Master's Thesis - Evaluating Reliability of Static Analysis Results Using Machine Learning
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Updated
May 16, 2024 - TeX
Master's Thesis - Evaluating Reliability of Static Analysis Results Using Machine Learning
A presentation together with a short report on graph attention networks proposed by Veličković et al in 2018.
EE531: Statistical Learning Theory at KAIST, Fall 2019
A short review on Graph Neural Networks done during the Master's degree Mathematics, Vision, Learning (MVA) from ENS Paris-Saclay.
Slideshow for the nine month progression review of my PhD.
Notes for the CST Part III Representation Learning on Graph and Networks (L45) module
Exploring and visualizing limitations of message-passing paradigm for GNNs. 📉
Benchmarking Node2vec and DeepWalk for course on Graph Neural Networks
A PPI network driven approach to drug-target-interaction prediction using deep graph learning methods.
Multi-agent reinforcement learning on trains, for Deep Learning class at UNIBO
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