10 citations · 24 across the 9 of their papers we have counts for
9 papers
SAVA: Scalable Learning-Agnostic Data Valuation
Samuel Kessler, Tam Le, Vu Nguyen
Selecting data for training machine learning models is crucial since large, web-scraped, real datasets contain noisy artifacts that affect the quality and relevance of individual d…
Fisher Flow Matching for Generative Modeling over Discrete Data
Oscar Davis, Samuel Kessler, Mircea Petrache +3
Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological sequence design, and graph-structured mo…
Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies
Kieran Wood, Samuel Kessler, Stephen J. Roberts +1
Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions rapidly change, as was seen in the advent of the COVID-19 pandemic in 202…
On Sequential Bayesian Inference for Continual Learning
Samuel Kessler, Adam Cobb, Tim G. J. Rudner +2
Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revis…
The Effectiveness of World Models for Continual Reinforcement Learning
Samuel Kessler, Mateusz Ostaszewski, Michał Bortkiewicz +5
World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning - a situation when the…
Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition
Bethan Thomas, Samuel Kessler, Salah Karout
Self-supervised learning (SSL) is a powerful tool that allows learning of underlying representations from unlabeled data. Transformer based models such as wav2vec 2.0 and HuBERT ar…
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning
Samuel Kessler, Bethan Thomas, Salah Karout
We present a method for transferring pre-trained self-supervised (SSL) speech representations to multiple languages. There is an abundance of unannotated speech, so creating self-s…
Same State, Different Task: Continual Reinforcement Learning without Interference
Samuel Kessler, Jack Parker-Holder, Philip Ball +2
Continual Learning (CL) considers the problem of training an agent sequentially on a set of tasks while seeking to retain performance on all previous tasks. A key challenge in CL i…
Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning
Samuel Kessler, Vu Nguyen, Stefan Zohren +1
We place an Indian Buffet process (IBP) prior over the structure of a Bayesian Neural Network (BNN), thus allowing the complexity of the BNN to increase and decrease automatically.…