9 papers
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…
Evaluation of Vision-LLMs in Surveillance Video
Pascal Benschop, Cristian Meo, Justin Dauwels +1
The widespread use of cameras in our society has created an overwhelming amount of video data, far exceeding the capacity for human monitoring. This presents a critical challenge f…
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…
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…
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates
Cristian Meo, Ksenia Sycheva, Anirudh Goyal +1
It is a common practice in natural language processing to pre-train a single model on a general domain and then fine-tune it for downstream tasks. However, when it comes to Large L…