Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Gendron, Gaël"'
Autor:
Rogers, Mitchell, Knowles, Kobe, Gendron, Gaël, Heidari, Shahrokh, Valdez, David Arturo Soriano, Azhar, Mihailo, O'Leary, Padriac, Eyre, Simon, Witbrock, Michael, Delmas, Patrice
Deep learning approaches for animal re-identification have had a major impact on conservation, significantly reducing the time required for many downstream tasks, such as well-being monitoring. We propose a method called Recurrence over Video Frames
Externí odkaz:
http://arxiv.org/abs/2406.13002
Despite impressive performance on language modelling and complex reasoning tasks, Large Language Models (LLMs) fall short on the same tasks in uncommon settings or with distribution shifts, exhibiting a lack of generalisation ability. By contrast, sy
Externí odkaz:
http://arxiv.org/abs/2402.02636
Autor:
Gendron, Gaël, Chen, Yang, Rogers, Mitchell, Liu, Yiping, Azhar, Mihailo, Heidari, Shahrokh, Valdez, David Arturo Soriano, Knowles, Kobe, O'Leary, Padriac, Eyre, Simon, Witbrock, Michael, Dobbie, Gillian, Liu, Jiamou, Delmas, Patrice
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 essential to better understand the causes behind animal behaviour and wha
Externí odkaz:
http://arxiv.org/abs/2312.14333
Autor:
Bao, Qiming, Gendron, Gael, Peng, Alex Yuxuan, Zhong, Wanjun, Tan, Neset, Chen, Yang, Witbrock, Michael, Liu, Jiamou
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-like levels. However, their generalisation and robustness when performin
Externí odkaz:
http://arxiv.org/abs/2310.09430
Autor:
Bao, Qiming, Leinonen, Juho, Peng, Alex Yuxuan, Zhong, Wanjun, Gendron, Gaël, Pistotti, Timothy, Huang, Alice, Denny, Paul, Witbrock, Michael, Liu, Jiamou
Large language models exhibit superior capabilities in processing and understanding language, yet their applications in educational contexts remain underexplored. Learnersourcing enhances learning by engaging students in creating their own educationa
Externí odkaz:
http://arxiv.org/abs/2309.10444
Autor:
Rogers, Mitchell, Gendron, Gaël, Valdez, David Arturo Soriano, Azhar, Mihailo, Chen, Yang, Heidari, Shahrokh, Perelini, Caleb, O'Leary, Padriac, Knowles, Kobe, Tait, Izak, Eyre, Simon, Witbrock, Michael, Delmas, Patrice
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 behaviour within a zoo setting, such as scan sampling, require excessive human
Externí odkaz:
http://arxiv.org/abs/2306.11326
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, the mechanisms responsible for this success remain opaque, and it is
Externí odkaz:
http://arxiv.org/abs/2305.19555
Autor:
Bao, Qiming, Peng, Alex Yuxuan, Deng, Zhenyun, Zhong, Wanjun, Gendron, Gael, Pistotti, Timothy, Tan, Neset, Young, Nathan, Chen, Yang, Zhu, Yonghua, Denny, Paul, Witbrock, Michael, Liu, Jiamou
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges when gathering reliable data from the web to
Externí odkaz:
http://arxiv.org/abs/2305.12599
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 initial distribution. As causal engines aim to learn mechanisms indepe
Externí odkaz:
http://arxiv.org/abs/2302.00293
Publikováno v:
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. IJCAI 2023. Main Track. Pages 3239-3247
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 disentanglement, causal representation learning, and independent compone
Externí odkaz:
http://arxiv.org/abs/2302.00869