1 citations · 2 across the 13 of their papers we have counts for
13 papers
Classification of IED-free EEG Responses for Assisted Epilepsy Diagnosis
Giacomo Zanardini, Ryan Moesman, Paul van der Kleij +2
Diagnosing epilepsy is challenging when routine EEGs lack interictal epileptiform discharges (IEDs). Intermittent photic stimulation (IPS) and hyperventilation (HV) can increase di…
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 Situational and Spatial Awareness of VLMs with Synthetically Generated Video
Pascal Benschop, Justin Dauwels, Jan van Gemert
Spatial reasoning in vision language models (VLMs) remains fragile when semantics hinge on subtle temporal or geometric cues. We introduce a synthetic benchmark that probes two com…
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…
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…
Compositional Scene Understanding through Inverse Generative Modeling
Yanbo Wang, Justin Dauwels, Yilun Du
Generative models have demonstrated remarkable abilities in generating high-fidelity visual content. In this work, we explore how generative models can further be used not only to…
-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…
Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases
Cristian Meo, Akihiro Nakano, Mircea Lică +7
Unsupervised object-centric learning from videos is a promising approach towards learning compositional representations that can be applied to various downstream tasks, such as pre…
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…
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…
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)…