Zobrazeno 1 - 10
of 6 669
pro vyhledávání: '"A. P. Hutter"'
Autor:
Arango, Sebastian Pineda, Janowski, Maciej, Purucker, Lennart, Zela, Arber, Hutter, Frank, Grabocka, Josif
Finetuning is a common practice widespread across different communities to adapt pretrained models to particular tasks. Text classification is one of these tasks for which many pretrained models are available. On the other hand, ensembles of neural n
Externí odkaz:
http://arxiv.org/abs/2410.19889
Navigating efficiently to an object in an unexplored environment is a critical skill for general-purpose intelligent robots. Recent approaches to this object goal navigation problem have embraced a modular strategy, integrating classical exploration
Externí odkaz:
http://arxiv.org/abs/2410.19697
Tabular machine learning problems often require time-consuming and labor-intensive feature engineering. Recent efforts have focused on using large language models (LLMs) to capitalize on their potential domain knowledge. At the same time, researchers
Externí odkaz:
http://arxiv.org/abs/2410.17787
Reinforcement learning (RL) often necessitates a meticulous Markov Decision Process (MDP) design tailored to each task. This work aims to address this challenge by proposing a systematic approach to behavior synthesis and control for multi-contact lo
Externí odkaz:
http://arxiv.org/abs/2410.13817
Fairness-aware Machine Learning (FairML) applications are often characterized by complex social objectives and legal requirements, frequently involving multiple, potentially conflicting notions of fairness. Despite the well-known Impossibility Theore
Externí odkaz:
http://arxiv.org/abs/2410.13286
This paper introduces Mamba4Cast, a zero-shot foundation model for time series forecasting. Based on the Mamba architecture and inspired by Prior-data Fitted Networks (PFNs), Mamba4Cast generalizes robustly across diverse time series tasks without th
Externí odkaz:
http://arxiv.org/abs/2410.09385
Large language models (LLMs) exhibit remarkable reasoning abilities, allowing them to generalize across a wide range of downstream tasks, such as commonsense reasoning or instruction following. However, as LLMs scale, inference costs become increasin
Externí odkaz:
http://arxiv.org/abs/2410.06479
In reinforcement learning, if the agent's reward differs from the designers' true utility, even only rarely, the state distribution resulting from the agent's policy can be very bad, in theory and in practice. When RL policies would devolve into unde
Externí odkaz:
http://arxiv.org/abs/2410.06213
Autor:
Spinelli, Filippo A., Egli, Pascal, Nubert, Julian, Nan, Fang, Bleumer, Thilo, Goegler, Patrick, Brockes, Stephan, Hofmann, Ferdinand, Hutter, Marco
The precise and safe control of heavy material handling machines presents numerous challenges due to the hard-to-model hydraulically actuated joints and the need for collision-free trajectory planning with a free-swinging end-effector tool. In this w
Externí odkaz:
http://arxiv.org/abs/2410.05093
Autor:
Mueller, Andreas, Siems, Julien, Nori, Harsha, Salinas, David, Zela, Arber, Caruana, Rich, Hutter, Frank
Generalized Additive Models (GAMs) are widely recognized for their ability to create fully interpretable machine learning models for tabular data. Traditionally, training GAMs involves iterative learning algorithms, such as splines, boosted trees, or
Externí odkaz:
http://arxiv.org/abs/2410.04560