Search Results for author: Harrison Edwards

Found 10 papers, 5 papers with code

Autonomous object harvesting using synchronized optoelectronic microrobots

no code implementations8 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.

Cultural Vocal Bursts Intensity Prediction Object

Exploration by Random Network Distillation

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.

Montezuma's Revenge reinforcement-learning +2

Variational Option Discovery Algorithms

no code implementations26 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.

Variational Inference

The Context-Aware Learner

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.


Data Augmentation Generative Adversarial Networks

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.

Data Augmentation Few-Shot Learning +1

Towards a Neural Statistician

5 code implementations7 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.

Clustering Few-Shot Image Classification

Censoring Representations with an Adversary

1 code implementation18 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.


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