Zobrazeno 1 - 10
of 5 445
pro vyhledávání: '"P, Jacot"'
Diffusion-based generative models provide a powerful framework for learning to sample from a complex target distribution. The remarkable empirical success of these models applied to high-dimensional signals, including images and video, stands in star
Externí odkaz:
http://arxiv.org/abs/2410.11275
Deep neural networks (DNNs) at convergence consistently represent the training data in the last layer via a highly symmetric geometric structure referred to as neural collapse. This empirical evidence has spurred a line of theoretical research aimed
Externí odkaz:
http://arxiv.org/abs/2410.04887
We show that deep neural networks (DNNs) can efficiently learn any composition of functions with bounded $F_{1}$-norm, which allows DNNs to break the curse of dimensionality in ways that shallow networks cannot. More specifically, we derive a general
Externí odkaz:
http://arxiv.org/abs/2407.05664
The training dynamics of linear networks are well studied in two distinct setups: the lazy regime and balanced/active regime, depending on the initialization and width of the network. We provide a surprisingly simple unifying formula for the evolutio
Externí odkaz:
http://arxiv.org/abs/2405.17580
Autor:
Jacot, Arthur, Kaiser, Alexandre
We study Leaky ResNets, which interpolate between ResNets ($\tilde{L}=0$) and Fully-Connected nets ($\tilde{L}\to\infty$) depending on an 'effective depth' hyper-parameter $\tilde{L}$. In the infinite depth limit, we study 'representation geodesics'
Externí odkaz:
http://arxiv.org/abs/2405.17573
Autor:
Wen, Yuxiao, Jacot, Arthur
We describe the emergence of a Convolution Bottleneck (CBN) structure in CNNs, where the network uses its first few layers to transform the input representation into a representation that is supported only along a few frequencies and channels, before
Externí odkaz:
http://arxiv.org/abs/2402.08010
Autor:
Kevin Stritt, Beat Roth, Audrey Masnada, Felix Hammann, Damien Jacot, Sonia Domingos-Pereira, François Crettenand, Perrine Bohner, Isabelle Sommer, Emilien Bréat, Julien Sauser, Laurent Derré, Manuel Haschke, James J. Collins, John McKinney, Sylvain Meylan
Publikováno v:
Trials, Vol 25, Iss 1, Pp 1-10 (2024)
Abstract Background Urinary tract catheters, including Double-J or ureteral stents, are prone to bacterial colonization forming biofilms and leading to asymptomatic bacteriuria. In the context of asymptomatic bacteriuria, endourological procedures ca
Externí odkaz:
https://doaj.org/article/75781db5b8e844f3a26fc25aa92b7cf2
Autor:
Jacot, Arthur
Previous work has shown that DNNs with large depth $L$ and $L_{2}$-regularization are biased towards learning low-dimensional representations of the inputs, which can be interpreted as minimizing a notion of rank $R^{(0)}(f)$ of the learned function
Externí odkaz:
http://arxiv.org/abs/2305.19008
Autor:
Wang, Zihan, Jacot, Arthur
The $L_{2}$-regularized loss of Deep Linear Networks (DLNs) with more than one hidden layers has multiple local minima, corresponding to matrices with different ranks. In tasks such as matrix completion, the goal is to converge to the local minimum w
Externí odkaz:
http://arxiv.org/abs/2305.16038
Systemic cytokines related to memory function 6–9 months and 12–15 months after SARS-CoV-2 infection
Autor:
A. Nuber-Champier, G. Breville, P. Voruz, I. Jacot de Alcântara, A. Cionca, G. Allali, P. H. Lalive, L. Benzakour, K.-O. Lövblad, O. Braillard, M. Nehme, M. Coen, J. Serratrice, J.-L. Reny, J. Pugin, I. Guessous, B. N. Landis, F. Assal, Julie Anne Péron
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract Cognitive symptoms persisting beyond the acute phase of COVID-19 infection are commonly described for up to 2 years after infection. The relationship between cognitive performance, in particular episodic memory processes observed chronically
Externí odkaz:
https://doaj.org/article/a6ebd57f758b4b21871690dc9b4bed38