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..
NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Authors: Randy Ardywibowo, Shahin Boluki, Xinyu Gong, Zhangyang Wang, Xiaoning Qian
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform multiple Oo D detection experiments and observe that our NADS performs favorably, with up to 57% improvement in accuracy compared to state-of-the-art methods among 15 different testing configurations. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA 2Department of Computer Science and Engineering, Texas A&M University, Col lege Station, Texas, USA. |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We applied our architecture search on five datasets: Celeb A (Liu et al.), CIFAR-10, CIFAR-100, (Krizhevsky et al., 2009), SVHN (Netzer et al., 2011), and MNIST (Le Cun). |
| Dataset Splits | No | We can gradually remove the continuous relaxation and sample discrete architectures by annealing the tem perature parameter τ, allowing us to perform architecture search without using a validation set. |
| Hardware Specification | Yes | With this setup, we found that we are able to identify neural architectures in less than 1 GPU day on an Nvidia RTX 2080 Ti graphics card. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | In all experiments, we used the Adam optimizer with a fixed learning rate of 1 10 5 with a batch size of 4 for 10000 iterations. We approximate the WAIC score using M = 4 architecture samples, and set the tempera ture parameter τ = 1.5. We then retrained each architecture for 150000 iterations using Adam with a learning rate of 1 10 5 . |