2 citations · 2 across the 4 of their papers we have counts for
4 papers
Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning
Deepika SN Vemuri, Sayanta Adhikari, Ankit Saha +2
Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this by representing classes thr…
LogicCBMs: Logic-Enhanced Concept-Based Learning
Deepika SN Vemuri, Gautham Bellamkonda, Aditya Pola +1
Concept Bottleneck Models (CBMs) provide a basis for semantic abstractions within a neural network architecture. Such models have primarily been seen through the lens of interpreta…
Walking the Web of Concept-Class Relationships in Incrementally Trained Interpretable Models
Susmit Agrawal, Deepika Vemuri, Sri Siddarth Chakaravarthy P +1
Concept-based methods have emerged as a promising direction to develop interpretable neural networks in standard supervised settings. However, most works that study them in increme…
Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks
Tanmay Garg, Deepika Vemuri, Vineeth N Balasubramanian
This paper presents a novel concept learning framework for enhancing model interpretability and performance in visual classification tasks. Our approach appends an unsupervised exp…