Saliency Maps (SMs) have been extensively used to interpret deep learnin...
Training object detection models usually requires instance-level annotat...
To apply federated learning to drug discovery we developed a novel platf...
SparseChem provides fast and accurate machine learning models for bioche...
To be able to predict a molecular graph structure (W) given a 2D image o...
Graph neural networks (GNNs) are a powerful architecture for tackling gr...
Research in Multiple Sclerosis (MS) has recently focused on extracting
k...
Bayesian regression remains a simple but effective tool based on Bayesia...
We prove a Central Limit Theorem (CLT) for Martin-Löf Random (MLR)
seque...
In drug discovery, knowledge of the graph structure of chemical compound...
In machine learning, chemical molecules are often represented by sparse
...
Modeling real-world multidimensional time series can be particularly
cha...
We present a generative approach to classify scarcely observed longitudi...
Solving linear systems is often the computational bottleneck in real-lif...
The simulation of a wave propagation caused by seismic stimulation allow...
High-dimensional data requires scalable algorithms. We propose and analy...
The understanding of the type of inhibitory interaction plays an importa...
We propose Macau, a powerful and flexible Bayesian factorization method ...