Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Paischer, Fabian"'
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
Recently, vision-language models like CLIP have advanced the state of the art in a variety of multi-modal tasks including image captioning and caption evaluation. Many approaches adapt CLIP-style models to a downstream task by training a mapping netw
Externí odkaz:
http://arxiv.org/abs/2307.05591
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
Reinforcement learning agents deployed in the real world often have to cope with partially observable environments. Therefore, most agents employ memory mechanisms to approximate the state of the environment. Recently, there have been impressive succ
Externí odkaz:
http://arxiv.org/abs/2306.09312
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:
Paischer, Fabian, Adler, Thomas, Patil, Vihang, Bitto-Nemling, Angela, Holzleitner, Markus, Lehner, Sebastian, Eghbal-zadeh, Hamid, Hochreiter, Sepp
In a partially observable Markov decision process (POMDP), an agent typically uses a representation of the past to approximate the underlying MDP. We propose to utilize a frozen Pretrained Language Transformer (PLT) for history representation and com
Externí odkaz:
http://arxiv.org/abs/2205.12258
Large pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are trained on English text only. It is exceeding
Externí odkaz:
http://arxiv.org/abs/2112.06598
Textual and semantic comprehension of images is essential for generating proper captions. The comprehension requires detection of objects, modeling of relations between them, an assessment of the semantics of the scene and, finally, representing the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d7f58c15430ed58118f7837c11333d6f
http://arxiv.org/abs/2307.05591
http://arxiv.org/abs/2307.05591
Reinforcement learning agents deployed in the real world often have to cope with partially observable environments. Therefore, most agents employ memory mechanisms to approximate the state of the environment. Recently, there have been impressive succ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dca94c937c90937f2a1364b62780177d
http://arxiv.org/abs/2306.09312
http://arxiv.org/abs/2306.09312