Zobrazeno 1 - 10
of 2 531
pro vyhledávání: '"P. Nica"'
Autor:
Nica, Bogdan
Publikováno v:
Linear Algebra and its Applications 681 (2024), 89-96
Consider a graph on the non-singular matrices over a finite field, in which two distinct non-singular matrices are joined by an edge whenever their sum is singular. We prove an upper bound for the independence number of this graph. As a consequence,
Externí odkaz:
http://arxiv.org/abs/2405.08734
Autor:
Nica, Bogdan
We obtain asymptotic estimates for the $\ell^p$-operator norm of spherical averaging operators associated to certain geometric group actions. The motivating example is the case of Gromov hyperbolic groups, for which we obtain asymptotically sharp est
Externí odkaz:
http://arxiv.org/abs/2405.08682
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
We introduce a novel approach for batch selection in Stochastic Gradient Descent (SGD) training, leveraging combinatorial bandit algorithms. Our methodology focuses on optimizing the learning process in the presence of label noise, a prevalent issue
Externí odkaz:
http://arxiv.org/abs/2311.00096
Autor:
Li, Mufan Bill, Nica, Mihai
Recent analyses of neural networks with shaped activations (i.e. the activation function is scaled as the network size grows) have led to scaling limits described by differential equations. However, these results do not a priori tell us anything abou
Externí odkaz:
http://arxiv.org/abs/2310.12079
Autor:
Bose, Avishek Joey, Akhound-Sadegh, Tara, Huguet, Guillaume, Fatras, Kilian, Rector-Brooks, Jarrid, Liu, Cheng-Hao, Nica, Andrei Cristian, Korablyov, Maksym, Bronstein, Michael, Tong, Alexander
The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce FoldFlow, a series of novel generative models of increasing modeling power based on the flow-matc
Externí odkaz:
http://arxiv.org/abs/2310.02391
Diffusion models learn to reverse the progressive noising of a data distribution to create a generative model. However, the desired continuous nature of the noising process can be at odds with discrete data. To deal with this tension between continuo
Externí odkaz:
http://arxiv.org/abs/2309.02530
Publikováno v:
Complexity, Vol 2018 (2018)
Carbon plasmas generated by excimer laser ablation are often applied for deposition (in vacuum or under controlled atmosphere) of high-technological interest nanostructures and thin films. For specific excimer irradiation conditions, these transient
Externí odkaz:
https://doaj.org/article/8e0e7efb98a54ade8d80bc63acb2c316
Autor:
Jakub, Cameron, Nica, Mihai
Neural networks are powerful functions with widespread use, but the theoretical behaviour of these functions is not fully understood. Creating deep neural networks by stacking many layers has achieved exceptional performance in many applications and
Externí odkaz:
http://arxiv.org/abs/2306.01513
A de Bruijn torus is the two dimensional generalization of a de Bruijn sequence. While some methods exist to generate these tori, only a few methods of construction are known. We present a novel method to generate de Bruijn tori with rectangular wind
Externí odkaz:
http://arxiv.org/abs/2306.01498