Zobrazeno 1 - 10
of 4 804
pro vyhledávání: '"Jaques, P. A."'
Training agents that can coordinate zero-shot with humans is a key mission in multi-agent reinforcement learning (MARL). Current algorithms focus on training simulated human partner policies which are then used to train a Cooperator agent. The simula
Externí odkaz:
http://arxiv.org/abs/2411.13934
InvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of Environmental, Social, and Governance (ESG) disclosure mandates on corporate climate investments. Supported by both PyTorch and JAX implementatio
Externí odkaz:
http://arxiv.org/abs/2411.09856
Autor:
Castro, Daniel C., Bustos, Aurelia, Bannur, Shruthi, Hyland, Stephanie L., Bouzid, Kenza, Wetscherek, Maria Teodora, Sánchez-Valverde, Maria Dolores, Jaques-Pérez, Lara, Pérez-Rodríguez, Lourdes, Takeda, Kenji, Salinas, José María, Alvarez-Valle, Javier, Herrero, Joaquín Galant, Pertusa, Antonio
Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) extends RRG by including the localisation of individual findings on the image. Currently, there are no man
Externí odkaz:
http://arxiv.org/abs/2411.05085
For AI agents to be helpful to humans, they should be able to follow natural language instructions to complete everyday cooperative tasks in human environments. However, real human instructions inherently possess ambiguity, because the human speakers
Externí odkaz:
http://arxiv.org/abs/2409.18073
Reinforcement Learning from Human Feedback (RLHF) is a powerful paradigm for aligning foundation models to human values and preferences. However, current RLHF techniques cannot account for the naturally occurring differences in individual human prefe
Externí odkaz:
http://arxiv.org/abs/2408.10075
Autor:
D'Ambrosio, David B., Abeyruwan, Saminda, Graesser, Laura, Iscen, Atil, Amor, Heni Ben, Bewley, Alex, Reed, Barney J., Reymann, Krista, Takayama, Leila, Tassa, Yuval, Choromanski, Krzysztof, Coumans, Erwin, Jain, Deepali, Jaitly, Navdeep, Jaques, Natasha, Kataoka, Satoshi, Kuang, Yuheng, Lazic, Nevena, Mahjourian, Reza, Moore, Sherry, Oslund, Kenneth, Shankar, Anish, Sindhwani, Vikas, Vanhoucke, Vincent, Vesom, Grace, Xu, Peng, Sanketi, Pannag R.
Achieving human-level speed and performance on real world tasks is a north star for the robotics research community. This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in c
Externí odkaz:
http://arxiv.org/abs/2408.03906
In this paper we investigate the explainability of transformer models and their plausibility for hate speech and counter speech detection. We compare representatives of four different explainability approaches, i.e., gradient-based, perturbation-base
Externí odkaz:
http://arxiv.org/abs/2407.20274
While the field of medical image analysis has undergone a transformative shift with the integration of machine learning techniques, the main challenge of these techniques is often the scarcity of large, diverse, and well-annotated datasets. Medical i
Externí odkaz:
http://arxiv.org/abs/2404.16000
Autor:
Abdulhai, Marwa, Serapio-Garcia, Gregory, Crepy, Clément, Valter, Daria, Canny, John, Jaques, Natasha
Moral foundations theory (MFT) is a psychological assessment tool that decomposes human moral reasoning into five factors, including care/harm, liberty/oppression, and sanctity/degradation (Graham et al., 2009). People vary in the weight they place o
Externí odkaz:
http://arxiv.org/abs/2310.15337
Autor:
de Morais, Felipe, Goldoni, Diógines, Kautzmann, Tiago, da Silva, Rodrigo, Jaques, Patricia A.
Emotions and other affective states play a pivotal role in cognition and, consequently, the learning process. It is well-established that computer-based learning environments (CBLEs) that can detect and adapt to students' affective states can enhance
Externí odkaz:
http://arxiv.org/abs/2310.13711