Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning
Authors: Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted over both synthetic and real data and comparison with competitive AL methods demonstrate the effectiveness of the proposed ADL model. |
| Researcher Affiliation | Academia | Rochester Institute of Technology1 University of Texas at Dallas2 {ws7586, qi.yu}@rit.edu1 {xujiang.zhao, feng.chen}@utdallas.edu2 |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an unambiguous statement about releasing source code or a direct link to a code repository. |
| Open Datasets | Yes | The real-world experiment is conducted on three datasets, MNIST, not MNIST, and CIFAR-10, all of which have ten classes. |
| Dataset Splits | No | The paper does not explicitly describe a validation dataset split. It mentions 'leaving 2-5 classes out for initial training' as part of the AL scenario setup, but not a distinct validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using '3-layer MLP with tanh' and 'Le Net with Relu' but does not specify software dependencies with version numbers (e.g., Python, PyTorch versions). |
| Experiment Setup | Yes | For synthetic data, we adopt a 3-layer MLP with tanh for activation. For real data, we use Le Net with Relu for activation. ... d is a fixed decay rate (set to 1/100K in our experiments). |