Human Uncertainty Inference via Deterministic Ensemble Neural Networks
Authors: Yujin Cha, Sang Wan Lee5877-5886
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Results We verified the applicability of the PEN concept to simulations using several well-known datasets (Netzer et al. 2011; Xiao, Rasul, and Vollgraf 2017; Tschandl, Rosendahl, and Kittler 2018) to demonstrate that the PEN algorithm can infer the range and classification of the entropy reflecting the human visual decision uncertainty in real-world problems. As the applicability of the PEN was confirmed in our simulation experiment, a behavioral sampling experiment was conducted on human subjects. |
| Researcher Affiliation | Academia | Yujin Cha1, Sang Wan Lee1,2,3 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST) 2Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology (KAIST) 3Center for Neuroscience-inspired Artificial Intelligence, Korea Advanced Institute of Science and Technology (KAIST) {chayj, sangwan}@kaist.ac.kr |
| Pseudocode | No | The paper describes its methods using prose and mathematical equations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The dataset and details of experiments can be downloaded from : https://github.com/brain-machine-intelligence/PEN |
| Open Datasets | Yes | The experimental dataset was constructed by extracting images from the MIMIC-CXR dataset (Johnson et al. 2019a,b) and the Che Xpert dataset (Irvin et al. 2019). |
| Dataset Splits | Yes | We used a 5 fold cross-validation ensemble approach, wherein we cross-divided the training dataset into training and validation datasets, and independently trained a network. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., libraries, frameworks, or operating systems with their versions) that were used for the experiments. |
| Experiment Setup | Yes | The inference boundary constants used in Eq. (14) are summarized in Table 1. ... We used a 5 fold cross-validation ensemble approach, wherein we cross-divided the training dataset into training and validation datasets, and independently trained a network. |