Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Understanding Instance-Level Label Noise: Disparate Impacts and Treatments
Authors: Yang Liu
ICML 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,090 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,825 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 simulate a 2D example: there are two classes of instances. The outer annulus represents one class and the inner ball is the other. Given the plotted training data, we train a 2-layer neural network using the cross-entropy (CE) loss. ... We further illustrate this in Figure 3 where we train a neural network on the CIFAR-10 dataset with synthesized noisy labels. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, University of California, Santa Cruz, CA, USA. Correspondence to: Yang Liu <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Figure 1 shows a collection of 10 similar Cats" from the CIFAR-10 dataset (Krizhevsky et al., 2009). ... Figure 3 where we train a neural network on the CIFAR-10 dataset with synthesized noisy labels. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts) for training, validation, or testing. |
| 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 does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | No | The paper mentions training a '2-layer neural network using the cross-entropy (CE) loss' but does not provide specific hyperparameters (e.g., learning rate, batch size, number of epochs) or detailed training configurations in the main text. |