Zobrazeno 1 - 10
of 155
pro vyhledávání: '"ESTAPÉ, JORDI"'
Autor:
Cummins, Chris, Seeker, Volker, Armengol-Estapé, Jordi, Markosyan, Aram H., Synnaeve, Gabriel, Leather, Hugh
Tools for rewriting, refactoring and optimizing code should be fast and correct. Large language models (LLMs), by their nature, possess neither of these qualities. Yet, there remains tremendous opportunity in using LLMs to improve code. We explore th
Externí odkaz:
http://arxiv.org/abs/2410.08806
Autor:
Armengol-Estapé, Jordi, Michalski, Vincent, Kumar, Ramnath, St-Charles, Pierre-Luc, Precup, Doina, Kahou, Samira Ebrahimi
Few-shot learning aims to learn representations that can tackle novel tasks given a small number of examples. Recent studies show that cross-modal learning can improve representations for few-shot classification. More specifically, language is a rich
Externí odkaz:
http://arxiv.org/abs/2405.18751
Autor:
Armengol-Estapé, Jordi, Rocha, Rodrigo C. O., Woodruff, Jackson, Minervini, Pasquale, O'Boyle, Michael F. P.
The escalating demand to migrate legacy software across different Instruction Set Architectures (ISAs) has driven the development of assembly-to-assembly translators to map between their respective assembly languages. However, the development of thes
Externí odkaz:
http://arxiv.org/abs/2404.16041
Decompilation is a well-studied area with numerous high-quality tools available. These are frequently used for security tasks and to port legacy code. However, they regularly generate difficult-to-read programs and require a large amount of engineeri
Externí odkaz:
http://arxiv.org/abs/2305.12520
Autor:
Martínez, Pablo Antonio, Woodruff, Jackson, Armengol-Estapé, Jordi, Bernabé, Gregorio, García, José Manuel, O'Boyle, Michael F. P.
Publikováno v:
In Proceedings of the 32nd ACM SIGPLAN International Conference on Compiler Construction (CC '23), February 25-26, 2023, Montr\'eal, QC, Canada
Dedicated tensor accelerators demonstrate the importance of linear algebra in modern applications. Such accelerators have the potential for impressive performance gains, but require programmers to rewrite code using vendor APIs - a barrier to wider s
Externí odkaz:
http://arxiv.org/abs/2301.11659
Autor:
Gutiérrez-Fandiño, Asier, Pérez-Fernández, David, Armengol-Estapé, Jordi, Griol, David, Callejas, Zoraida
In the recent years, transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other t
Externí odkaz:
http://arxiv.org/abs/2206.15147
Autor:
de Gibert, Ona, Kharitonova, Ksenia, Figueras, Blanca Calvo, Armengol-Estapé, Jordi, Melero, Maite
In this work, we introduce sequence-to-sequence language resources for Catalan, a moderately under-resourced language, towards two tasks, namely: Summarization and Machine Translation (MT). We present two new abstractive summarization datasets in the
Externí odkaz:
http://arxiv.org/abs/2202.06871
The Large Labelled Logo Dataset (L3D): A Multipurpose and Hand-Labelled Continuously Growing Dataset
In this work, we present the Large Labelled Logo Dataset (L3D), a multipurpose, hand-labelled, continuously growing dataset. It is composed of around 770k of color 256x256 RGB images extracted from the European Union Intellectual Property Office (EUI
Externí odkaz:
http://arxiv.org/abs/2112.05404
We introduce a new language representation model in finance called Financial Embedding Analysis of Sentiment (FinEAS). In financial markets, news and investor sentiment are significant drivers of security prices. Thus, leveraging the capabilities of
Externí odkaz:
http://arxiv.org/abs/2111.00526
There are many Language Models for the English language according to its worldwide relevance. However, for the Spanish language, even if it is a widely spoken language, there are very few Spanish Language Models which result to be small and too gener
Externí odkaz:
http://arxiv.org/abs/2110.12201