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.
Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples
Authors: Jay Nandy, Wynne Hsu, Mong Li Lee
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,350 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,930 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 conduct two sets of experiments: First, we experiment on a synthetic dataset. Next, we present a comparative study on a range of image classification tasks. 1 2 |
| Researcher Affiliation | Academia | Jay Nandy Wynne Hsu Mong Li Lee National University of Singapore EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 2Code Link: https://github.com/jayjaynandy/maximize-representation-gap |
| Open Datasets | Yes | We conduct two sets of experiments... Next, we present a comparative study on a range of image classification tasks. ... We carry out experiments on CIFAR-10 and CIFAR-100 [28] and Tiny Image Net [29]... Image Net-25K is obtained by randomly selecting 25, 000 images from the Image Net dataset [30]. |
| Dataset Splits | No | The paper mentions training and test examples but does not explicitly provide details about a separate validation set or specific train/validation/test splits within the main text. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper mentions 'λin and λout are user-defined hyper-parameters' and refers to 'Appendix B.1 for additional details on experimental setup, hyper-parameters', but does not provide concrete hyperparameter values or detailed system-level training settings within the main text. |