Zobrazeno 1 - 10
of 5 129
pro vyhledávání: '"Hansen, Lars"'
The language technology moonshot moment of Generative, Large Language Models (GLLMs) was not limited to English: These models brought a surge of technological applications, investments and hype to low-resource languages as well. However, the capabili
Externí odkaz:
http://arxiv.org/abs/2410.22839
In this work, we present BiSSL, a first-of-its-kind training framework that introduces bilevel optimization to enhance the alignment between the pretext pre-training and downstream fine-tuning stages in self-supervised learning. BiSSL formulates the
Externí odkaz:
http://arxiv.org/abs/2410.02387
Self-supervised speech representation models, particularly those leveraging transformer architectures, have demonstrated remarkable performance across various tasks such as speech recognition, speaker identification, and emotion detection. Recent stu
Externí odkaz:
http://arxiv.org/abs/2409.16302
Understanding how neural networks align with human cognitive processes is a crucial step toward developing more interpretable and reliable AI systems. Motivated by theories of human cognition, this study examines the relationship between \emph{convex
Externí odkaz:
http://arxiv.org/abs/2409.06362
Publikováno v:
2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), London, United Kingdom, 2024, pp. 1-6
Speech representation models based on the transformer architecture and trained by self-supervised learning have shown great promise for solving tasks such as speech and speaker recognition, keyword spotting, emotion detection, and more. Typically, it
Externí odkaz:
http://arxiv.org/abs/2408.11858
Electroencephalography (EEG) research typically focuses on tasks with narrowly defined objectives, but recent studies are expanding into the use of unlabeled data within larger models, aiming for a broader range of applications. This addresses a crit
Externí odkaz:
http://arxiv.org/abs/2408.08065
To examine the microstructural evolution that occurs during transient creep, we deformed olivine aggregates to different strains that spanned the initial transient deformation. Two sets of samples with different initial grain sizes of 5 $\mu$m and 20
Externí odkaz:
http://arxiv.org/abs/2407.04982
Much machine learning research progress is based on developing models and evaluating them on a benchmark dataset (e.g., ImageNet for images). However, applying such benchmark-successful methods to real-world data often does not work as expected. This
Externí odkaz:
http://arxiv.org/abs/2406.09981
Autor:
Tětková, Lenka, Scheidt, Teresa Karen, Fogh, Maria Mandrup, Jørgensen, Ellen Marie Gaunby, Nielsen, Finn Årup, Hansen, Lars Kai
Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user, viz.\ as a tool for personalized explainability. An important class of concept-based explainability methods is constru
Externí odkaz:
http://arxiv.org/abs/2404.07008
We study the implications of model uncertainty in a climate-economics framework with three types of capital: "dirty" capital that produces carbon emissions when used for production, "clean" capital that generates no emissions but is initially less pr
Externí odkaz:
http://arxiv.org/abs/2310.13200