52 citations · 93 across the 12 of their papers we have counts for
12 papers
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…
DeepSet SimCLR: Self-supervised deep sets for improved pathology representation learning
David Torpey, Richard Klein
Often, applications of self-supervised learning to 3D medical data opt to use 3D variants of successful 2D network architectures. Although promising approaches, they are significan…
On the robustness of self-supervised representations for multi-view object classification
David Torpey, Richard Klein
It is known that representations from self-supervised pre-training can perform on par, and often better, on various downstream tasks than representations from fully-supervised pre-…
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…
Improving Pose Estimation through Contextual Activity Fusion
David Poulton, Richard Klein
This research presents the idea of activity fusion into existing Pose Estimation architectures to enhance their predictive ability. This is motivated by the rise in higher level co…
Generative Adversarial Networks for Non-Raytraced Global Illumination on Older GPU Hardware
Jared Harris-Dewey, Richard Klein
We give an overview of the different rendering methods and we demonstrate that the use of a Generative Adversarial Networks (GAN) for Global Illumination (GI) gives a superior qual…
Automated Parking Space Detection Using Convolutional Neural Networks
Julien Nyambal, Richard Klein
Finding a parking space nowadays becomes an issue that is not to be neglected, it consumes time and energy. We have used computer vision techniques to infer the state of the parkin…
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…
The Wits Intelligent Teaching System: Detecting Student Engagement During Lectures Using Convolutional Neural Networks
Richard Klein, Turgay Celik
To perform contingent teaching and be responsive to students' needs during class, lecturers must be able to quickly assess the state of their audience. While effective teachers are…
Explicit homography estimation improves contrastive self-supervised learning
David Torpey, Richard Klein
The typical contrastive self-supervised algorithm uses a similarity measure in latent space as the supervision signal by contrasting positive and negative images directly or indire…
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…