Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Schmied, Thomas"'
Autor:
Paischer, Fabian, Hauzenberger, Lukas, Schmied, Thomas, Alkin, Benedikt, Deisenroth, Marc Peter, Hochreiter, Sepp
Foundation models (FMs) are pre-trained on large-scale datasets and then fine-tuned on a downstream task for a specific application. The most successful and most commonly used fine-tuning method is to update the pre-trained weights via a low-rank ada
Externí odkaz:
http://arxiv.org/abs/2410.07170
Autor:
Schmied, Thomas, Paischer, Fabian, Patil, Vihang, Hofmarcher, Markus, Pascanu, Razvan, Hochreiter, Sepp
In-context learning (ICL) is the ability of a model to learn a new task by observing a few exemplars in its context. While prevalent in NLP, this capability has recently also been observed in Reinforcement Learning (RL) settings. Prior in-context RL
Externí odkaz:
http://arxiv.org/abs/2410.07071
Reinforcement Learning (RL) has been successful in various domains like robotics, game playing, and simulation. While RL agents have shown impressive capabilities in their specific tasks, they insufficiently adapt to new tasks. In supervised learning
Externí odkaz:
http://arxiv.org/abs/2306.14884
Autor:
Steinparz, Christian, Schmied, Thomas, Paischer, Fabian, Dinu, Marius-Constantin, Patil, Vihang, Bitto-Nemling, Angela, Eghbal-zadeh, Hamid, Hochreiter, Sepp
In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a plethora of research problems such as continual domain shifts, which
Externí odkaz:
http://arxiv.org/abs/2207.05742
Autor:
Schmidt, Dominik, Schmied, Thomas
Across the Arcade Learning Environment, Rainbow achieves a level of performance competitive with humans and modern RL algorithms. However, attaining this level of performance requires large amounts of data and hardware resources, making research in t
Externí odkaz:
http://arxiv.org/abs/2111.10247
Machine learning (ML) methods have recently emerged as an effective way to perform automated parameter tuning of databases. State-of-the-art approaches include Bayesian optimization (BO) and reinforcement learning (RL). In this work, we describe our
Externí odkaz:
http://arxiv.org/abs/2011.07921
Autor:
Schmied, Thomas
Reinforcement learning (RL) methods learn through interaction with an environment. The RL paradigm is inherently designed to be performed in an online fashion. However, for many applications in the real world, learning online is not always feasible d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22b7cdc8bed8fa43977398e1a547d6e9
Autor:
Schmied, Thomas.
Publikováno v:
Onlinezugriff via World Wide Web.
Kurzfassung einer Seminararbeit Bern, 1984.
Externí odkaz:
http://www.digibern.ch/bzgh/sprung.php?id=1986035
Autor:
Schmied, Thomas
eingereicht von: Thomas Schmied Universität Linz, Univ., Diplomarbeit, 2015
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3361::392581ad49ae685075e1442e940db61a