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..
Aligned Objective for Soft-Pseudo-Label Generation in Supervised Learning
Authors: Ning Xu, Yihao Hu, Congyu Qiao, Xin Geng
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the benchmark datasets validate the effectiveness of the proposed framework.4. Experiments |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing, China 2Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China. E-mail: EMAIL. |
| Pseudocode | Yes | Algorithm 1 SEAL Algorithm |
| Open Source Code | Yes | Source code is available at https://github.com/palm-ml/SEAL |
| Open Datasets | Yes | We employ three benchmark datasets for multi-class classification including CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009), and Tiny Image Net (Le & Yang, 2015), to evaluate the proposed approach. |
| Dataset Splits | Yes | We allocated 10% of the training data from each dataset for validation purposes. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like SGD optimizer and backbone networks (ResNet), but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | We configure the total number of epochs as 200 and set the batch size to 128. We employ the SGD optimizer with a momentum of 0.9 and a weight decay of 1e-4, where the initial learning rate is established at 0.1 with a decay factor of 10%. |