arXiv:2607. 17508v1 Announce Type: cross Abstract: We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesizes coefficient-space structure from natural-language task descriptions and a memory of previously learned task-specific predictors.
By Sazan Mahbub, Caleb Ellington, Zhiyuan Li, Yixin Yang, Souvik Kundu, Ben Lengerich, Eric P. Xing
arXiv:2608. 14662v1 Announce Type: cross Abstract: Accurate recognition of pain using physiological signals remains a challenging problem due to pain's subjective nature and high inter-individual variability.
By Dominika Kunc, Przemys{\l}aw Kazienko, Stanis{\l}aw Saganowski
arXiv:2509. 16727v5 Announce Type: replace-cross Abstract: Automated pain assessment from facial expressions is crucial for non-communicative patient.
By Xin Lei Lin, Soroush Mehraban, Abhishek Moturu, Babak Taati
The study investigates whether language‑grounded explanations improve trust in automated sheep pain recognition from facial expressions. By grounding a model in the Sheep Pain Facial Expression Scale (SPFES) and testing attention‑based explanations, the authors find that such explanations are largely ineffective. They then replace the appearance bypass with a concept bottleneck that reads only SPFES concept scores, which slightly reduces performance but yields demonstrably learned concepts and better recovery of minority pain states.
By Alam Noor, Miguel Guti'errez Gait'an
The paper investigates whether language‑grounded explanations improve trust in automated sheep pain detection from facial expressions. It finds that attention‑based explanations tied to the Sheep Pain Facial Expression Scale (SPFES) are largely ineffective, prompting the authors to replace the appearance bypass with a concept bottleneck that uses SPFES concept scores supervised by per‑region annotations. This new architecture slightly reduces performance but yields more semantically meaningful concepts, recovering minority pain states and revealing ear‑and‑eye severity orderings without explicit severity supervision, and highlights the pitfalls of pooled concept accuracy under clinical imbalance.
arXiv:2608. 14675v1 Announce Type: cross Abstract: While Self-Supervised Learning (SSL) effectively extracts general representations from noisy, unconstrained physiological signals such as photoplethysmography (PPG), its suitability for highly subjective tasks remains unproven.
By Dominika Kunc, Przemys{\l}aw Kazienko, Stanis{\l}aw Saganowski