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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Winning the Lottery with Continuous Sparsification
Authors: Pedro Savarese, Hugo Silva, Michael Maire
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that we surpass the state-of-the-art for both objectives, across models and datasets, including VGG trained on CIFAR-10 and Res Net-50 trained on Image Net. |
| Researcher Affiliation | Academia | Pedro Savarese TTI-Chicago EMAIL Hugo Silva University of Alberta EMAIL Michael Maire University of Chicago EMAIL |
| Pseudocode | Yes | Algorithm 1 Iterative Magnitude Pruning [19] Input: Pruning ratio τ, number of rounds R, iterations per round T, rewind point k... Algorithm 2 Continuous Sparsification Input: Mask init s(0), penalty λ, number of rounds R, iterations per round T, rewind point k |
| Open Source Code | Yes | Code available at https://github.com/lolemacs/continuous-sparsification |
| Open Datasets | Yes | VGG trained on CIFAR-10 [23] and Res Net-50 trained on Image Net [24]. |
| Dataset Splits | No | The paper describes training and testing procedures but does not explicitly mention or detail the use of a validation set for model tuning or selection. |
| Hardware Specification | No | The paper mentions using '4 GPUs' for experiments but does not specify the model or type of these GPUs, or any other specific hardware details. |
| Software Dependencies | No | The paper mentions using SGD as an optimizer but does not specify any software libraries (e.g., PyTorch, TensorFlow) or their version numbers that were used for implementation. |
| Experiment Setup | Yes | in each round, we train with SGD, a learning rate of 0.1, and a momentum of 0.9, for a total of 85 epochs, using a batch size of 64 for VGG and 128 for Res Net. We decay the learning rate by a factor of 10 at epochs 56 and 71, and utilize a weight decay of 0.0001. |