activity
20182022
most citedLearning Representations of Sets through Optimized Permutations

8 citations · 14 across the 7 of their papers we have counts for

collaborators

14 papers

cs.LG2022

Generalisation and the Risk--Entropy Curve

Dominic Belcher, Antonia Marcu, Adam Prügel-Bennett

In this paper we show that the expected generalisation performance of a learning machine is determined by the distribution of risks or equivalently its logarithm -- a quantity we t…

cs.LG2022

Orthogonalising gradients to speed up neural network optimisation

Mark Tuddenham, Adam Prügel-Bennett, Jonathan Hare

The optimisation of neural networks can be sped up by orthogonalising the gradients before the optimisation step, ensuring the diversification of the learned representations. We or…

cs.CV2021

Language Models as Zero-shot Visual Semantic Learners

Yue Jiao, Jonathon Hare, Adam Prügel-Bennett

Visual Semantic Embedding (VSE) models, which map images into a rich semantic embedding space, have been a milestone in object recognition and zero-shot learning. Current approache…

cs.CV2021

What Remains of Visual Semantic Embeddings

Yue Jiao, Jonathon Hare, Adam Prügel-Bennett

Zero shot learning (ZSL) has seen a surge in interest over the decade for its tight links with the mechanism making young children recognize novel objects. Although different parad…

cs.CV2021

Object detection for crabs in top-view seabed imagery

Vlad Velici, Adam Prügel-Bennett

This report presents the application of object detection on a database of underwater images of different species of crabs, as well as aerial images of sea lions and finally the Pas…

cs.LG2021

RotLSTM: Rotating Memories in Recurrent Neural Networks

Vlad Velici, Adam Prügel-Bennett

Long Short-Term Memory (LSTM) units have the ability to memorise and use long-term dependencies between inputs to generate predictions on time series data. We introduce the concept…