(WWW'21) ATON - an Outlier Interpreation / Outlier explanation method
We introduce XBrainLab, an open-source user-friendly software, for accelerated interpretation of neural patterns from EEG data based on cutting-edge computational approach.
[TMLR] "Can You Win Everything with Lottery Ticket?" by Tianlong Chen, Zhenyu Zhang, Jun Wu, Randy Huang, Sijia Liu, Shiyu Chang, Zhangyang Wang
Comprehensible Convolutional Neural Networks via Guided Concept Learning
MechaMap - Toolkit for Mechanistic Interpretability (MI) Research
Code for ER-Test, accepted to the Findings of EMNLP 2022
A framework for evaluating auto-interp pipelines, i.e., natural language explanations of neurons.
Pipeline em Python para classificação de trechos literários em três classes operacionais de complexidade cognitiva textual: baixa, média e alta, usando PLN, métricas linguísticas e aprendizado de máquina.
An AI-driven system for detecting dark patterns in user interfaces, enhanced with explainability to clearly interpret why a pattern is flagged. Designed to improve transparency, trust, and ethical design in digital products.
The official implementation of the paper "EXPL-FR: Explaining Face Recognition Models via Vision-Language Alignment", accepted at ECCV 2026 Workshops.
Machine learning system that detects payment fraud and explains each decision, with proper handling of class imbalance and data bias.
AI system that models psychological evolution over time - tracking emotional drift, cognitive fatigue & burnout risk using transformer NLP + temporal modeling. 🧠
Quantifying the robustness of XAI in financial fraud detection. A systematic verification framework for assessing the stability and fidelity of additive local explanations (SHAP/LIME) under controlled perturbations.
Hardware Acceleration of Explainable AI on Heterogeneous Multi-Accelerator Platforms
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