no code implementations • 16 Dec 2023 • Stefan Kolek, Aditya Chattopadhyay, Kwan Ho Ryan Chan, Hector Andrade-Loarca, Gitta Kutyniok, Réne Vidal
To solve the optimization problem, we propose a new query dictionary learning algorithm inspired by classical sparse dictionary learning.
no code implementations • 24 Aug 2023 • Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele, Rene Vidal
Variational Information Pursuit (V-IP) is a framework for making interpretable predictions by design by sequentially selecting a short chain of task-relevant, user-defined and interpretable queries about the data that are most informative for the task.
1 code implementation • 6 Feb 2023 • Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D. Haeffele, Donald Geman, René Vidal
We then demonstrate that the IP strategy is the optimal solution to this problem.
1 code implementation • 3 Jul 2022 • Aditya Chattopadhyay, Stewart Slocum, Benjamin D. Haeffele, Rene Vidal, Donald Geman
There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms.
no code implementations • 11 Apr 2022 • Aditya Chattopadhyay, Xi Zhang, David Paul Wipf, Himanshu Arora, Rene Vidal
The architecture consists of a graph encoder that maps the input graph to a structured latent space, and a graph decoder that generates a furniture graph, given a latent code and the room graph.
no code implementations • 1 Jan 2021 • Aditya Chattopadhyay, Benjamin David Haeffele, Donald Geman, Rene Vidal
In this paper, we propose to measure the complexity of a learning task by the minimum expected number of questions that need to be answered to solve the task.
1 code implementation • 6 Feb 2019 • Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N. Balasubramanian
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such).
22 code implementations • 30 Oct 2017 • Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader, Vineeth N. Balasubramanian
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.