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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Self-Distillation as Instance-Specific Label Smoothing
Authors: Zhilu Zhang, Mert Sabuncu
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,033 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,055 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results using multiple datasets and neural network architectures that, overall, demonstrate the utility of predictive diversity. |
| Researcher Affiliation | Academia | Zhilu Zhang Cornell University EMAIL Mert R. Sabuncu Cornell Univerisity EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments on CIFAR-100 [20], CUB-200 [37] and Tiny-imagenet [9] using Res Net [13] and Dense Net [16]. |
| Dataset Splits | Yes | 10% of the training data is split as the validation set. |
| Hardware Specification | No | The paper does not specify the exact hardware (e.g., GPU/CPU models or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software components or libraries. |
| Experiment Setup | Yes | We follow the original optimization configurations, and train the Res Net models for 150 epochs and Dese Net models for 200 epochs. ... We fix = 0.15 in label smoothing for all our experiments ... The hyper-parameter of Eq. 3 is taken to be 0.6 for self-distillation. |