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pro vyhledávání: '"DROUIN, P."'
Akademický článek
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Autor:
De Chezelles, Thibault Le Sellier, Gasse, Maxime, Drouin, Alexandre, Caccia, Massimo, Boisvert, Léo, Thakkar, Megh, Marty, Tom, Assouel, Rim, Shayegan, Sahar Omidi, Jang, Lawrence Keunho, Lù, Xing Han, Yoran, Ori, Kong, Dehan, Xu, Frank F., Reddy, Siva, Cappart, Quentin, Neubig, Graham, Salakhutdinov, Ruslan, Chapados, Nicolas, Lacoste, Alexandre
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging automation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fra
Externí odkaz:
http://arxiv.org/abs/2412.05467
Autor:
Brouillard, Philippe, Squires, Chandler, Wahl, Jonas, Kording, Konrad P., Sachs, Karen, Drouin, Alexandre, Sridhar, Dhanya
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic
Externí odkaz:
http://arxiv.org/abs/2412.01953
Autor:
Williams, Andrew Robert, Ashok, Arjun, Marcotte, Étienne, Zantedeschi, Valentina, Subramanian, Jithendaraa, Riachi, Roland, Requeima, James, Lacoste, Alexandre, Rish, Irina, Chapados, Nicolas, Drouin, Alexandre
Forecasting is a critical task in decision making across various domains. While numerical data provides a foundation, it often lacks crucial context necessary for accurate predictions. Human forecasters frequently rely on additional information, such
Externí odkaz:
http://arxiv.org/abs/2410.18959
Reconstruction functions are pivotal in sample compression theory, a framework for deriving tight generalization bounds. From a small sample of the training set (the compression set) and an optional stream of information (the message), they recover a
Externí odkaz:
http://arxiv.org/abs/2410.13577
Autor:
Brouillard, Philippe, Lachapelle, Sébastien, Kaltenborn, Julia, Gurwicz, Yaniv, Sridhar, Dhanya, Drouin, Alexandre, Nowack, Peer, Runge, Jakob, Rolnick, David
Scientific research often seeks to understand the causal structure underlying high-level variables in a system. For example, climate scientists study how phenomena, such as El Ni\~no, affect other climate processes at remote locations across the glob
Externí odkaz:
http://arxiv.org/abs/2410.07013
Autor:
Yon, Victor, Galaup, Bastien, Rohrbacher, Claude, Rivard, Joffrey, Morel, Alexis, Leclerc, Dominic, Godfrin, Clement, Li, Ruoyu, Kubicek, Stefan, De Greve, Kristiaan, Dupont-Ferrier, Eva, Beilliard, Yann, Melko, Roger G., Drouin, Dominique
Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional n
Externí odkaz:
http://arxiv.org/abs/2409.20320
Autor:
Bodin, Arnaud, Drouin, Christian
We propose a mathematical walk around the gcd of the values $A(n)$ and $B(n)$ of two polynomials evaluated at an integer $n$. This is an opportunity to use a very powerful tool: the resultant.
Comment: The final version will appear in the Americ
Comment: The final version will appear in the Americ
Externí odkaz:
http://arxiv.org/abs/2409.01224
Autor:
Sahu, Gaurav, Puri, Abhay, Rodriguez, Juan, Abaskohi, Amirhossein, Chegini, Mohammad, Drouin, Alexandre, Taslakian, Perouz, Zantedeschi, Valentina, Lacoste, Alexandre, Vazquez, David, Chapados, Nicolas, Pal, Christopher, Mudumba, Sai Rajeswar, Laradji, Issam Hadj
Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representin
Externí odkaz:
http://arxiv.org/abs/2407.06423
Autor:
Boisvert, Léo, Thakkar, Megh, Gasse, Maxime, Caccia, Massimo, De Chezelles, Thibault Le Sellier, Cappart, Quentin, Chapados, Nicolas, Lacoste, Alexandre, Drouin, Alexandre
The ability of large language models (LLMs) to mimic human-like intelligence has led to a surge in LLM-based autonomous agents. Though recent LLMs seem capable of planning and reasoning given user instructions, their effectiveness in applying these c
Externí odkaz:
http://arxiv.org/abs/2407.05291