52 citations · 93 across the 12 of their papers we have counts for
5 papers · 1 filter
Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks
Devon Jarvis, Richard Klein, Benjamin Rosman +1
In spite of finite dimension ReLU neural networks being a consistent factor behind recent deep learning successes, a theory of feature learning in these models remains elusive. Cur…
On The Specialization of Neural Modules
Devon Jarvis, Richard Klein, Benjamin Rosman +1
A number of machine learning models have been proposed with the goal of achieving systematic generalization: the ability to reason about new situations by combining aspects of prev…
Overlooked Implications of the Reconstruction Loss for VAE Disentanglement
Nathan Michlo, Richard Klein, Steven James
Learning disentangled representations with variational autoencoders (VAEs) is often attributed to the regularisation component of the loss. In this work, we highlight the interacti…
Confident in the Crowd: Bayesian Inference to Improve Data Labelling in Crowdsourcing
Pierce Burke, Richard Klein
With the increased interest in machine learning and big data problems, the need for large amounts of labelled data has also grown. However, it is often infeasible to get experts to…
Quantisation and Pruning for Neural Network Compression and Regularisation
Kimessha Paupamah, Steven James, Richard Klein
Deep neural networks are typically too computationally expensive to run in real-time on consumer-grade hardware and low-powered devices. In this paper, we investigate reducing the…