Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Ferianc, Martin"'
Autor:
Chen, Hao Mark, Castelli, Liam, Ferianc, Martin, Zhou, Hongyu, Liu, Shuanglong, Luk, Wayne, Fan, Hongxiang
Reliable uncertainty estimation plays a crucial role in various safety-critical applications such as medical diagnosis and autonomous driving. In recent years, Bayesian neural networks (BayesNNs) have gained substantial research and industrial intere
Externí odkaz:
http://arxiv.org/abs/2406.14593
Autor:
Luo, Xiaoliang, Rechardt, Akilles, Sun, Guangzhi, Nejad, Kevin K., Yáñez, Felipe, Yilmaz, Bati, Lee, Kangjoo, Cohen, Alexandra O., Borghesani, Valentina, Pashkov, Anton, Marinazzo, Daniele, Nicholas, Jonathan, Salatiello, Alessandro, Sucholutsky, Ilia, Minervini, Pasquale, Razavi, Sepehr, Rocca, Roberta, Yusifov, Elkhan, Okalova, Tereza, Gu, Nianlong, Ferianc, Martin, Khona, Mikail, Patil, Kaustubh R., Lee, Pui-Shee, Mata, Rui, Myers, Nicholas E., Bizley, Jennifer K, Musslick, Sebastian, Bilgin, Isil Poyraz, Niso, Guiomar, Ales, Justin M., Gaebler, Michael, Murty, N Apurva Ratan, Loued-Khenissi, Leyla, Behler, Anna, Hall, Chloe M., Dafflon, Jessica, Bao, Sherry Dongqi, Love, Bradley C.
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could pot
Externí odkaz:
http://arxiv.org/abs/2403.03230
Ensembles of separate neural networks (NNs) have shown superior accuracy and confidence calibration over single NN across tasks. To improve the hardware efficiency of ensembles of separate NNs, recent methods create ensembles within a single network
Externí odkaz:
http://arxiv.org/abs/2402.06580
Autor:
Ferianc, Martin, Rodrigues, Miguel
YAMLE: Yet Another Machine Learning Environment is an open-source framework that facilitates rapid prototyping and experimentation with machine learning (ML) models and methods. The key motivation is to reduce repetitive work when implementing new ap
Externí odkaz:
http://arxiv.org/abs/2402.06268
The deployment of large language models (LLMs) raises concerns regarding their cultural misalignment and potential ramifications on individuals and societies with diverse cultural backgrounds. While the discourse has focused mainly on political and s
Externí odkaz:
http://arxiv.org/abs/2309.12342
Enhancing the generalisation abilities of neural networks (NNs) through integrating noise such as MixUp or Dropout during training has emerged as a powerful and adaptable technique. Despite the proven efficacy of noise in NN training, there is no con
Externí odkaz:
http://arxiv.org/abs/2306.17630
Continual learning enables the incremental training of machine learning models on non-stationary data streams.While academic interest in the topic is high, there is little indication of the use of state-of-the-art continual learning algorithms in pra
Externí odkaz:
http://arxiv.org/abs/2304.12067
Autor:
Ferianc, Martin, Rodrigues, Miguel
Considering uncertainty estimation of modern neural networks (NNs) is one of the most important steps towards deploying machine learning systems to meaningful real-world applications such as in medicine, finance or autonomous systems. At the moment,
Externí odkaz:
http://arxiv.org/abs/2205.09526
Neural networks (NNs) are making a large impact both on research and industry. Nevertheless, as NNs' accuracy increases, it is followed by an expansion in their size, required number of compute operations and energy consumption. Increase in resource
Externí odkaz:
http://arxiv.org/abs/2112.10229
Autor:
Abdalla, Youssef, Ferianc, Martin, Awad, Atheer, Kim, Jeesu, Elbadawi, Moe, Basit, Abdul W., Orlu, Mine, Rodrigues, Miguel
Publikováno v:
In International Journal of Pharmaceutics 15 August 2024 661