Zobrazeno 1 - 10
of 823
pro vyhledávání: '"SCHMIDT, VICTOR"'
Autor:
Ramlaoui, Ali, Saulus, Théo, Terver, Basile, Schmidt, Victor, Rolnick, David, Malliaros, Fragkiskos D., Duval, Alexandre
Rapid advancements in machine learning (ML) are transforming materials science by significantly speeding up material property calculations. However, the proliferation of ML approaches has made it challenging for scientists to keep up with the most pr
Externí odkaz:
http://arxiv.org/abs/2407.08313
Autor:
Duval, Alexandre, Mathis, Simon V., Joshi, Chaitanya K., Schmidt, Victor, Miret, Santiago, Malliaros, Fragkiskos D., Cohen, Taco, Liò, Pietro, Bengio, Yoshua, Bronstein, Michael
Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space. In these graphs, the geometric attributes transform acco
Externí odkaz:
http://arxiv.org/abs/2312.07511
Autor:
Carbonero, Alvaro, Duval, Alexandre, Schmidt, Victor, Miret, Santiago, Hernandez-Garcia, Alex, Bengio, Yoshua, Rolnick, David
The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all atoms. However, in practice not all this information may be readily ava
Externí odkaz:
http://arxiv.org/abs/2310.06682
Autor:
AI4Science, Mila, Hernandez-Garcia, Alex, Duval, Alexandre, Volokhova, Alexandra, Bengio, Yoshua, Sharma, Divya, Carrier, Pierre Luc, Benabed, Yasmine, Koziarski, Michał, Schmidt, Victor
Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance,
Externí odkaz:
http://arxiv.org/abs/2310.04925
The growing popularity of generative flow networks (GFlowNets or GFNs) from a range of researchers with diverse backgrounds and areas of expertise necessitates a library which facilitates the testing of new features such as training losses that can b
Externí odkaz:
http://arxiv.org/abs/2305.14594
Autor:
Duval, Alexandre, Schmidt, Victor, Garcia, Alex Hernandez, Miret, Santiago, Malliaros, Fragkiskos D., Bengio, Yoshua, Rolnick, David
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetri
Externí odkaz:
http://arxiv.org/abs/2305.05577
Autor:
Duval, Alexandre, Schmidt, Victor, Miret, Santiago, Bengio, Yoshua, Hernández-García, Alex, Rolnick, David
Mitigating the climate crisis requires a rapid transition towards lower-carbon energy. Catalyst materials play a crucial role in the electrochemical reactions involved in numerous industrial processes key to this transition, such as renewable energy
Externí odkaz:
http://arxiv.org/abs/2211.12020
Autor:
Schmidt, Victor, Luccioni, Alexandra Sasha, Teng, Mélisande, Zhang, Tianyu, Reynaud, Alexia, Raghupathi, Sunand, Cosne, Gautier, Juraver, Adrien, Vardanyan, Vahe, Hernandez-Garcia, Alex, Bengio, Yoshua
Publikováno v:
ICLR 2022
Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understanding the effects of climate chang
Externí odkaz:
http://arxiv.org/abs/2110.02871
Autor:
Gupta, Prateek, Maharaj, Tegan, Weiss, Martin, Rahaman, Nasim, Alsdurf, Hannah, Sharma, Abhinav, Minoyan, Nanor, Harnois-Leblanc, Soren, Schmidt, Victor, Charles, Pierre-Luc St., Deleu, Tristan, Williams, Andrew, Patel, Akshay, Qu, Meng, Bilaniuk, Olexa, Caron, Gaétan Marceau, Carrier, Pierre Luc, Ortiz-Gagné, Satya, Rousseau, Marc-Andre, Buckeridge, David, Ghosn, Joumana, Zhang, Yang, Schölkopf, Bernhard, Tang, Jian, Rish, Irina, Pal, Christopher, Merckx, Joanna, Muller, Eilif B., Bengio, Yoshua
The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make inform
Externí odkaz:
http://arxiv.org/abs/2010.16004
Autor:
Bengio, Yoshua, Gupta, Prateek, Maharaj, Tegan, Rahaman, Nasim, Weiss, Martin, Deleu, Tristan, Muller, Eilif, Qu, Meng, Schmidt, Victor, St-Charles, Pierre-Luc, Alsdurf, Hannah, Bilanuik, Olexa, Buckeridge, David, Caron, Gáetan Marceau, Carrier, Pierre-Luc, Ghosn, Joumana, Ortiz-Gagne, Satya, Pal, Chris, Rish, Irina, Schölkopf, Bernhard, Sharma, Abhinav, Tang, Jian, Williams, Andrew
The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution t
Externí odkaz:
http://arxiv.org/abs/2010.12536