56 citations · 60 across the 15 of their papers we have counts for
8 papers · 1 filter
EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
Thomas Evers, Cristian Meo, Wendelin Bohmer +2
We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms. Central to this family is…
Assessing the Geographic Generalization and Physical Consistency of Generative Models for Climate Downscaling
Carlo Saccardi, Maximilian Pierzyna, Haitz Sáez de Ocáriz Borde +6
Kilometer-scale weather data is crucial for real-world applications but remains computationally intensive to produce using traditional weather simulations. An emerging solution is…
-TCVAE: On the relationship between Disentanglement and Diversity
Cristian Meo, Louis Mahon, Anirudh Goyal +1
While disentangled representations have shown promise in generative modeling and representation learning, their downstream usefulness remains debated. Recent studies re-defined dis…
Masked Generative Priors Improve World Models Sequence Modelling Capabilities
Cristian Meo, Mircea Lica, Zarif Ikram +6
Deep Reinforcement Learning (RL) has become the leading approach for creating artificial agents in complex environments. Model-based approaches, which are RL methods with world mod…
Precipitation Nowcasting Using Physics Informed Discriminator Generative Models
Junzhe Yin, Cristian Meo, Ankush Roy +5
Nowcasting leverages real-time atmospheric conditions to forecast weather over short periods. State-of-the-art models, including PySTEPS, encounter difficulties in accurately forec…
Extreme Precipitation Nowcasting using Transformer-based Generative Models
Cristian Meo, Ankush Roy, Mircea Lică +6
This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL)…
Discrete Messages Improve Communication Efficiency among Isolated Intelligent Agents
Hang Chen, Yuchuan Jang, Weijie Zhou +3
Individuals, despite having varied life experiences and learning processes, can communicate effectively through languages. This study aims to explore the efficiency of language as…
TC-VAE: Uncovering Out-of-Distribution Data Generative Factors
Cristian Meo, Anirudh Goyal, Justin Dauwels
Uncovering data generative factors is the ultimate goal of disentanglement learning. Although many works proposed disentangling generative models able to uncover the underlying gen…