Search Results for author: Cristian Meo

Found 8 papers, 3 papers with code

Extreme Precipitation Nowcasting using Transformer-based Generative Models

no code implementations6 Mar 2024 Cristian Meo, Ankush Roy, Mircea Lică, Junzhe Yin, Zeineb Bou Che, Yanbo Wang, Ruben Imhoff, Remko Uijlenhoet, Justin Dauwels

This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL) regularization.

Discrete Messages Improve Communication Efficiency among Isolated Intelligent Agents

no code implementations26 Dec 2023 Hang Chen, Yuchuan Jang, Weijie Zhou, Cristian Meo, Ziwei Chen, Dianbo Liu

Individuals, despite having varied life experiences and learning processes, can communicate effectively through languages.

TC-VAE: Uncovering Out-of-Distribution Data Generative Factors

no code implementations8 Apr 2023 Cristian Meo, Anirudh Goyal, Justin Dauwels

We show that the proposed model is able to uncover OOD generative factors on different datasets and outperforms on average the related baselines in terms of downstream disentanglement metrics.

Disentanglement

Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

2 code implementations4 Oct 2022 Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal, Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio

We formalize the notions of coordination level and heterogeneity level of an environment and present HECOGrid, a suite of multi-agent RL environments that facilitates empirical evaluation of different MARL approaches across different levels of coordination and environmental heterogeneity by providing a quantitative control over coordination and heterogeneity levels of the environment.

Multi-agent Reinforcement Learning reinforcement-learning +1

Adaptation through prediction: multisensory active inference torque control

1 code implementation13 Dec 2021 Cristian Meo, Giovanni Franzese, Corrado Pezzato, Max Spahn, Pablo Lanillos

Adaptation to external and internal changes is major for robotic systems in uncertain environments.

Active Inference in Robotics and Artificial Agents: Survey and Challenges

no code implementations3 Dec 2021 Pablo Lanillos, Cristian Meo, Corrado Pezzato, Ajith Anil Meera, Mohamed Baioumy, Wataru Ohata, Alexander Tschantz, Beren Millidge, Martijn Wisse, Christopher L. Buckley, Jun Tani

Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning.

Bayesian Inference

Multimodal VAE Active Inference Controller

1 code implementation7 Mar 2021 Cristian Meo, Pablo Lanillos

Active inference, a theoretical construct inspired by brain processing, is a promising alternative to control artificial agents.

Continuous Control Representation Learning

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