Zobrazeno 1 - 10
of 8 932
pro vyhledávání: '"A Larochelle"'
The use of deep neural networks in reinforcement learning (RL) often suffers from performance degradation as model size increases. While soft mixtures of experts (SoftMoEs) have recently shown promise in mitigating this issue for online RL, the reaso
Externí odkaz:
http://arxiv.org/abs/2410.01930
Autor:
Agarwal, Rishabh, Singh, Avi, Zhang, Lei M., Bohnet, Bernd, Rosias, Luis, Chan, Stephanie, Zhang, Biao, Anand, Ankesh, Abbas, Zaheer, Nova, Azade, Co-Reyes, John D., Chu, Eric, Behbahani, Feryal, Faust, Aleksandra, Larochelle, Hugo
Large language models (LLMs) excel at few-shot in-context learning (ICL) -- learning from a few examples provided in context at inference, without any weight updates. Newly expanded context windows allow us to investigate ICL with hundreds or thousan
Externí odkaz:
http://arxiv.org/abs/2404.11018
Autor:
Larochelle, Dimitra Laurence
Publikováno v:
Diplomatie, 2024 May 01(127), 84-85.
Externí odkaz:
https://www.jstor.org/stable/48793814
Autor:
Shah, Vedant, Träuble, Frederik, Malik, Ashish, Larochelle, Hugo, Mozer, Michael, Arora, Sanjeev, Bengio, Yoshua, Goyal, Anirudh
Machine \emph{unlearning}, which involves erasing knowledge about a \emph{forget set} from a trained model, can prove to be costly and infeasible by existing techniques. We propose a nearly compute-free zero-shot unlearning technique based on a discr
Externí odkaz:
http://arxiv.org/abs/2311.15268
Learning from human feedback (LHF) -- and in particular learning from pairwise preferences -- has recently become a crucial ingredient in training large language models (LLMs), and has been the subject of much research. Most recent works frame it as
Externí odkaz:
http://arxiv.org/abs/2311.14115
Autor:
Teng, Mélisande, Elmustafa, Amna, Akera, Benjamin, Bengio, Yoshua, Abdelwahed, Hager Radi, Larochelle, Hugo, Rolnick, David
Biodiversity is declining at an unprecedented rate, impacting ecosystem services necessary to ensure food, water, and human health and well-being. Understanding the distribution of species and their habitats is crucial for conservation policy plannin
Externí odkaz:
http://arxiv.org/abs/2311.00936
Publikováno v:
Tackling Climate Change with Machine Learning Workshop, 11th International Conference on Learning Representations (ICLR 2023), Kigali, Rwanda
Climate change is a major driver of biodiversity loss, changing the geographic range and abundance of many species. However, there remain significant knowledge gaps about the distribution of species, due principally to the amount of effort and expert
Externí odkaz:
http://arxiv.org/abs/2305.01079
Autor:
Julien F. Paul, Célina Ducroux, Pamela Correia, Audrey Daigneault, Catherine Larochelle, Christian Stapf, Laura C. Gioia
Publikováno v:
Frontiers in Neurology, Vol 15 (2024)
BackgroundInterest is emerging regarding the role of blood biomarkers in acute stroke. The aim of this pilot study was to determine the feasibility of biomarker acquisition in suspected acute stroke, using modern ultrasensitive immunoassay techniques
Externí odkaz:
https://doaj.org/article/6d06a8c9445c4049a7bd6a57a8a29465
Publikováno v:
Engineering Applications of Computational Fluid Mechanics, Vol 18, Iss 1 (2024)
In this paper, the indoor environment of a small-scale high-density controlled environment agriculture (CEA-HD) space was simulated using computational fluid dynamics. Spatial modelling of the indoor environment considering the influential phenomena
Externí odkaz:
https://doaj.org/article/3011cbc381ec4cc7909c488a31e58e37
Autor:
Zhou, Hattie, Nova, Azade, Larochelle, Hugo, Courville, Aaron, Neyshabur, Behnam, Sedghi, Hanie
Large language models (LLMs) have shown increasing in-context learning capabilities through scaling up model and data size. Despite this progress, LLMs are still unable to solve algorithmic reasoning problems. While providing a rationale with the fin
Externí odkaz:
http://arxiv.org/abs/2211.09066