Very productive spring in the lab with 4 newly accepted papers at top ML conferences by Alessio Gravina, Riccardo Massidda and Michele Resta. Congrats guys!

Check here if you are interested in deep learning models for dynamic graphs, as seen through the lenses of neural ODEs:

Gravina, A., Lovisotto, G., Gallicchio, C., Bacciu, D., & Grohnfeldt, C. (2024). Long Range Propagation on Continuous-Time Dynamic Graphs. ICML 2024, https://arxiv.org/abs/2406.02740 

Gravina, A., Zambon, D., Bacciu, D., & Alippi, C. (2024). Temporal Graph ODEs for Irregularly-Sampled Time Series. IJCAI 2024, https://arxiv.org/abs/2404.19508

Here is the result of a joint collaboration with Sara Magliacane at University of Amsterdam on learning causal abstractions:

Massidda, Riccardo, Sara Magliacane, and Davide Bacciu. "Learning Causal Abstractions of Linear Structural Causal Models." UAI 2024, https://arxiv.org/abs/2406.00394

Last but not least, learn how you can use your multilingual language model to self-generate experiences to constrast catastrophic forgetting in continual incremental language learning:

Resta, Michele, and Davide Bacciu  Self-generated Replay Memories for Continual Neural Machine Translation. NAACL 2024, https://arxiv.org/abs/2403.13130

COSMO allows graph structure learning in a constraint-free scalable way, bringing computational complexity down from cubitc to quadratic (in number of nodes). Thanks to the COSMOlogists team that made this possible: Riccardo Massidda, Francesco Landolfi and Martina Cinquini.

My interview with AI news on AI history, perspective and strategies in the Italian and European area, with a special mention to our EMERGE Project and the role of awareness in AI systems. Written transcript here (In Italian).