SAILab

Niccolò Pancino When: Nov 9th, 2022 – 11:00 – 11:30 AM Where: Google meet link Description Drug Side Effect Prediction with Deep Learning Molecular Embedding in a Graph of Graphs Domain In collaboration with Yohann Perronn, Pietro Bongini and Franco Scarselli Drug Side Effects (DSEs) or Adverse Drug Reactions (ADRs) constitute an important health risk, […]

Federica Baccini When: Nov 2nd, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Weighted simplicial complexes and their representation power of higher-order network data and topology Hypergraphs and simplicial complexes both capture the higher-order interactions of complex systems, ranging from higher-order collaboration networks to brain networks. One open problem in the field […]

Marco Tanfoni When: Oct 19th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Monocular RGB Scene Reconstruction using Transformers by A. Božič, P. Palafox, J. Thies, A. Dai, M. Nießner. We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach. From an input monocular RGB video, the video frames are processed by a […]

Filippo Costanti When: Oct 12th, 2022 – 11:45 – 12:30 AM Where: Google meet link Description Lifelong Learning of Graph Neural Networks for Open-World Node Classification Graph neural networks (GNNs) have emerged as the standard method for numerous tasks on graph-structured data such as node classification. However, real-world graphs are often evolving over time and […]

Barbara Toniella Corradini When: Oct 12th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description A Deep Learning approach for oocytes segmentation and analysis Medical Assisted Procreation (MAP) has seen a sharp increase in demand over the past decade, due to a variety of reasons, including genetic factors, health conditions altered by stress […]

Michele Casoni When: Sep 28th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Continual Learning: an Optimal Control approach Continual Learning is a branch of Machine Learning which studies the ability of a model to learn continually from a stream of data. For academics and practitioners, this new way of conceiving learning […]

Elia Giuseppe Ceroni When: Sep 21st, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging by Lin Lu, Laurent Dercle, Binsheng Zhao, and Lawrence H. Schwartz In current clinical practice, tumor response assessment is usually based […]

Caterina Graziani When: Sep 14th, 2022 – 11:45 – 12:30 AM Where: Google meet link Description On the Extension of the Weisfeiler-Lehman Hierarchy by WL Tests for Arbitrary Graphs Graph isomorphism (GI) has occupied both theoreticians and applied scientists since the early 1950s. Over the years, several approaches and algorithms with which an isomorphism between […]

Veronica Lachi When: Sep 14th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Weisfeiler-Lehman goes dynamic: an analysis of the expressive power of Graph Neural Network for Attributed and Dynamic Graphs Graph Neural Networks (GNNs) are a large class of connectionist models for graph processing. Recent theoretical studies on the expressive power […]

Simone Marullo When: Jun 29th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Learning to Prompt for Continual Learning The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge. Typical methods rely on a rehearsal buffer or known […]