Zobrazeno 1 - 10
of 1 006
pro vyhledávání: '"Lúcio, M."'
We derive, and validate numerically, an analytical model for electron-only magnetic reconnection applicable to strongly magnetized (low-beta) plasmas. Our model predicts sub-ion-scale reconnection rates significantly higher than those pertaining to l
Externí odkaz:
http://arxiv.org/abs/2407.06020
We investigate the linear and nonlinear evolution of the ion-acoustic instability in a collisionless plasma via two-dimensional (2D2V) Vlasov-Poisson numerical simulations. We initialize the system in a stable state and gradually drive it towards ins
Externí odkaz:
http://arxiv.org/abs/2309.05563
Autor:
Shen, Junhong, Li, Liam, Dery, Lucio M., Staten, Corey, Khodak, Mikhail, Neubig, Graham, Talwalkar, Ameet
Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this wo
Externí odkaz:
http://arxiv.org/abs/2302.05738
We discuss the relationships between the outcome of the COVID-19 pandemic in Brazil at the municipal level and different health, social, demographic, and economic indices. We obtain significant correlations between the data gathered for each municipa
Externí odkaz:
http://arxiv.org/abs/2210.10840
As machine learning permeates more industries and models become more expensive and time consuming to train, the need for efficient automated hyperparameter optimization (HPO) has never been more pressing. Multi-step planning based approaches to hyper
Externí odkaz:
http://arxiv.org/abs/2210.04971
We study how available data on COVID-19 cases and deaths in different countries are reliable. Our analysis is based on a modification of the law of anomalous numbers, the Newcomb-Benford law, applied to the daily number of deaths and new cases in eac
Externí odkaz:
http://arxiv.org/abs/2208.11226
Autor:
Pozner, Raúl, Salariato, Diego, Zanotti, Christian A., Zavala-Gallo, Lucio M., Zuloaga, Fernando O.
Publikováno v:
Darwiniana, 2023 Jul 01. 11(1), 115-135.
Externí odkaz:
https://www.jstor.org/stable/27231418
Auxiliary objectives, supplementary learning signals that are introduced to help aid learning on data-starved or highly complex end-tasks, are commonplace in machine learning. Whilst much work has been done to formulate useful auxiliary objectives, t
Externí odkaz:
http://arxiv.org/abs/2205.14082
In most settings of practical concern, machine learning practitioners know in advance what end-task they wish to boost with auxiliary tasks. However, widely used methods for leveraging auxiliary data like pre-training and its continued-pretraining va
Externí odkaz:
http://arxiv.org/abs/2109.07437
While deep learning has been very beneficial in data-rich settings, tasks with smaller training set often resort to pre-training or multitask learning to leverage data from other tasks. In this case, careful consideration is needed to select tasks an
Externí odkaz:
http://arxiv.org/abs/2108.11346