Generative SHAP
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Related Articles from SNS
Dr. SHAP-AV: Decoding Relative Modality Contributions via Shapley Attribution in Audio-Visual Speech Recognition
arXiv:2603.12046v2 Announce Type: replace-cross Abstract: Audio-Visual Speech Recognition (AVSR) leverages both acoustic and visual information for robust recognition under noise. However, how models balance these modalities remains unclear. We present Dr. SHAP-AV, a framework using Shapley values to analyze modality contributions in AVSR.
From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment
Announce Type: new Abstract: Automated scoring models are increasingly used to assign rubric-based quality ratings to complex language performances, including classroom transcripts, yet they typically provide little insight into why a particular score is produced. We propose a general framework for sentence-level interpretability of rubric-based scoring that combines model-agnostic Shapley-value attributions with rationales generated by large language models (LLMs). Instantiated on the...
A Robust and Explainable Transformer-Based Framework for Phishing Email Detection
arXiv:2511.12085v3 Announce Type: replace Abstract: Phishing and related cyber threats are becoming increasingly sophisticated, with email-based phishing remaining the most persistent attack vector. These attacks exploit human vulnerabilities to deliver malware or gain unauthorized access to sensitive information. Transformer-based models enhance phishing detection through robust contextual language understanding; yet they are often regarded as black boxes due to a lack of interpretability.
Can AI be Easy? Lessons Learned from the EZR.py Toolkit
arXiv:2606.03640v1 Announce Type: new Abstract: Much recent press claims that developers no longer need to read code. We disagree, at least within the domain of tabular software-engineering (SE) optimization tasks: rows of $x$ and $y$ values where the $y$ values are expensive to obtain.
Learning quality scores for chromatin accessibility bigWig tracks using Machine Learning
High-throughput chromatin accessibility assays such as bulk and single-cell ATAC-seq have generated large collections of processed signal tracks in bigWig format, which are widely used for visualisation, data integration, and Machine Learning (ML)-based analyses. Despite their central role, systematic quality control (QC) frameworks operating directly at the level of bigWig signal tracks remain underdeveloped. This gap limits the ability to assess data reliability and hampers robust...
Mesoscopic cortical activities associated with pupil-linked perceptions inferred via explainable machine learning
Pupil dilation reflects arousal-related neural processes and is closely linked to sensory perception, attention, and cognitive state, but the mesoscopic cortical dynamics that accompany stimulus-evoked dilation remain unclear. Here, we combined simultaneous pupillometry and wide-field Ca2+imaging in mice with explainable machine learning to identify cortical activity patterns predictive of pupil dilation. Cortical activity was recorded during hindpaw somatosensory stimulation, visual pattern...