2 citations · 5 across the 22 of their papers we have counts for
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Neural Additive Experts: Context-Gated Experts for Controllable Model Additivity
Guangzhi Xiong, Sanchit Sinha, Aidong Zhang
The trade-off between interpretability and accuracy remains a core challenge in machine learning. Standard Generalized Additive Models (GAMs) offer clear feature attributions but a…
CASL: Concept-Aligned Sparse Latents for Interpreting Diffusion Models
Zhenghao He, Guangzhi Xiong, Boyang Wang +2
Internal activations of diffusion models encode rich semantic information, but interpreting such representations remains challenging. While Sparse Autoencoders (SAEs) have shown pr…
A Comprehensive Survey on the Risks and Limitations of Concept-based Models
Sanchit Sinha, Aidong Zhang
Concept-based Models are a class of inherently explainable networks that improve upon standard Deep Neural Networks by providing a rationale behind their predictions using human-un…
Structural Causality-based Generalizable Concept Discovery Models
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
The rising need for explainable deep neural network architectures has utilized semantic concepts as explainable units. Several approaches utilizing disentangled representation lear…
ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
Guangzhi Xiong, Sanchit Sinha, Aidong Zhang
Generalized additive models (GAMs) have long been a powerful white-box tool for the intelligible analysis of tabular data, revealing the influence of each feature on the model pred…
CoLiDR: Concept Learning using Aggregated Disentangled Representations
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
Interpretability of Deep Neural Networks using concept-based models offers a promising way to explain model behavior through human-understandable concepts. A parallel line of resea…