7.7k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Harish-Chandra Research InstituteIN27 papers
- International Institute of Information TechnologyIN23 papers
- Homi Bhabha National InstituteIN20 papers
- Indian Institute of Science Education and Research ThiruvananthapuramIN20 papers
- Indian Institute of Science BangaloreIN15 papers
- Indian Institute of Technology HyderabadIN14 papers
- Institute of Physics, BhubaneshwarIN11 papers
- Birla Institute of Technology and Science, PilaniIN10 papers
- National University of SingaporeSG9 papers
- Indian Institute of Technology KanpurIN8 papers
- Université Libre de BruxellesBE8 papers
- Birla Institute of Technology and Science - Hyderabad CampusIN7 papers
5 papers · 2 filters
Value-of-Information based Arbitration between Model-based and Model-free Control
Krishn Bera, Yash Mandilwar, Bapi Raju
There have been numerous attempts in explaining the general learning behaviours using model-based and model-free methods. While the model-based control is flexible yet computationa…
Generating Counterfactual and Contrastive Explanations using SHAP
Shubham Rathi
With the advent of GDPR, the domain of explainable AI and model interpretability has gained added impetus. Methods to extract and communicate visibility into decision-making models…
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces
Philipp Becker, Harit Pandya, Gregor Gebhardt +3
In order to integrate uncertainty estimates into deep time-series modelling, Kalman Filters (KFs) (Kalman et al., 1960) have been integrated with deep learning models, however, suc…
PLUME: Polyhedral Learning Using Mixture of Experts
Kulin Shah, P. S. Sastry, Naresh Manwani
In this paper, we propose a novel mixture of expert architecture for learning polyhedral classifiers. We learn the parameters of the classifierusing an expectation maximization alg…
Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar +1
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architectur…