Biagio (Mattia) La Rosa bio photo

Biagio (Mattia) La Rosa

I am a researcher interested in methodologies to improve the interpretability of Deep Learning techniques. Passionate also about neuroscience, psychology, games, and astronomy.

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Brainstorm XAI Reading Group

The Brainstorm XAI Reading Group is a group of international researchers who like to brainstorm about papers. The spirit of the group is friendly and drama-free. We value a diverse, equitable, and inclusive environment where all the members can express their opinions and where different point of views and cultures are encouraged and valued. The main purpose of each meeting is not to understand papers but to brainstorm about their weak spots, extensions, strengths, applications, etc. The reading group welcomes any person (student/researcher/professor) who is not afraid to share their own ideas, brainstorm as a group, and analyze and criticize a paper. You need just to fill this form https://forms.gle/UJKV2nhZkwKzJsfU9 in order to be included in the mailing list and get access to the calendar events. The group is active since 2023, discusses papers on Explainable AI (XAI), and we assume every member has a basic background in XAI. Our group has been quite heterogeneous, so we discuss papers from several domains (vision, graphs, NLP, RL, classic AI, etc).

Below you can find the news about the scheduled presentation for 2024/2025 season. Currently, we meet every other Tuesday at 6:30pm CET / 9:30am Los Angeles Time

  • 26 Nov 24
    • TBD
  • 12 Nov 24
    • “MambaLRP: Explaining Selective State Space Sequence Models”. Arnoush Rezaei Jafari, Gregoire Montavon, Klaus-Robert Muller, and Oliver Eberle
  • 29 Oct 24
    • “Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts”. Andong Tan, Fengtao Zhou, and Hao Chen

Past presentations (2023/2024)

  • Concept Learning for Interpretable Multi-Agent Reinforcement Learning. Renos Zabounidis, Joseph Campbell, Simon Stepputtis, Dana Hughes, Katia Sycara
  • Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents. Quentin Delfosse, Sebastian Sztwiertnia, Mark Rothermel, Wolfgang Stammer, Kristian Kersting
  • IA-RED2: Interpretability-Aware Redundancy Reduction for Vision Transformers. Bowen Pan, Rameswar Panda, Yifan Jiang, Zhangyang Wang, Rogerio Feris, Aude Oliva
  • This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin
  • CLIP-Dissect: Automatic Description of Neuron Representations in Deep Vision Networks. Tuomas Oikarinen, Tsui-Wei Weng
  • Everybody Needs a Little HELP: Explaining Graphs via Hierarchical Concepts. Jonas Jürß, Lucie Charlotte Magister, Pietro Barbiero, Pietro Liò, Nikola Simidjievski
  • Concept Bottleneck Generative Models. Aya Abdelsalam Ismail, Julius Adebayo, Hector Corrada Bravo, Stephen Ra, Kyunghyun Cho
  • Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents. Quentin Delfosse, Sebastian Sztwiertnia, Mark Rothermel, Wolfgang Stammer, Kristian Kersting
  • KAN: Kolmogorov-Arnold Networks. Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, Max Tegmark
  • Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet. Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L Turner, Callum McDougall, Monte MacDiarmid, Alex Tamkin, Esin Durmus, Tristan Hume, Francesco Mosconi, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, Tom Henighan
  • Interpreting Language Models with Contrastive Explanations. Kayo Yin, Graham Neubig
  • Locating and Editing Factual Associations in GPT. Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov
  • From attribution maps to human-understandable explanations through Concept Relevance Propagation. *Reduan Achtibat, Maximilian Dreyer, Ilona Eisenbraun, Sebastian Bosse, Thomas Wiegand, Wojciech Samek & Sebastian Lapuschkin
  • What s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules. Jonas Fischer, Anna Olah, Jilles Vreeken
  • Labeling Neural Representations with Inverse Recognition. Kirill Bykov, Laura Kopf, Shinichi Nakajima, Marius Kloft, Marina M.-C. Höhne