Zobrazeno 1 - 10
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pro vyhledávání: '"Agostinelli, A"'
In scientific fields such as quantum computing, physics, chemistry, and machine learning, high dimensional data are typically represented using sparse tensors. Tensor contraction is a popular operation on tensors to exploit meaning or alter the input
Externí odkaz:
http://arxiv.org/abs/2410.10094
Autor:
Cideron, Geoffrey, Agostinelli, Andrea, Ferret, Johan, Girgin, Sertan, Elie, Romuald, Bachem, Olivier, Perrin, Sarah, Ramé, Alexandre
Generative models are transforming creative domains such as music generation, with inference-time strategies like Classifier-Free Guidance (CFG) playing a crucial role. However, CFG doubles inference cost while limiting originality and diversity acro
Externí odkaz:
http://arxiv.org/abs/2410.06084
Pathfinding problems are found throughout robotics, computational science, and natural sciences. Traditional methods to solve these require training deep neural networks (DNNs) for each new problem domain, consuming substantial time and resources. Th
Externí odkaz:
http://arxiv.org/abs/2406.02598
A promising approach to preserving model performance in linearized transformers is to employ position-based re-weighting functions. However, state-of-the-art re-weighting functions rely heavily on target sequence lengths, making it difficult or impos
Externí odkaz:
http://arxiv.org/abs/2405.13046
Large language models (LLMs) have achieved state-of-the-art performance in various language processing tasks, motivating their adoption in simultaneous translation. Current fine-tuning methods to adapt LLMs for simultaneous translation focus on promp
Externí odkaz:
http://arxiv.org/abs/2405.10443
Autor:
Futeral, Matthieu, Agostinelli, Andrea, Tagliasacchi, Marco, Zeghidour, Neil, Kharitonov, Eugene
Generative spoken language models produce speech in a wide range of voices, prosody, and recording conditions, seemingly approaching the diversity of natural speech. However, the extent to which generated speech is acoustically diverse remains unclea
Externí odkaz:
http://arxiv.org/abs/2404.10419
Autor:
Cideron, Geoffrey, Girgin, Sertan, Verzetti, Mauro, Vincent, Damien, Kastelic, Matej, Borsos, Zalán, McWilliams, Brian, Ungureanu, Victor, Bachem, Olivier, Pietquin, Olivier, Geist, Matthieu, Hussenot, Léonard, Zeghidour, Neil, Agostinelli, Andrea
We propose MusicRL, the first music generation system finetuned from human feedback. Appreciation of text-to-music models is particularly subjective since the concept of musicality as well as the specific intention behind a caption are user-dependent
Externí odkaz:
http://arxiv.org/abs/2402.04229
Autor:
Lakkaraju, Kausik, Khandelwal, Vedant, Srivastava, Biplav, Agostinelli, Forest, Tang, Hengtao, Singh, Prathamjeet, Wu, Dezhi, Irvin, Matt, Kundu, Ashish
Artificial intelligence (AI) has the potential to transform education with its power of uncovering insights from massive data about student learning patterns. However, ethical and trustworthy concerns of AI have been raised but are unsolved. Prominen
Externí odkaz:
http://arxiv.org/abs/2402.01760
Autor:
Robiglio, Thomas, Neri, Matteo, Coppes, Davide, Agostinelli, Cosimo, Battiston, Federico, Lucas, Maxime, Petri, Giovanni
The interplay between causal mechanisms and emerging collective behaviors is a central aspect of understanding, controlling, and predicting complex networked systems. In our work, we investigate the relationship between higher-order mechanisms and hi
Externí odkaz:
http://arxiv.org/abs/2401.11588
We consider robust estimation of wrapped models to multivariate circular data that are points on the surface of a $p$-torus based on the weighted likelihood methodology.Robust model fitting is achieved by a set of weighted likelihood estimating equat
Externí odkaz:
http://arxiv.org/abs/2401.04686