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检索条件"机构=Department of Machine Learning and Robotics"
177 条 记 录,以下是1-10 订阅
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Sampling-based multi-dimensional recalibration  24
Sampling-based multi-dimensional recalibration
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Proceedings of the 41st International Conference on machine learning
作者: Youngseog Chung Ian Char Jeff Schneider Machine Learning Department Machine Learning Department and Robotics Institute Carnegie Mellon University Pittsburgh PA
Calibration of probabilistic forecasts in the regression setting has been widely studied in the single dimensional case, where the output variables are assumed to be univariate. In many problem settings, however, the ...
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Multi-Label Classification for Low-Resource Issue Tickets with BERT
Multi-Label Classification for Low-Resource Issue Tickets wi...
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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024
作者: Sungthong, Chawanee Ruangtanusak, Saksorn Songmuang, Pokpong Thammasat University Faculty Of Science And Technology Department Of Computer Science Pathum Thani Thailand Bedrock Analytics Ai And Robotics Ventures Applied Machine Learning Team Bangkok Thailand
This study focuses on the problem in the field of issue ticketing systems, where the need for human involvement in classifying work task in the ticket often results in insufficient classification. Our primary focus is... 详细信息
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Steering Dialogue Dynamics for Robustness against Multi-turn Jailbreaking Attacks
arXiv
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arXiv 2025年
作者: Hu, Hanjiang Robey, Alexander Liu, Changliu Robotics Institute Machine Learning Department Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Robotics Institute Carnegie Mellon University United States
Large language models (LLMs) are highly vulnerable to jailbreaking attacks, wherein adversarial prompts are designed to elicit harmful responses. While existing defenses effectively mitigate single-turn attacks by det... 详细信息
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WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models
WEDGE: A multi-weather autonomous driving dataset built from...
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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023
作者: Marathe, Aboli Ramanan, Deva Walambe, Rahee Kotecha, Ketan Carnegie Mellon University Machine Learning Department PA United States Carnegie Mellon University Robotics Institute PA United States India India
The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distributio... 详细信息
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Int-HRL: towards intention-based hierarchical reinforcement learning
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Neural Computing and Applications 2024年 1-12页
作者: Penzkofer, Anna Schaefer, Simon Strohm, Florian Bâce, Mihai Leutenegger, Stefan Bulling, Andreas Institute for Visualisation and Interactive Systems University of Stuttgart Pfaffenwaldring 5A Stuttgart70569 Germany Machine Learning for Robotics Technical University of Munich Boltzmannstrasse 3 Munich85748 Germany Department of Computer Science KU Leuven box 2600 Andreas Vesaliusstraat 13 Leuven3000 Belgium
While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased samp... 详细信息
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Guided Decoding for Robot On-line Motion Generation and Adaption  23
Guided Decoding for Robot On-line Motion Generation and Adap...
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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024
作者: Chen, Nutan Cseke, Botond Aljalbout, Elie Paraschos, Alexandros Alles, Marvin Van Der Smagt, Patrick Machine Learning Research Lab Volkswagen Group Germany Robotics and Perception Group Department of Informatics Switzerland Uzh Eth Zurich Department of Neuroinformatics Switzerland Eötvös Loránd University Faculty of Informatics Budapest Hungary
We present a novel motion generation approach for robot arms, with high degrees of freedom, in complex settings that can adapt online to obstacles or new via points. learning from Demonstration facilitates rapid adapt... 详细信息
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Systematic Literature Review on Industry Revolution 4.0 to Predict Maintenance and Life Time of machines in Manufacturing Industry  3
Systematic Literature Review on Industry Revolution 4.0 to P...
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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023
作者: Sowmya, P. Ravichandran, Sathish Kumar Rakshitha Department of Robotics and Artificial Intelligence Karnataka Nitte India Christ University School of Engineering and Technology Department of Computer Science Karnataka Bangalore India Department of Artificial Intelligence and Machine Learning Karnataka Nitte India
Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic need... 详细信息
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PID-inspired inductive biases for deep reinforcement learning in partially observable control tasks  23
PID-inspired inductive biases for deep reinforcement learnin...
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Proceedings of the 37th International Conference on Neural Information Processing Systems
作者: Ian Char Jeff Schneider Machine Learning Department Carnegie Mellon University Pittsburgh PA Machine Learning Department Robotics Institute Carnegie Mellon University Pittsburgh PA
Deep reinforcement learning (RL) has shown immense potential for learning to control systems through data alone. However, one challenge deep RL faces is that the full state of the system is often not observable. When ...
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One Policy to Run Them All: an End-to-end learning Approach to Multi-Embodiment Locomotion  8
One Policy to Run Them All: an End-to-end Learning Approach ...
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8th Conference on Robot learning, CoRL 2024
作者: Bohlinger, Nico Czechmanowski, Grzegorz Krupka, Maciej Kicki, Piotr Walas, Krzysztof Peters, Jan Tateo, Davide Department of Computer Science Technical University of Darmstadt Germany Institute of Robotics and Machine Intelligence Poznan University of Technology Poland Research Department: Systems AI for Robot Learning Germany IDEAS NCBR Warsaw Poland Hessian.AI Germany Centre for Cognitive Science Germany
Deep Reinforcement learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is... 详细信息
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AN EXPERIMENTAL DESIGN PERSPECTIVE ON MODEL-BASED REINFORCEMENT learning  10
AN EXPERIMENTAL DESIGN PERSPECTIVE ON MODEL-BASED REINFORCEM...
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10th International Conference on learning Representations, ICLR 2022
作者: Mehta, Viraj Paria, Biswajit Schneider, Jeff Ermon, Stefano Neiswanger, Willie Robotics Insitute Machine Learning Department Carnegie Mellon University PittsburghPA United States Computer Science Department Stanford University StanfordCA United States
In many practical applications of RL, it is expensive to observe state transitions from the environment. For example, in the problem of plasma control for nuclear fusion, computing the next state for a given state-act... 详细信息
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