Zobrazeno 1 - 10
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pro vyhledávání: '"knowledge integration."'
Vision Language Models (VLMs), pre-trained on large-scale image-text datasets, enable zero-shot predictions for unseen data but may underperform on specific unseen tasks. Continual learning (CL) can help VLMs effectively adapt to new data distributio
Externí odkaz:
http://arxiv.org/abs/2411.06764
Autor:
Igelbrink, Felix, Renz, Marian, Günther, Martin, Powell, Piper, Niecksch, Lennart, Lima, Oscar, Atzmueller, Martin, Hertzberg, Joachim
Semantic mapping is a key component of robots operating in and interacting with objects in structured environments. Traditionally, geometric and knowledge representations within a semantic map have only been loosely integrated. However, recent advanc
Externí odkaz:
http://arxiv.org/abs/2411.18147
Autor:
Ghunaim, Yasir, Hoehndorf, Robert
Pre-training machine learning models on molecular properties has proven effective for generating robust and generalizable representations, which is critical for advancements in drug discovery and materials science. While recent work has primarily foc
Externí odkaz:
http://arxiv.org/abs/2410.11914
A large-scale knowledge graph enhances reproducibility in biomedical data discovery by providing a standardized, integrated framework that ensures consistent interpretation across diverse datasets. It improves generalizability by connecting data from
Externí odkaz:
http://arxiv.org/abs/2410.07454
Publikováno v:
Journal of Knowledge Management, 2024, Vol. 28, Issue 10, pp. 3075-3103.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JKM-07-2023-0575
Publikováno v:
38th Conference on Neural Information Processing Systems (NeurIPS 2024)
Federated Learning has emerged as a promising paradigm for collaborative machine learning, while preserving user data privacy. Despite its potential, standard FL lacks support for diverse heterogeneous device prototypes, which vary significantly in m
Externí odkaz:
http://arxiv.org/abs/2409.18461