Zobrazeno 1 - 10
of 2 782
pro vyhledávání: '"Jolicoeur, P."'
The Fr\'echet Video Distance (FVD) is a widely adopted metric for evaluating video generation distribution quality. However, its effectiveness relies on critical assumptions. Our analysis reveals three significant limitations: (1) the non-Gaussianity
Externí odkaz:
http://arxiv.org/abs/2410.05203
The fluctuations produced during cosmic inflation may exhibit non-Gaussian characteristics that are imprinted in the large-scale structure of the Universe. This non-Gaussian imprint is an ultra-large scale signal that can be detected using the power
Externí odkaz:
http://arxiv.org/abs/2409.19383
We discuss the magnetic ground state and properties of a frustrated two-dimensional classical Heisenberg model of interacting hexagonal clusters of spins. The energy of the ground states is found exactly for arbitrary values of $J_1$ (intra-cluster c
Externí odkaz:
http://arxiv.org/abs/2409.17793
Generating novel molecules is challenging, with most representations leading to generative models producing many invalid molecules. Spanning Tree-based Graph Generation (STGG) is a promising approach to ensure the generation of valid molecules, outpe
Externí odkaz:
http://arxiv.org/abs/2407.09357
Autor:
Jolicoeur, Sheean, Guedezounme, Sêcloka L., Maartens, Roy, Paul, Pritha, Clarkson, Chris, Camera, Stefano
Publikováno v:
JCAP08(2024)027
Galaxy surveys contain information on the largest scales via wide-angle and relativistic contributions. By combining two different galaxy populations, we can suppress the strong cosmic variance on ultra-large scales and thus enhance the detectability
Externí odkaz:
http://arxiv.org/abs/2406.06274
With recent advances in video prediction, controllable video generation has been attracting more attention. Generating high fidelity videos according to simple and flexible conditioning is of particular interest. To this end, we propose a controllabl
Externí odkaz:
http://arxiv.org/abs/2406.05630
LoGAH: Predicting 774-Million-Parameter Transformers using Graph HyperNetworks with 1/100 Parameters
A good initialization of deep learning models is essential since it can help them converge better and faster. However, pretraining large models is unaffordable for many researchers, which makes a desired prediction for initial parameters more necessa
Externí odkaz:
http://arxiv.org/abs/2405.16287
Autor:
Korablyov, Maksym, Liu, Cheng-Hao, Jain, Moksh, van der Sloot, Almer M., Jolicoeur, Eric, Ruediger, Edward, Nica, Andrei Cristian, Bengio, Emmanuel, Lapchevskyi, Kostiantyn, St-Cyr, Daniel, Schuetz, Doris Alexandra, Butoi, Victor Ion, Rector-Brooks, Jarrid, Blackburn, Simon, Feng, Leo, Nekoei, Hadi, Gottipati, SaiKrishna, Vijayan, Priyesh, Gupta, Prateek, Rampášek, Ladislav, Avancha, Sasikanth, Bacon, Pierre-Luc, Hamilton, William L., Paige, Brooks, Misra, Sanchit, Jastrzebski, Stanislaw Kamil, Kaul, Bharat, Precup, Doina, Hernández-Lobato, José Miguel, Segler, Marwin, Bronstein, Michael, Marinier, Anne, Tyers, Mike, Bengio, Yoshua
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active lear
Externí odkaz:
http://arxiv.org/abs/2405.01616
Publikováno v:
EPJC 84 (2024) 491
The fluctuations generated by Inflation are nearly Gaussian in the simplest models, but may be non-Gaussian in more complex models, potentially leading to signatures in the late Universe. In particular, local type primordial non-Gaussianity induces s
Externí odkaz:
http://arxiv.org/abs/2312.12994
Publikováno v:
EPJC 84 (2024) 95
In the pursuit of understanding the large-scale structure of the Universe, the synergy between complementary cosmological surveys has proven to be a powerful tool. Using multiple tracers of the large-scale structure can significantly improve the cons
Externí odkaz:
http://arxiv.org/abs/2310.17959