544 citations · 545 across the 4 of their papers we have counts for
6 papers
SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanation
Sehyun Lee, Dahee Kwon, Damin Lee +1
One of the pivotal recent challenges in neural network interpretability is polysemanticity, where a single neuron is activated by multiple, often unrelated concepts, hindering clea…
Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations
Dahee Kwon, Sehyun Lee, Jaesik Choi
Deep vision models have achieved remarkable classification performance by leveraging a hierarchical architecture in which human-interpretable concepts emerge through the compositio…
Pathwise Explanation of ReLU Neural Networks
Seongwoo Lim, Won Jo, Joohyung Lee +1
Neural networks have demonstrated a wide range of successes, but their ``black box" nature raises concerns about transparency and reliability. Previous research on ReLU networks ha…
Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information
Youngju Joung, Sehyun Lee, Jaesik Choi
To improve trust and transparency, it is crucial to be able to interpret the decisions of Deep Neural classifiers (DNNs). Instance-level examinations, such as attribution technique…
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
Luca Longo, Mario Brcic, Federico Cabitza +16
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In respo…
CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports
Ye Eun Chun, Sunjae Kwon, Kyunghwan Sohn +6
In this paper, we introduce CR-COPEC called Causal Rationale of Corporate Performance Changes from financial reports. This is a comprehensive large-scale domain-adaptation causal s…