8 citations · 25 across the 8 of their papers we have counts for
6 papers · 1 filter
FMix: Enhancing Mixed Sample Data Augmentation
Ethan Harris, Antonia Marcu, Matthew Painter +3
Mixed Sample Data Augmentation (MSDA) has received increasing attention in recent years, with many successful variants such as MixUp and CutMix. By studying the mutual information…
Avoiding hashing and encouraging visual semantics in referential emergent language games
Daniela Mihai, Jonathon Hare
There has been an increasing interest in the area of emergent communication between agents which learn to play referential signalling games with realistic images. In this work, we…
Deep Set Prediction Networks
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for pre…
FSPool: Learning Set Representations with Featurewise Sort Pooling
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem. We introduce a pooling method for sets of feature vectors ba…
Learning Representations of Sets through Optimized Permutations
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Representations of sets are challenging to learn because operations on sets should be permutation-invariant. To this end, we propose a Permutation-Optimisation module that learns h…
Torchbearer: A Model Fitting Library for PyTorch
Ethan Harris, Matthew Painter, Jonathon Hare
We introduce torchbearer, a model fitting library for pytorch aimed at researchers working on deep learning or differentiable programming. The torchbearer library provides a high l…