We wish him all the best for continuing his work on explainability & frontier pathology models at Aignostics!

🧱RudolfV: a foundation model by pathologists for pathologists: arxiv.org/abs/2401.04079

🧱Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics: arxiv.org/abs/2501.05409

🔎xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology: proceedings.neurips.cc/paper_files/...

🔎Explainable AI reveals Clever Hans effects in unsupervised learning models: www.nature.com/articles/s42...

🔎Toward Explainable Artificial Intelligence for Precision Pathology: www.annualreviews.org/content/jour...

Counterfactual explainers for dynamic graphs by Qu et al. by @scadsai.bsky.social
arxiv.org/abs/2403.16846
Explainable Biomedical Claim Verification by Liang et al. from @dfki.bsky.social
arxiv.org/abs/2502.21014

We were happy to see other explainability-themed posters:
A study of monosemanticity of SAE features in VLMs by Pach et al. from @munichcenterml.bsky.social
arxiv.org/abs/2504.02821
User-centered research for data attribution by Nguyen et al. from @tuebingen-ai.bsky.social
arxiv.org/abs/2409.16978

Manuel Welte presented ongoing work on intrinsic interpretability of transformer models through a novel approach for restructuring internal representations.

@lkopf.bsky.social and @eberleoliver.bsky.social presented the PRISM framework for multi-concept feature descriptions in LLMs.
arxiv.org/abs/2506.15538