski, Laurent Mennillo, Tao Xu, Mohamed Elsayed, Filip Stojic, Harrison Edwards, Shuailong Zhang, Cindi Morshead, Vijay Pawar, Aaron R. Wheeler, Danail Stoyanov, Michael Shaw
Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the use of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate.
21 code implementations • ICLR 2019 • Yuri Burda, Harrison Edwards, Amos Storkey, Oleg Klimov
In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods.
9 code implementations • ICLR 2019 • Antreas Antoniou, Harrison Edwards, Amos Storkey
The field of few-shot learning has recently seen substantial advancements.
no code implementations • 26 Jul 2018 • Joshua Achiam, Harrison Edwards, Dario Amodei, Pieter Abbeel
We explore methods for option discovery based on variational inference and make two algorithmic contributions.
no code implementations • ICML 2018 • Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards
Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems.
no code implementations • ICLR 2018 • Conor Durkan, Amos Storkey, Harrison Edwards
Such reasoning requires learning disentangled representations of data which are interpretable in isolation, but can also be combined in a new, unseen scenario.
7 code implementations • ICLR 2018 • Antreas Antoniou, Amos Storkey, Harrison Edwards
The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items.
5 code implementations • 7 Jun 2016 • Harrison Edwards, Amos Storkey
We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision.
1 code implementation • 18 Nov 2015 • Harrison Edwards, Amos Storkey
The flexibility of this method is shown via a novel problem: removing annotations from images, from unaligned training examples of annotated and unannotated images, and with no a priori knowledge of the form of annotation provided to the model.