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pro vyhledávání: '"Andrei, C."'
Link prediction models can benefit from incorporating textual descriptions of entities and relations, enabling fully inductive learning and flexibility in dynamic graphs. We address the challenge of also capturing rich structured information about th
Externí odkaz:
http://arxiv.org/abs/2408.06778
Autor:
Mirea, Anca G., Vlaicu, Ioana D., Derbali, Sarah, Neatu, Florentina, Tomulescu, Andrei G., Besleaga, Cristina, Enculescu, Monica, Kuncser, Andrei C., Iacoban, Alexandra C., Filipoiu, Nicolae, Cuzminschi, Marina, Nemnes, George A., Manolescu, Andrei, Florea, Mihaela, Pintilie, Ioana
Herein we present a comparative study among different mesoporous electron transporter layers (ETLs), namely nanometric m-TiO2, m-SnO2 and m-SnO2 quantum dots (QDs), deposited by spray coating method. The experimental data correlated with the photovol
Externí odkaz:
http://arxiv.org/abs/2406.18261
We argue that Transformers are essentially graph-to-graph models, with sequences just being a special case. Attention weights are functionally equivalent to graph edges. Our Graph-to-Graph Transformer architecture makes this ability explicit, by inpu
Externí odkaz:
http://arxiv.org/abs/2310.17936
Unlike the Open Domain Question Answering (ODQA) setting, the conversational (ODConvQA) domain has received limited attention when it comes to reevaluating baselines for both efficiency and effectiveness. In this paper, we study the State-of-the-Art
Externí odkaz:
http://arxiv.org/abs/2310.14708
Epigraphy increasingly turns to modern artificial intelligence (AI) technologies such as machine learning (ML) for extracting insights from ancient inscriptions. However, scarce labeled data for training ML algorithms severely limits current techniqu
Externí odkaz:
http://arxiv.org/abs/2310.07310
Document-level relation extraction typically relies on text-based encoders and hand-coded pooling heuristics to aggregate information learned by the encoder. In this paper, we leverage the intrinsic graph processing capabilities of the Transformer mo
Externí odkaz:
http://arxiv.org/abs/2308.14423
Autor:
Joaquim Barreto, Marilia Martins, Cynthia M. Borges, Sofia Helena Vitte, Wilson Nadruz Junior, Rodrigo Bueno de Oliveira, Andrei C. Sposito
Publikováno v:
Brazilian Journal of Nephrology, Vol 46, Iss 4 (2024)
Abstract The accumulation of advanced glycation end-products (AGEs) elicits morphofunctional kidney impairment. AGEs levels can be noninvasively estimated by skin autofluorescence (SAF). We explored whether high SAF predicts kidney outcomes in type 2
Externí odkaz:
https://doaj.org/article/d767b87abcaf4baaa25c4b4ab7671f4b
Autor:
Mahmood, Muhammad Arif, Diana, Chioibasu, Sajjad, Uzair, Mihai, Sabin, Tiseanu, Ion, Popescu, Andrei C.
Publikováno v:
Rapid Prototyping Journal, 2023, Vol. 30, Issue 3, pp. 415-429.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/RPJ-03-2023-0114
We demonstrate the adaption of three established methods to the field of surrogate machine learning model development. These methods are data augmentation, custom loss functions and transfer learning. Each of these methods have seen widespread use in
Externí odkaz:
http://arxiv.org/abs/2210.02631