Zobrazeno 1 - 10
of 11 011
pro vyhledávání: '"Archambeau A"'
Autor:
Franceschi, Luca, Donini, Michele, Perrone, Valerio, Klein, Aaron, Archambeau, Cédric, Seeger, Matthias, Pontil, Massimiliano, Frasconi, Paolo
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determine the effectiveness of systems based on th
Externí odkaz:
http://arxiv.org/abs/2410.22854
Pre-trained language models (PLM), for example BERT or RoBERTa, mark the state-of-the-art for natural language understanding task when fine-tuned on labeled data. However, their large size poses challenges in deploying them for inference in real-worl
Externí odkaz:
http://arxiv.org/abs/2405.02267
A large branch of explainable machine learning is grounded in cooperative game theory. However, research indicates that game-theoretic explanations may mislead or be hard to interpret. We argue that often there is a critical mismatch between what one
Externí odkaz:
http://arxiv.org/abs/2402.09947
With increasing scale in model and dataset size, the training of deep neural networks becomes a massive computational burden. One approach to speed up the training process is Selective Backprop. For this approach, we perform a forward pass to obtain
Externí odkaz:
http://arxiv.org/abs/2312.05021
Large language models (LLMs) encode vast amounts of world knowledge. However, since these models are trained on large swaths of internet data, they are at risk of inordinately capturing information about dominant groups. This imbalance can propagate
Externí odkaz:
http://arxiv.org/abs/2310.14777
Autor:
Ben Grant
Publikováno v:
International Yeats Studies. 6:222-225
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::da865b3c9fbd0d3cc1cd4a9ac4a022e3
https://doi.org/10.34068/iys.6.1.14
https://doi.org/10.34068/iys.6.1.14
Autor:
Hanks, Patrick, Lenarčič, Simon
Publikováno v:
Dictionary of American Family Names, 2 ed., 2022.
Externí odkaz:
http://www.oxfordreference.com/view/10.1093/acref/9780190245115.001.0001/acref-9780190245115-e-2169
Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capt
Externí odkaz:
http://arxiv.org/abs/2305.03623
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