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.
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
Authors: Rui Lin, Jie Ran, King Hung Chiu, Graziano Chesi, Ngai Wong
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,951 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,738 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 test the proposed De But chains on Le Net (Table 1, left), VGG-16-BN (Table 1, right) and Res Net50 [11] using the standard MNIST [21], CIFAR-10 [17] and Image Net [8] datasets, respectively. The results are presented in the subsections below. |
| Researcher Affiliation | Collaboration | 1 Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong Emial Address: EMAIL 2 United Microelectronics Centre (Hong Kong) Limited, Hong Kong Science Park, N.T., Hong Kong Emial Address: EMAIL |
| Pseudocode | No | The paper describes its algorithms and processes using prose and mathematical equations but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The codes and Appendix are publicly available at: https://github.com/ruilin0212/De But. |
| Open Datasets | Yes | We test the proposed De But chains on Le Net (Table 1, left), VGG-16-BN (Table 1, right) and Res Net50 [11] using the standard MNIST [21], CIFAR-10 [17] and Image Net [8] datasets, respectively. |
| Dataset Splits | Yes | We test the proposed De But chains on Le Net (Table 1, left), VGG-16-BN (Table 1, right) and Res Net50 [11] using the standard MNIST [21], CIFAR-10 [17] and Image Net [8] datasets, respectively. |
| Hardware Specification | Yes | All coding is done with Py Torch, and experiments run on an NVIDIA Ge Force GTX1080 Ti Graphics Card with 11GB frame buffer. |
| Software Dependencies | No | The paper mentions "Py Torch" as the coding framework but does not specify a version number for it or any other software dependency. |
| Experiment Setup | Yes | On MNIST and CIFAR10 datasets, the learning rate is 0.01 with a decaying step of 50, the batch size and the number of epochs are set to 64 and 150, respectively. As for Image Net training, the decaying step is 30 and the training warms up in the ο¬rst 5 epochs. The batch size and the number of epochs are 128 and 100, respectively. We use the standard stochastic gradient descent (SGD) for ο¬ne-tuning. |