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.
Dual Lottery Ticket Hypothesis
Authors: Yue Bai, Huan Wang, ZHIQIANG TAO, Kunpeng Li, Yun Fu
ICLR 2022 | Venue PDF | LLM Run Details | Input Tokens: 22,191 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,329 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 | Extensive experiments on several public datasets and comparisons with competitive approaches validate our DLTH as well as the effectiveness of the proposed model RST. |
| Researcher Affiliation | Collaboration | 1Northeastern University, Boston, MA, USA 2Santa Clara University, Santa Clara, CA, USA 3Meta Research, Burlingame, CA, USA |
| Pseudocode | No | The paper describes the Random Sparse Network Transformation (RST) in text but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/yueb17/DLTH. |
| Open Datasets | Yes | Experiments are based on Res Net56/Res Net18 He et al. (2016) on CIFAR10/CIFAR100 Krizhevsky et al. (2009), and a Image Net subset Deng et al. (2009) to compare our method with Lottery Ticket Hypothesis (LTH) Frankle & Carbin (2018) and other strong baselines. |
| Dataset Splits | Yes | Experiments are based on Res Net56/Res Net18 He et al. (2016) on CIFAR10/CIFAR100 Krizhevsky et al. (2009), and a Image Net subset Deng et al. (2009). Total number of epochs is 200 with 0.1/0.01/0.001 learning rates starting at 0/100/150 epochs, respectively. |
| Hardware Specification | Yes | We use 4 NVIDIA Titan XP GPUs to perform our experimental evaluations. |
| Software Dependencies | No | The paper mentions optimization by SGD but does not specify software dependencies like Python, PyTorch, or CUDA with version numbers. |
| Experiment Setup | Yes | Experiments on CIFAR10/CIFAR100 are optimized by SGD with 0.9 momentum and 5e-4 weight decay using 128 batch size. Total number of epochs is 200 with 0.1/0.01/0.001 learning rates starting at 0/100/150 epochs, respectively. |