Zobrazeno 1 - 10
of 510
pro vyhledávání: '"Monteiro, João P"'
Autor:
Monteiro, Joao, Noel, Pierre-Andre, Marcotte, Etienne, Rajeswar, Sai, Zantedeschi, Valentina, Vazquez, David, Chapados, Nicolas, Pal, Christopher, Taslakian, Perouz
Large Language Models (LLMs) are trained on vast amounts of data, most of which is automatically scraped from the internet. This data includes encyclopedic documents that harbor a vast amount of general knowledge (e.g., Wikipedia) but also potentiall
Externí odkaz:
http://arxiv.org/abs/2406.11811
Autor:
Rosário Salema de Carvalho
Publikováno v:
Cadernos do Arquivo Municipal, Iss 17 (2022)
Externí odkaz:
https://doaj.org/article/434a5e1aea814f9d807d0cb55152dc4c
Autor:
Monteiro, João, Marcotte, Étienne, Noël, Pierre-André, Zantedeschi, Valentina, Vázquez, David, Chapados, Nicolas, Pal, Christopher, Taslakian, Perouz
In-context learning (ICL) approaches typically leverage prompting to condition decoder-only language model generation on reference information. Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations
Externí odkaz:
http://arxiv.org/abs/2404.15420
Empirical risk minimization (ERM) is sensitive to spurious correlations in the training data, which poses a significant risk when deploying systems trained under this paradigm in high-stake applications. While the existing literature focuses on maxim
Externí odkaz:
http://arxiv.org/abs/2310.18555
Autor:
José D'Encarnação
Publikováno v:
Biblos, Iss 2 (2018)
http://dx.doi.org/10.14195/0870-4112_3-2_10
Externí odkaz:
https://doaj.org/article/006cb15f5418467495c726ba36ebf644
Autor:
Guille-Escuret, Charles, Noël, Pierre-André, Mitliagkas, Ioannis, Vazquez, David, Monteiro, Joao
Improving the reliability of deployed machine learning systems often involves developing methods to detect out-of-distribution (OOD) inputs. However, existing research often narrowly focuses on samples from classes that are absent from the training s
Externí odkaz:
http://arxiv.org/abs/2308.11480
Autor:
Li, Raymond, Allal, Loubna Ben, Zi, Yangtian, Muennighoff, Niklas, Kocetkov, Denis, Mou, Chenghao, Marone, Marc, Akiki, Christopher, Li, Jia, Chim, Jenny, Liu, Qian, Zheltonozhskii, Evgenii, Zhuo, Terry Yue, Wang, Thomas, Dehaene, Olivier, Davaadorj, Mishig, Lamy-Poirier, Joel, Monteiro, João, Shliazhko, Oleh, Gontier, Nicolas, Meade, Nicholas, Zebaze, Armel, Yee, Ming-Ho, Umapathi, Logesh Kumar, Zhu, Jian, Lipkin, Benjamin, Oblokulov, Muhtasham, Wang, Zhiruo, Murthy, Rudra, Stillerman, Jason, Patel, Siva Sankalp, Abulkhanov, Dmitry, Zocca, Marco, Dey, Manan, Zhang, Zhihan, Fahmy, Nour, Bhattacharyya, Urvashi, Yu, Wenhao, Singh, Swayam, Luccioni, Sasha, Villegas, Paulo, Kunakov, Maxim, Zhdanov, Fedor, Romero, Manuel, Lee, Tony, Timor, Nadav, Ding, Jennifer, Schlesinger, Claire, Schoelkopf, Hailey, Ebert, Jan, Dao, Tri, Mishra, Mayank, Gu, Alex, Robinson, Jennifer, Anderson, Carolyn Jane, Dolan-Gavitt, Brendan, Contractor, Danish, Reddy, Siva, Fried, Daniel, Bahdanau, Dzmitry, Jernite, Yacine, Ferrandis, Carlos Muñoz, Hughes, Sean, Wolf, Thomas, Guha, Arjun, von Werra, Leandro, de Vries, Harm
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilitie
Externí odkaz:
http://arxiv.org/abs/2305.06161
Autor:
Neto, Pedro C., Montezuma, Diana, Oliveira, Sara P., Oliveira, Domingos, Fraga, João, Monteiro, Ana, Monteiro, João, Ribeiro, Liliana, Gonçalves, Sofia, Reinhard, Stefan, Zlobec, Inti, Pinto, Isabel M., Cardoso, Jaime S.
Publikováno v:
npj Precis. Onc. 8, 56 (2024)
Considering the profound transformation affecting pathology practice, we aimed to develop a scalable artificial intelligence (AI) system to diagnose colorectal cancer from whole-slide images (WSI). For this, we propose a deep learning (DL) system tha
Externí odkaz:
http://arxiv.org/abs/2301.02608
Autor:
Vítor Rodrigues
Publikováno v:
Cadernos do Arquivo Municipal, Iss 4 (2015)
Externí odkaz:
https://doaj.org/article/ddde7687c9634e03a8a4a4a8f2c0a7df
Publikováno v:
Advances in Neural Information Processing Systems 36 (2024)
Handling out-of-distribution (OOD) samples has become a major stake in the real-world deployment of machine learning systems. This work explores the use of self-supervised contrastive learning to the simultaneous detection of two types of OOD samples
Externí odkaz:
http://arxiv.org/abs/2210.01742