Aditya Chattopadhyay

According to our database, Aditya Chattopadhyay authored at least 18 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Priming: Hybrid State Space Models From Pre-trained Transformers.

Aditya Chattopadhyay, Elvis Nunez, Prannay Kaul, Benjamin Bowman, Evan Becker, Luca Zancato, David Thomas, Wei Xia, Stefano Soatto

CoRR, May, 2026

Learning When to Attend: Conditional Memory Access for Long-Context LLMs.

Sakshi Choudhary, Aditya Chattopadhyay, Luca Zancato, Elvis Nunez, Matthew Trager, Wei Xia, Stefano Soatto

CoRR, March, 2026

2025

Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression.

Liangzu Peng, Aditya Chattopadhyay, Luca Zancato, Elvis Nunez, Wei Xia, Stefano Soatto

CoRR, November, 2025

Experience-Guided Adaptation of Inference-Time Reasoning Strategies.

Adam Stein, Matthew Trager, Benjamin Bowman, Michael Kleinman, Aditya Chattopadhyay, Wei Xia, Stefano Soatto

CoRR, November, 2025

LATTS: Locally Adaptive Test-Time Scaling.

Theo Uscidda, Matthew Trager, Michael Kleinman, Aditya Chattopadhyay, Wei Xia, Stefano Soatto

CoRR, September, 2025

InCoDe: Interpretable Compressed Descriptions For Image Generation.

Armand Comas Massague, Aditya Chattopadhyay, Feliu Formosa, Changyu Liu, Octavia I. Camps, René Vidal

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Learning Interpretable Queries for Explainable Image Classification with Information Pursuit.

Stefan Kolek, Aditya Chattopadhyay, Kwan Ho Ryan Chan, Héctor Andrade-Loarca, Gitta Kutyniok, René Vidal

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025

2024

PaCE: Parsimonious Concept Engineering for Large Language Models.

Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan, Darshan Thaker, Aditya Chattopadhyay, Chris Callison-Burch, René Vidal

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Performance Bounds for Active Binary Testing with Information Maximization.

Aditya Chattopadhyay, Benjamin David Haeffele, René Vidal, Donald Geman

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Bootstrapping Variational Information Pursuit with Large Language and Vision Models for Interpretable Image Classification.

Aditya Chattopadhyay, Kwan Ho Ryan Chan, René Vidal

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Interpretable by Design: Learning Predictors by Composing Interpretable Queries.

Aditya Chattopadhyay, Stewart Slocum, Benjamin D. Haeffele, René Vidal, Donald Geman

IEEE Trans. Pattern Anal. Mach. Intell., June, 2023

Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions.

Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele, René Vidal

CoRR, 2023

Learning Graph Variational Autoencoders with Constraints and Structured Priors for Conditional Indoor 3D Scene Generation.

Aditya Chattopadhyay, Xi Zhang, David Paul Wipf, Himanshu Arora, René Vidal

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Information Maximization Perspective of Orthogonal Matching Pursuit with Applications to Explainable AI.

Aditya Chattopadhyay, Ryan Pilgrim, René Vidal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Variational Information Pursuit for Interpretable Predictions.

Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin David Haeffele, Donald Geman, René Vidal

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Structured Graph Variational Autoencoders for Indoor Furniture layout Generation.

Aditya Chattopadhyay, Xi Zhang, David Paul Wipf, Himanshu Arora, René Vidal

CoRR, 2022

2019

Neural Network Attributions: A Causal Perspective.

Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N. Balasubramanian

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks.

Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader, Vineeth N. Balasubramanian

Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, 2018