5 citations · 9 across the 4 of their papers we have counts for
6 papers · 1 filter
Aligned Diffusion Schrödinger Bridges
Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh +3
Diffusion Schrödinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points. Despi…
It's FLAN time! Summing feature-wise latent representations for interpretability
An-phi Nguyen, Maria Rodriguez Martinez
Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. legal system, healthcare. In these situations, algorithmic decision…
MonoNet: Towards Interpretable Models by Learning Monotonic Features
An-phi Nguyen, María Rodríguez Martínez
Being able to interpret, or explain, the predictions made by a machine learning model is of fundamental importance. This is especially true when there is interest in deploying data…
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
Matteo Manica, Ali Oskooei, Jannis Born +3
In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using…
edGNN: a Simple and Powerful GNN for Directed Labeled Graphs
Guillaume Jaume, An-phi Nguyen, María Rodríguez Martínez +2
The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings. Building on pr…
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
Ali Oskooei, Jannis Born, Matteo Manica +3
We present a novel approach for the prediction of anticancer compound sensitivity by means of multi-modal attention-based neural networks (PaccMann). In our approach, we integrate…