9 citations · 24 across the 13 of their papers we have counts for
6 papers · 1 filter
Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networks
Gaël Gendron, Yang Chen, Mitchell Rogers +11
Better understanding the natural world is a crucial task with a wide range of applications. In environments with close proximity between humans and animals, such as zoos, it is ess…
Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning
Qiming Bao, Gael Gendron, Alex Yuxuan Peng +5
Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-li…
Meerkat Behaviour Recognition Dataset
Mitchell Rogers, Gaël Gendron, David Arturo Soriano Valdez +10
Recording animal behaviour is an important step in evaluating the well-being of animals and further understanding the natural world. Current methods for documenting animal behaviou…
Large Language Models Are Not Strong Abstract Reasoners
Gaël Gendron, Qiming Bao, Michael Witbrock +1
Large Language Models have shown tremendous performance on a large variety of natural language processing tasks, ranging from text comprehension to common sense reasoning. However,…
Disentanglement of Latent Representations via Causal Interventions
Gaël Gendron, Michael Witbrock, Gillian Dobbie
The process of generating data such as images is controlled by independent and unknown factors of variation. The retrieval of these variables has been studied extensively in the di…
A Survey of Methods, Challenges and Perspectives in Causality
Gaël Gendron, Michael Witbrock, Gillian Dobbie
Deep Learning models have shown success in a large variety of tasks by extracting correlation patterns from high-dimensional data but still struggle when generalizing out of their…