Zobrazeno 1 - 10
of 7 964
pro vyhledávání: '"A. Eiras"'
Autor:
A. Eiras de Araújo
Publikováno v:
Revista Brasileira de Cancerologia, Vol 4, Iss 7 (2023)
A raridade da localização dos lipomas na região palmar, leva-nos a publicar o seguinte caso que tivemos oportunidade de operar no Serviço Nacional de Câncer: V. L. C., sexo feminino, 62 anos, branca, brasileira, casada, de profissão doméstica,
Externí odkaz:
https://doaj.org/article/372d4ed8ab6844ed8bcc482f48061b86
Autor:
A. Eiras de Araújo
Publikováno v:
Revista Brasileira de Cancerologia, Vol 24, Iss 37 (2023)
Depois de salientar que os métodos clássicos de tratamento do câncer da mama mastectomias mais ou menos radicais, associadas ou não à radioterapia — já deram tudo o que dêles se pode esperar, o Autor salienta a necessidade de serem tentadas
Externí odkaz:
https://doaj.org/article/989710b853e749e3a5b5386c5ff2b44a
Fine-tuning large language models on small, high-quality datasets can enhance their performance on specific downstream tasks. Recent research shows that fine-tuning on benign, instruction-following data can inadvertently undo the safety alignment pro
Externí odkaz:
http://arxiv.org/abs/2406.10288
Autor:
Eiras, Francisco, Petrov, Aleksandar, Vidgen, Bertie, Schroeder, Christian, Pizzati, Fabio, Elkins, Katherine, Mukhopadhyay, Supratik, Bibi, Adel, Purewal, Aaron, Botos, Csaba, Steibel, Fabro, Keshtkar, Fazel, Barez, Fazl, Smith, Genevieve, Guadagni, Gianluca, Chun, Jon, Cabot, Jordi, Imperial, Joseph, Nolazco, Juan Arturo, Landay, Lori, Jackson, Matthew, Torr, Phillip H. S., Darrell, Trevor, Lee, Yong, Foerster, Jakob
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the tec
Externí odkaz:
http://arxiv.org/abs/2405.08597
Autor:
Eiras, Francisco, Petrov, Aleksandar, Vidgen, Bertie, de Witt, Christian Schroeder, Pizzati, Fabio, Elkins, Katherine, Mukhopadhyay, Supratik, Bibi, Adel, Csaba, Botos, Steibel, Fabro, Barez, Fazl, Smith, Genevieve, Guadagni, Gianluca, Chun, Jon, Cabot, Jordi, Imperial, Joseph Marvin, Nolazco-Flores, Juan A., Landay, Lori, Jackson, Matthew, Röttger, Paul, Torr, Philip H. S., Darrell, Trevor, Lee, Yong Suk, Foerster, Jakob
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks
Externí odkaz:
http://arxiv.org/abs/2404.17047
Autor:
Paz-Ruza, Jorge, Alonso-Betanzos, Amparo, Guijarro-Berdiñas, Bertha, Cancela, Brais, Eiras-Franco, Carlos
Dyadic regression models, which predict real-valued outcomes for pairs of entities, are fundamental in many domains (e.g. predicting the rating of a user to a product in Recommender Systems) and promising and under exploration in many others (e.g. ap
Externí odkaz:
http://arxiv.org/abs/2401.10690
Referring Image Segmentation (RIS) - the problem of identifying objects in images through natural language sentences - is a challenging task currently mostly solved through supervised learning. However, while collecting referred annotation masks is a
Externí odkaz:
http://arxiv.org/abs/2310.13479
Autor:
Paz-Ruza, Jorge, Alonso-Betanzos, Amparo, Guijarro-Berdiñas, Berta, Cancela, Brais, Eiras-Franco, Carlos
Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these sy
Externí odkaz:
http://arxiv.org/abs/2308.01196
Autor:
Lamb, Tom A., Brunel, Rudy, Dvijotham, Krishnamurthy DJ, Kumar, M. Pawan, Torr, Philip H. S., Eiras, Francisco
Knowledge distillation (KD) has received much attention due to its success in compressing networks to allow for their deployment in resource-constrained systems. While the problem of adversarial robustness has been studied before in the KD setting, p
Externí odkaz:
http://arxiv.org/abs/2306.04431
Autor:
Eiras, Francisco, Bibi, Adel, Bunel, Rudy, Dvijotham, Krishnamurthy Dj, Torr, Philip, Kumar, M. Pawan
Recent work provides promising evidence that Physics-Informed Neural Networks (PINN) can efficiently solve partial differential equations (PDE). However, previous works have failed to provide guarantees on the worst-case residual error of a PINN acro
Externí odkaz:
http://arxiv.org/abs/2305.10157