@AntonioPuri00 and @frances55037016 recently delivered two seminars at Charles University in Prague: one on the evolution of recommender systems up to Sheaf Neural Networks, and one on how physics principles like entropy & renormalization groups can guide learning across domains.

RSTLess people at #ACL2025 presenting 2 papers + meeting with friends! @FlorinCuconasu presenting “The Distracting Effect: Understanding Irrelevant Passages in RAG” and @ValeriaRuscio presenting “Beyond Position: the emergence of wavelet-like properties in Transformers”. #NLP

Today @giulio_deras presented two works in the poster session of #ICTIR25 Eclipse: Contrastive Dimension Importance Estimation with Pseudo-Irrelevance Feedback for Dense Retrieval arxiv.org/pdf/2412.14967 and as a proxy presenter: QPP-RA: Aggregating Large Language Model Rankings

Last #IJCNN25 paper from our group, presented by @frances55037016 during the topological deep learning special session. Renormalized Graph Representations for Node Classification, co-authored with @Andrea_Bacciu , @GioTrappolini, @pl219_Cambridge and @fabreetseo.

@andreag_difra00, @giuditeodoro and @AntonioPuri00 presenting their work in the special session for Neural IR and RecSys at #IJCNN25, where @giuditeodoro also served as session chair! Lots of insights on Recommender systems. Link to the papers in the comments! #IR #Research

Our @_FedeSiciliano_ yesterday was interviewed by @hnonline, a Slovak newspaper, because of his expertise in AI. They discussed about the current status of AI, with a focus on the European Union vision of the topic. Bringing Italian style to Slovak 🇸🇰🇮🇹 #AI #Research #Recommender

Long day in the office for @andreag_difra00, presenting at #IJCNN2025: A poster for the competition on Learning with Noisy Graph Labels Link Prediction with Physics-Inspired GNNs (GNN session) And served as session chair for both events! Link: arxiv.org/abs/2402.14802

GREEN: an inference-time method using our new dataset EcoTaskSet to recommend Pareto-optimal models balancing accuracy and emissions. Our UI lets users choose dataset & accuracy-emissions trade-off and recommends the best model 📚arxiv.org/pdf/2505.01468 💻 github.com/antoniopurificato…

📢 Our paper "A Theoretical Analysis of Recommendation Loss Functions under Negative Sampling" has been accepted at #IJCNN2025! ✅ Done by @_FedeSiciliano_ , with @giuditeodoro , @ntonellotto , and @fabreetseo . 📍 See you in Rome! #RecommenderSystems

🎉 Paper accepted at #IJCNN2025! "Renormalized Graph Representations for Node Classification" by @frances55037016, @GioTrappolini, @Andrea_Bacciu, @pl219_Cambridge, @fabreetseo proposes a graph representation based on renormalization group and explores its use in graph learning.