Zobrazeno 1 - 10
of 234
pro vyhledávání: '"Runkler, Thomas"'
Large Language Models (LLMs) have provided a new pathway for Named Entity Recognition (NER) tasks. Compared with fine-tuning, LLM-powered prompting methods avoid the need for training, conserve substantial computational resources, and rely on minimal
Externí odkaz:
http://arxiv.org/abs/2407.08035
Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This
Externí odkaz:
http://arxiv.org/abs/2405.07601
Autor:
Ha, Cuong Nhat, Asaadi, Shima, Karn, Sanjeev Kumar, Farri, Oladimeji, Heimann, Tobias, Runkler, Thomas
Vision-language models, while effective in general domains and showing strong performance in diverse multi-modal applications like visual question-answering (VQA), struggle to maintain the same level of effectiveness in more specialized domains, e.g.
Externí odkaz:
http://arxiv.org/abs/2404.16192
This paper presents the first algorithm for model-based offline quantum reinforcement learning and demonstrates its functionality on the cart-pole benchmark. The model and the policy to be optimized are each implemented as variational quantum circuit
Externí odkaz:
http://arxiv.org/abs/2404.10017
Web tables contain a large amount of valuable knowledge and have inspired tabular language models aimed at tackling table interpretation (TI) tasks. In this paper, we analyse a widely used benchmark dataset for evaluation of TI tasks, particularly fo
Externí odkaz:
http://arxiv.org/abs/2403.04577
The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers. The prevalence of these miniature devices raises the question of whether aggregating t
Externí odkaz:
http://arxiv.org/abs/2307.06822
Recently, offline RL algorithms have been proposed that remain adaptive at runtime. For example, the LION algorithm \cite{lion} provides the user with an interface to set the trade-off between behavior cloning and optimality w.r.t. the estimated retu
Externí odkaz:
http://arxiv.org/abs/2306.09744
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications c
Externí odkaz:
http://arxiv.org/abs/2304.05201
Publikováno v:
2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022)
Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain. Due to the increasing amounts of data generated by monitoring tools as well as more and more sop
Externí odkaz:
http://arxiv.org/abs/2212.13991
Swarm intelligence is a discipline that studies the collective behavior that is produced by local interactions of a group of individuals with each other and with their environment. In Computer Science domain, numerous swarm intelligence techniques ar
Externí odkaz:
http://arxiv.org/abs/2211.07940