I am currently in the stage of proposing several research trajectories on the context of Applied Deep Learning to Multi-modal data. Here, there are three research trajectories that I am planning to undergo:
- Multi-modal fusion: this axis focusses on finding the effective means of merging different data modalities by also taking into account underlying data characteristics. Keywords: Geometric Deep Learning, Sensor fusion, Multi-modal learning.
- Feature Learning: this research direction concentrates in discovering automatic learning mechanisms for representative and effecient features generations from input datas. Keywords: Self-supervised learning, Unsupervised Learning, Few shot learnings.
- Models Explainability: this line of research considers the problems of explaining the models predictions, that as such, will be able to instill confident in models predicitons. Keywords: Explainability, Interpretability.
More updates on this topic will be communicated in this page soon.
Posted 19 April 2024