Zobrazeno 1 - 10
of 852
pro vyhledávání: '"Ferreira, Fábio"'
Autor:
Guazzelli, Marcilei A., Avanzi, Luis H., Aguiar, Vitor A. P., Vilas-Bôas, Alexis C., Alberton, Saulo G., Masunaga, Sueli H., Chinaglia, Eliane F., Araki, Koiti, Nakamura, Marcelo, Toyama, Marcos M., Ferreira, Fabio F., Escote, Marcia T., Santos, Roberto B. B., Medina, Nilberto H., Oliveira, José Roberto B., Cappuzzello, Francesco, Cavallaro, Manuela
Publikováno v:
Diamond & Related Materials 151, 111803 (2025)
Highly Ordered Pyrolytic Graphite (HOPG) has been extensively researched due to its chemical and physical properties that make it suitable for applications in several technologies. Its high thermal conductivity makes HOPG an excellent heat sink, a cr
Externí odkaz:
http://arxiv.org/abs/2411.12737
Autor:
Strangmann, Tobias, Purucker, Lennart, Franke, Jörg K. H., Rapant, Ivo, Ferreira, Fabio, Hutter, Frank
As the landscape of large language models expands, efficiently finetuning for specific tasks becomes increasingly crucial. At the same time, the landscape of parameter-efficient finetuning methods rapidly expands. Consequently, practitioners face a m
Externí odkaz:
http://arxiv.org/abs/2411.01195
Autor:
Ferreira, Fabio S., Ashburner, John, Bouzigues, Arabella, Suksasilp, Chatrin, Russell, Lucy L., Foster, Phoebe H., Ferry-Bolder, Eve, van Swieten, John C., Jiskoot, Lize C., Seelaar, Harro, Sanchez-Valle, Raquel, Laforce, Robert, Graff, Caroline, Galimberti, Daniela, Vandenberghe, Rik, de Mendonca, Alexandre, Tiraboschi, Pietro, Santana, Isabel, Gerhard, Alexander, Levin, Johannes, Sorbi, Sandro, Otto, Markus, Pasquier, Florence, Ducharme, Simon, Butler, Chris R., Ber, Isabelle Le, Finger, Elizabeth, Tartaglia, Maria C., Masellis, Mario, Rowe, James B., Synofzik, Matthis, Moreno, Fermin, Borroni, Barbara, Kaski, Samuel, Rohrer, Jonathan D., Mourao-Miranda, Janaina
In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease understanding, treatment develo
Externí odkaz:
http://arxiv.org/abs/2410.07890
A World Model is a compressed spatial and temporal representation of a real world environment that allows one to train an agent or execute planning methods. However, world models are typically trained on observations from the real world environment,
Externí odkaz:
http://arxiv.org/abs/2409.14084
Many Self-Supervised Learning (SSL) methods aim for model invariance to different image augmentations known as views. To achieve this invariance, conventional approaches make use of random sampling operations within the image augmentation pipeline. W
Externí odkaz:
http://arxiv.org/abs/2310.03940
With the ever-increasing number of pretrained models, machine learning practitioners are continuously faced with which pretrained model to use, and how to finetune it for a new dataset. In this paper, we propose a methodology that jointly searches fo
Externí odkaz:
http://arxiv.org/abs/2306.03828
Autor:
Wagner, Diane, Ferreira, Fabio, Stoll, Danny, Schirrmeister, Robin Tibor, Müller, Samuel, Hutter, Frank
Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks. However, the rapid pace of advancements in this area comes at a price: traini
Externí odkaz:
http://arxiv.org/abs/2207.07875
We study the dielectric response of few layered crystals of various transition metal dichalcogenides (TMDs) and hexagonal Boron Nitride (hBN). We showed that the out-of-plane polarizability of a multilayer crystal (which characterizes response to the
Externí odkaz:
http://arxiv.org/abs/2206.09183
Autor:
Öztürk, Ekrem, Ferreira, Fabio, Jomaa, Hadi S., Schmidt-Thieme, Lars, Grabocka, Josif, Hutter, Frank
Publikováno v:
International Conference on Machine Learning 2022
Given a new dataset D and a low compute budget, how should we choose a pre-trained model to fine-tune to D, and set the fine-tuning hyperparameters without risking overfitting, particularly if D is small? Here, we extend automated machine learning (A
Externí odkaz:
http://arxiv.org/abs/2206.08476
Autor:
Baz, Adrian El, Ullah, Ihsan, Alcobaça, Edesio, Carvalho, André C. P. L. F., Chen, Hong, Ferreira, Fabio, Gouk, Henry, Guan, Chaoyu, Guyon, Isabelle, Hospedales, Timothy, Hu, Shell, Huisman, Mike, Hutter, Frank, Liu, Zhengying, Mohr, Felix, Öztürk, Ekrem, van Rijn, Jan N., Sun, Haozhe, Wang, Xin, Zhu, Wenwu
Publikováno v:
NeurIPS 2021 Competition and Demonstration Track, Dec 2021, On-line, United States
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resources, restricting their success to domains where such resources are avai
Externí odkaz:
http://arxiv.org/abs/2206.08138