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pro vyhledávání: '"Cvicek, Vaclav"'
Machine learning models such as Transformers or LSTMs struggle with tasks that are compositional in nature such as those involving reasoning/inference. Although many datasets exist to evaluate compositional generalization, when it comes to evaluating
Externí odkaz:
http://arxiv.org/abs/2203.15099
Publikováno v:
ACL 2022
Several studies have reported the inability of Transformer models to generalize compositionally, a key type of generalization in many NLP tasks such as semantic parsing. In this paper we explore the design space of Transformer models showing that the
Externí odkaz:
http://arxiv.org/abs/2108.04378
Autor:
Ainslie, Joshua, Ontanon, Santiago, Alberti, Chris, Cvicek, Vaclav, Fisher, Zachary, Pham, Philip, Ravula, Anirudh, Sanghai, Sumit, Wang, Qifan, Yang, Li
Transformer models have advanced the state of the art in many Natural Language Processing (NLP) tasks. In this paper, we present a new Transformer architecture, Extended Transformer Construction (ETC), that addresses two key challenges of standard Tr
Externí odkaz:
http://arxiv.org/abs/2004.08483
Publikováno v:
Phys. Rev. Lett. 105, 176807 (2010)
A modest in-plane magnetic field \Bpar\ is sufficient to destroy the fractional quantized Hall states at $\nu = 5/2$ and 7/2 and replace them with anisotropic compressible phases. Remarkably, we find that at larger \Bpar\ these anisotropic phases can
Externí odkaz:
http://arxiv.org/abs/1006.2199
Publikováno v:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
Several studies have reported the inability of Transformer models to generalize compositionally, a key type of generalization in many NLP tasks such as semantic parsing. In this paper we explore the design space of Transformer models showing that the
Publikováno v:
PLoS Computational Biology. 3/30/2016, Vol. 12 Issue 3, p1-31. 31p. 6 Diagrams, 6 Charts, 1 Graph.
Autor:
Cvicek, Vaclav
G-protein coupled receptors (GPCRs) form a large family of proteins and are very important drug targets. They are membrane proteins, which makes computational prediction of their structure challenging. Homology modeling is further complicated by low
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A modest in-plane magnetic field \Bpar\ is sufficient to destroy the fractional quantized Hall states at $��= 5/2$ and 7/2 and replace them with anisotropic compressible phases. Remarkably, we find that at larger \Bpar\ these anisotropic phases c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52482e8ad83763c2f315f711ccb22b9b
Autor:
Ponzoni, Luca1 lponzoni@sissa.it, Rossetti, Giulia2,3,4,5 g.rossetti@fz-juelich.de, Maggi, Luca2, Giorgetti, Alejandro2,6, Carloni, Paolo2, Micheletti, Cristian1
Publikováno v:
PLoS Computational Biology. 2/3/2017, Vol. 13 Issue 2, p1-15. 15p.