Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Chanakya: Learning Runtime Decisions for Adaptive Real-Time Perception
Authors: Anurag Ghosh, Vaibhav Balloli, Akshay Nambi, Aditya Singh, Tanuja Ganu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We analyse and evaluate Chanakya through the following experiments. We employ s AP metric [6] that coherently evaluates real-time perception, combining latency and accuracy into a single metric. |
| Researcher Affiliation | Collaboration | Anurag Ghosh1 Vaibhav Balloli2 Akshay Nambi3 Aditya Singh3 Tanuja Ganu3 1Carnegie Mellon University 2University of Michigan 3Microsoft Research India |
| Pseudocode | Yes | Algorithm 1: Obtaining Observations From Streaming Perception System |
| Open Source Code | Yes | Code can be viewed at https://github.com/microsoft/chanakya. |
| Open Datasets | Yes | All the results are reported on Argoverse-HD, unless stated otherwise. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | For a real-time perception system on a given hardware (P40 GPU),... Consider the task of migrating a streaming perception system from a device employing P40 GPU, to newer V100 GPU. |
| Software Dependencies | No | The paper mentions various models and frameworks but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We optimize configurations across two decision dimensions D = {Ds : {360, 480, 540, 640, 720}, Dnp : {100, 300, 500, 1000}}, i.e., detector scale and number of proposals. |