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..
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Authors: Qian Lou, Song Bian, Lei Jiang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed extensive experiments to show the consistent effectiveness of Auto Privacy to minimize the HPPNN inference latency with trivial loss of accuracy. |
| Researcher Affiliation | Academia | Qian Lou Indiana University Bloomington EMAIL Song Bian Kyoto University EMAIL Lei Jiang Indiana University Bloomington EMAIL |
| Pseudocode | No | The paper describes the approach, including the DDPG agent, in textual form but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a specific link or explicit statement indicating that the source code for the described methodology (Auto Privacy) is publicly available. |
| Open Datasets | Yes | Our experiments are performed on the CIFAR-10/100 dataset. |
| Dataset Splits | No | The paper states 'Only 7CNET is trained and tested on CIFAR-10, while experiments of RESNET and MOBNET are performed on CIFAR-100', but it does not provide specific percentages, sample counts, or detailed methodology for dataset splits (training, validation, test). |
| Hardware Specification | Yes | We ran HPPNN inferences and measured the latency of each type of operations on an Intel Xeon E7-4850 CPU with 1TB DRAM. We implemented and trained Auto Privacy on a Nvidia GTX1080-Ti GPU. |
| Software Dependencies | No | The paper mentions using 'Microsoft SEAL library [6]' and 'swanky library [23]' but does not specify their version numbers. |
| Experiment Setup | Yes | The DDPG agent is trained with fixed learning rates, i.e., 10 4 for the actor network and 10 3 for the critic network. The replay buffer size of Auto Privacy is 2000. During exploration, the DDPG agent adds a random noise to each action. The standard deviation of Gaussian action noise is initially set to 0.5. After each episode, the noise is decayed exponentially with a decay rate of 0.99. |