32 citations · 34 across the 8 of their papers we have counts for
9 papers
Priming: Hybrid State Space Models From Pre-trained Transformers
Aditya Chattopadhyay, Elvis Nunez, Prannay Kaul +6
Hybrid State-Space models combine Attention with recurrent State-Space Model (SSM) layers, balancing eidetic memory from Attention with compressed fading memory from SSMs. This yie…
Learning When to Attend: Conditional Memory Access for Long-Context LLMs
Sakshi Choudhary, Aditya Chattopadhyay, Luca Zancato +4
Language models struggle to generalize beyond pretraining context lengths, limiting long-horizon reasoning and retrieval. Continued pretraining on long-context data can help but is…
Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression
Liangzu Peng, Aditya Chattopadhyay, Luca Zancato +3
Linear State-Space Models (SSMs) offer an efficient alternative to softmax Attention with constant memory and linear compute, but their lossy, fading summary of the past hurts reca…
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit
Stefan Kolek, Aditya Chattopadhyay, Kwan Ho Ryan Chan +3
Information Pursuit (IP) is an explainable prediction algorithm that greedily selects a sequence of interpretable queries about the data in order of information gain, updating its…
Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions
Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele +1
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 inte…
Variational Information Pursuit for Interpretable Predictions
Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D. Haeffele +2
There is a growing interest in the machine learning community in developing predictive algorithms that are "interpretable by design". Towards this end, recent work proposes to make…
Interpretable by Design: Learning Predictors by Composing Interpretable Queries
Aditya Chattopadhyay, Stewart Slocum, Benjamin D. Haeffele +2
There is a growing concern about typically opaque decision-making with high-performance machine learning algorithms. Providing an explanation of the reasoning process in domain-spe…
Structured Graph Variational Autoencoders for Indoor Furniture layout Generation
Aditya Chattopadhyay, Xi Zhang, David Paul Wipf +2
We present a structured graph variational autoencoder for generating the layout of indoor 3D scenes. Given the room type (e.g., living room or library) and the room layout (e.g., r…
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…