Zobrazeno 1 - 10
of 7 968
pro vyhledávání: '"A, Eiras"'
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
Publikováno v:
Virology Journal, Vol 21, Iss 1, Pp 1-7 (2024)
Abstract Viroids that belong to genera Avsunviroid and Pelamovirod (family Avsunviroidae) replicate and accumulate in the chloroplasts of infected cells. In this report, we confirmed by RNA in situ hybridization using digoxigenin-UTP-labelled ribopro
Externí odkaz:
https://doaj.org/article/edbaa5121ac64b009a45dc50d8faaa4b
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
Improving and guaranteeing the robustness of deep learning models has been a topic of intense research. Ensembling, which combines several classifiers to provide a better model, has shown to be beneficial for generalisation, uncertainty estimation, c
Externí odkaz:
http://arxiv.org/abs/2304.13019