8 citations · 19 across the 4 of their papers we have counts for
6 papers
How Convolutional Neural Network Architecture Biases Learned Opponency and Colour Tuning
Ethan Harris, Daniela Mihai, Jonathon Hare
Recent work suggests that changing Convolutional Neural Network (CNN) architecture by introducing a bottleneck in the second layer can yield changes in learned function. To underst…
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…
Spatial and Colour Opponency in Anatomically Constrained Deep Networks
Ethan Harris, Daniela Mihai, Jonathon Hare
Colour vision has long fascinated scientists, who have sought to understand both the physiology of the mechanics of colour vision and the psychophysics of colour perception. We con…
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…