1 citations · 2 across the 13 of their papers we have counts for
7 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…
BlockGPT: Spatio-Temporal Modelling of Rainfall via Frame-Level Autoregression
Cristian Meo, Varun Sarathchandran, Avijit Majhi +6
Predicting precipitation maps is a highly complex spatiotemporal modeling task, critical for mitigating the impacts of extreme weather events. Short-term precipitation forecasting,…
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)…