Zobrazeno 1 - 10
of 1 343
pro vyhledávání: '"A Nabli"'
Autor:
Rago, Antonio, Palfi, Bence, Sukpanichnant, Purin, Nabli, Hannibal, Vivek, Kavyesh, Kostopoulou, Olga, Kinross, James, Toni, Francesca
In recent years, various methods have been introduced for explaining the outputs of "black-box" AI models. However, it is not well understood whether users actually comprehend and trust these explanations. In this paper, we focus on explanations for
Externí odkaz:
http://arxiv.org/abs/2408.17401
Autor:
Nabli, Adel, Fournier, Louis, Erbacher, Pierre, Serrano, Louis, Belilovsky, Eugene, Oyallon, Edouard
Training Large Language Models (LLMs) relies heavily on distributed implementations, employing multiple GPUs to compute stochastic gradients on model replicas in parallel. However, synchronizing gradients in data parallel settings induces a communica
Externí odkaz:
http://arxiv.org/abs/2406.02613
Autor:
Fournier, Louis, Nabli, Adel, Aminbeidokhti, Masih, Pedersoli, Marco, Belilovsky, Eugene, Oyallon, Edouard
The performance of deep neural networks is enhanced by ensemble methods, which average the output of several models. However, this comes at an increased cost at inference. Weight averaging methods aim at balancing the generalization of ensembling and
Externí odkaz:
http://arxiv.org/abs/2405.17517
$\textbf{A}^2\textbf{CiD}^2$: Accelerating Asynchronous Communication in Decentralized Deep Learning
Publikováno v:
Thirty-seventh Conference on Neural Information Processing Systems, Dec 2023, New Orleans, United States
Distributed training of Deep Learning models has been critical to many recent successes in the field. Current standard methods primarily rely on synchronous centralized algorithms which induce major communication bottlenecks and synchronization locks
Externí odkaz:
http://arxiv.org/abs/2306.08289
Autor:
Nabli, Adel, Oyallon, Edouard
This work introduces DADAO: the first decentralized, accelerated, asynchronous, primal, first-order algorithm to minimize a sum of $L$-smooth and $\mu$-strongly convex functions distributed over a given network of size $n$. Our key insight is based o
Externí odkaz:
http://arxiv.org/abs/2208.00779
We introduce a simple neural encoder architecture that can be trained using an unsupervised contrastive learning objective which gets its positive samples from data-augmented k-Nearest Neighbors search. We show that when built on top of recent self-s
Externí odkaz:
http://arxiv.org/abs/2204.05148
Publikováno v:
In Computers & Industrial Engineering January 2025 199
Publikováno v:
Heliyon, Vol 10, Iss 9, Pp e30004- (2024)
Abstract Page: Background: Primary Sjogren's syndrome (pSS) is an autoimmune exocrinopathy in which extraglandular signs of pSS are determinant for the prognosis. Involvement of both peripheral and central nervous system (CNS) are known to be among t
Externí odkaz:
https://doaj.org/article/c9c8a3b0ec1d47cea139580acaab1b39
Publikováno v:
In Journal of Food Composition and Analysis June 2024 130
Autor:
Nabli, Adel, Carvalho, Margarida
Learning heuristics for combinatorial optimization problems through graph neural networks have recently shown promising results on some classic NP-hard problems. These are single-level optimization problems with only one player. Multilevel combinator
Externí odkaz:
http://arxiv.org/abs/2007.03151