Saptarashmi Bandyopadhyay — accepted AI-conference papers

2026

ESAM++: Efficient Online 3D Perception on the Edge

Qin Liu, Lavisha Aggarwal, Saptarashmi Bandyopadhyay, Vikas Bahirwani, Marc Niethammer, Ehsan Adeli +1

CVPR 2026

Online 3D scene perception in real time is essential for robotics, AR/VR, and autonomous systems, particularly in edge computing scenarios where computational resources are limited and privacy is crucial. Recent state-of-the-art methods like EmbodiedSAM (ESAM) demonstrate the promise of online 3D pe

Cited by 0 Source ↗code ↗Scholar ↗

2024

JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

Alexander Rutherford, Benjamin Ellis, Matteo Gallici, Jonathan Cook, Andrei Lupu, Garðar Ingvarsson +15

NeurIPS 2024poster

Benchmarks are crucial in the development of machine learning algorithms, significantly influencing reinforcement learning (RL) research through the available environments. Traditionally, RL environments run on the CPU, which limits their scalability with the computational resources typically availa…

Cited by 20 Source ↗PDF ↗code ↗Scholar ↗

2024

You Make me Feel like a Natural Question: Training QA Systems on Transformed Trivia Questions

Tasnim Kabir, Yoo Yeon Sung, Saptarashmi Bandyopadhyay, Hao Zou, Abhranil Chandra, Jordan Lee Boyd-Graber

EMNLP 2024main

Training question-answering QA and information retrieval systems for web queries require large, expensive datasets that are difficult to annotate and time-consuming to gather. Moreover, while natural datasets of information-seeking questions are often prone to ambiguity or ill-formed, there are trov…

Cited by 0 Source ↗PDF ↗Scholar ↗