Zobrazeno 1 - 10
of 33 030
pro vyhledávání: '"A Lavoie"'
Autor:
Besrour, Marwan, Lavoie, Jacob, Omrani, Takwa, Martin-Hardy, Gabriel, Koleibi, Esmaeil Ranjbar, Menard, Jeremy, Koua, Konin, Marcoux, Philippe, Boukadoum, Mounir, Fontaine, Rejean
The computational complexity of deep learning algorithms has given rise to significant speed and memory challenges for the execution hardware. In energy-limited portable devices, highly efficient processing platforms are indispensable for reproducing
Externí odkaz:
http://arxiv.org/abs/2408.07734
Autor:
Bordes, Florian, Pang, Richard Yuanzhe, Ajay, Anurag, Li, Alexander C., Bardes, Adrien, Petryk, Suzanne, Mañas, Oscar, Lin, Zhiqiu, Mahmoud, Anas, Jayaraman, Bargav, Ibrahim, Mark, Hall, Melissa, Xiong, Yunyang, Lebensold, Jonathan, Ross, Candace, Jayakumar, Srihari, Guo, Chuan, Bouchacourt, Diane, Al-Tahan, Haider, Padthe, Karthik, Sharma, Vasu, Xu, Hu, Tan, Xiaoqing Ellen, Richards, Megan, Lavoie, Samuel, Astolfi, Pietro, Hemmat, Reyhane Askari, Chen, Jun, Tirumala, Kushal, Assouel, Rim, Moayeri, Mazda, Talattof, Arjang, Chaudhuri, Kamalika, Liu, Zechun, Chen, Xilun, Garrido, Quentin, Ullrich, Karen, Agrawal, Aishwarya, Saenko, Kate, Celikyilmaz, Asli, Chandra, Vikas
Following the recent popularity of Large Language Models (LLMs), several attempts have been made to extend them to the visual domain. From having a visual assistant that could guide us through unfamiliar environments to generative models that produce
Externí odkaz:
http://arxiv.org/abs/2405.17247
Autor:
Baum, Sebastian, Huber, Patrick, Stengel, Patrick, Abe, Natsue, Ang, Daniel G., Apollonio, Lorenzo, Araujo, Gabriela R., Balogh, Levente, Boukhtouchen, Pranshu Bhaumik Yilda, Bramante, Joseph, Caccianiga, Lorenzo, Calabrese-Day, Andrew, Chang, Qing, Collar, Juan I., Ebadi, Reza, Elykov, Alexey, Freese, Katherine, Fung, Audrey, Galelli, Claudio, Gleason, Arianna E., Perez, Mariano Guerrero, Hakenmüller, Janina, Hanyu, Takeshi, Hasebe, Noriko, Hirose, Shigenobu, Horiuchi, Shunsaku, Hoshino, Yasushi, Ido, Yuki, Ivanov, Vsevolod, Kamiyama, Takashi, Kato, Takenori, Kawamura, Yoji, Kelso, Chris, Khodaparast, Giti A., LaVoie-Ingram, Emilie M., Leybourne, Matthew, Liu, Xingxin, Lucas, Thalles, Mariani, Brenden A. Magill Federico M., Mkhonto, Sharlotte, Mumm, Hans Pieter, Murase, Kohta, Naka, Tatsuhiro, Oguni, Kenji, Ream, Kathryn, Scholberg, Kate, Shen, Maximilian, Spitz, Joshua, Suzuki, Katsuhiko, Takla, Alexander, Tang, Jiashen, Tapia-Arellano, Natalia, Vermeesch, Pieter, Vincent, Aaron C., Vladimirov, Nikita, Walsworth, Ronald, Waters, David, Wurtz, Greg, Yamasaki, Seiko, Zhang, Xianyi
The second "Mineral Detection of Neutrinos and Dark Matter" (MDvDM'24) meeting was held January 8-11, 2024 in Arlington, VA, USA, hosted by Virginia Tech's Center for Neutrino Physics. This document collects contributions from this workshop, providin
Externí odkaz:
http://arxiv.org/abs/2405.01626
Autor:
Lavoie, Samuel, Kirichenko, Polina, Ibrahim, Mark, Assran, Mahmoud, Wilson, Andrew Gordon, Courville, Aaron, Ballas, Nicolas
There are a thousand ways to caption an image. Contrastive Language Pretraining (CLIP) on the other hand, works by mapping an image and its caption to a single vector -- limiting how well CLIP-like models can represent the diverse ways to describe an
Externí odkaz:
http://arxiv.org/abs/2405.00740
Selective attention helps us focus on task-relevant aspects in the constant flood of our sensory input. This constraint in our perception allows us to robustly generalize under distractions and to new compositions of perceivable concepts. Transformer
Externí odkaz:
http://arxiv.org/abs/2404.15721
Out-of-distribution (OOD) detection is a critical task for safe deployment of learning systems in the open world setting. In this work, we investigate the use of feature density estimation via normalizing flows for OOD detection and present a fully u
Externí odkaz:
http://arxiv.org/abs/2402.06537
Autor:
Bouchard, Catherine, Deschênes, Andréanne, Boulanger, Vincent, Bellavance, Jean-Michel, Lavoie-Cardinal, Flavie, Gagné, Christian
The development of robust signal unmixing algorithms is essential for leveraging multimodal datasets acquired through a wide array of scientific imaging technologies, including hyperspectral or time-resolved acquisitions. In experimental physics, enh
Externí odkaz:
http://arxiv.org/abs/2312.05357
Finetuning language models with reinforcement learning (RL), e.g. from human feedback (HF), is a prominent method for alignment. But optimizing against a reward model can improve on reward while degrading performance in other areas, a phenomenon know
Externí odkaz:
http://arxiv.org/abs/2312.07551
Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of research in cognitive science has hypothesized a process, ``iterated learnin
Externí odkaz:
http://arxiv.org/abs/2310.18777
Autor:
Guillaume Fontaine, Billy Vinette, Charlene Weight, Marc-André Maheu-Cadotte, Andréane Lavallée, Marie-France Deschênes, Alexandra Lapierre, Sonia A. Castiglione, Gabrielle Chicoine, Geneviève Rouleau, Nikolas Argiropoulos, Kristin Konnyu, Meagan Mooney, Christine E. Cassidy, Tanya Mailhot, Patrick Lavoie, Catherine Pépin, Sylvie Cossette, Marie-Pierre Gagnon, Sonia Semenic, Nicola Straiton, Sandy Middleton
Publikováno v:
Implementation Science, Vol 19, Iss 1, Pp 1-31 (2024)
Abstract Background Implementation strategies targeting individual healthcare professionals and teams, such as audit and feedback, educational meetings, opinion leaders, and reminders, have demonstrated potential in promoting evidence-based nursing p
Externí odkaz:
https://doaj.org/article/7bb44898259340bfa60b4281e2f1c049