Zobrazeno 1 - 10
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pro vyhledávání: '"Bié A"'
Autor:
Buyl, Maarten, Rogiers, Alexander, Noels, Sander, Dominguez-Catena, Iris, Heiter, Edith, Romero, Raphael, Johary, Iman, Mara, Alexandru-Cristian, Lijffijt, Jefrey, De Bie, Tijl
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These models have become popular in artificial intelligence (AI) assistants
Externí odkaz:
http://arxiv.org/abs/2410.18417
This paper presents FinGEITje, the first Dutch financial Large Language Model (LLM) specifically designed and optimized for various financial tasks. Together with the model, we release a specialized Dutch financial instruction tuning dataset with ove
Externí odkaz:
http://arxiv.org/abs/2410.12835
The increasing demand for transparent and reliable models, particularly in high-stakes decision-making areas such as medical image analysis, has led to the emergence of eXplainable Artificial Intelligence (XAI). Post-hoc XAI techniques, which aim to
Externí odkaz:
http://arxiv.org/abs/2410.02331
Numerous methods have been implemented that pursue fairness with respect to sensitive features by mitigating biases in machine learning. Yet, the problem settings that each method tackles vary significantly, including the stage of intervention, the c
Externí odkaz:
http://arxiv.org/abs/2409.16965
Publikováno v:
EUROPEAN SIGNAL PROCESSING CONFERENCE 2024 [EUSIPCO], Aug 2024, Lyon, France
Latent representation learning has been an active field of study for decades in numerous applications. Inspired among others by the tokenization from Natural Language Processing and motivated by the research of a simple data representation, recent wo
Externí odkaz:
http://arxiv.org/abs/2409.16677
Neural audio codecs have significantly advanced audio compression by efficiently converting continuous audio signals into discrete tokens. These codecs preserve high-quality sound and enable sophisticated sound generation through generative models tr
Externí odkaz:
http://arxiv.org/abs/2409.11228
Publikováno v:
2024 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2024), Sep 2024, London (UK), United Kingdom
As diffusion-based deep generative models gain prevalence, researchers are actively investigating their potential applications across various domains, including music synthesis and style alteration. Within this work, we are interested in timbre trans
Externí odkaz:
http://arxiv.org/abs/2409.15321
We explore tree-based macroeconomic regime-switching in the context of the dynamic Nelson-Siegel (DNS) yield-curve model. In particular, we customize the tree-growing algorithm to partition macroeconomic variables based on the DNS model's marginal li
Externí odkaz:
http://arxiv.org/abs/2408.12863
Autor:
Amin, Kareem, Bie, Alex, Kong, Weiwei, Kurakin, Alexey, Ponomareva, Natalia, Syed, Umar, Terzis, Andreas, Vassilvitskii, Sergei
We present an approach for generating differentially private synthetic text using large language models (LLMs), via private prediction. In the private prediction framework, we only require the output synthetic data to satisfy differential privacy gua
Externí odkaz:
http://arxiv.org/abs/2407.12108
In this paper, we show that a Clifford algebra valued function is slice if and only if it is in the kernel of Dunkl-spherical Dirac operator and a slice function $f$ is slice regular if and only if it is in the kernel of the Dunkl-Cauchy-Riemann oper
Externí odkaz:
http://arxiv.org/abs/2407.06811