2 citations · 5 across the 23 of their papers we have counts for
5 papers · 1 filter
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…
MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning
Sanchit Sinha, Yuguang Yue, Victor Soto +3
Adapting large language models (LLMs) to unseen tasks with in-context training samples without fine-tuning remains an important research problem. To learn a robust LLM that adapts…
A Self-explaining Neural Architecture for Generalizable Concept Learning
Sanchit Sinha, Guangzhi Xiong, Aidong Zhang
With the wide proliferation of Deep Neural Networks in high-stake applications, there is a growing demand for explainability behind their decision-making process. Concept learning…