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..
Lookahead: A Far-sighted Alternative of Magnitude-based Pruning
Authors: Sejun Park*, Jaeho Lee*, Sangwoo Mo, Jinwoo Shin
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that the proposed method consistently outperforms magnitude-based pruning on various networks, including VGG and Res Net, particularly in the high-sparsity regime. |
| Researcher Affiliation | Academia | KAIST EE KAIST AI EMAIL |
| Pseudocode | Yes | Algorithm 1 Lookahead Pruning (LAP) |
| Open Source Code | Yes | See https://github.com/alinlab/lookahead_pruning for codes. |
| Open Datasets | Yes | FCN is trained on MNIST dataset (Lecun et al., 1998), Conv-6, VGG, and Res Net are trained on CIFAR-10 dataset (Krizhevsky & Hinton, 2009), and VGG, Res Net, and WRN are trained on Tiny-Image Net. |
| Dataset Splits | No | The paper mentions 'test accuracy' and 'retraining phase' but does not explicitly provide information on training/test/validation splits with percentages, sample counts, or citations to predefined validation splits. |
| Hardware Specification | Yes | The computations have taken place on 40 CPUs of Intel Xeon E5-2630v4 @ 2.20GHz. ...additionally used a single NVidia Ge Force GTX1080 |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Back PACK' but does not provide specific version numbers for these software dependencies. It mentions 'Adam optimizer' but this is an algorithm, not a software dependency with a version. |
| Experiment Setup | Yes | We use Adam optimizer (Kingma & Ba, 2015) with batch size 60. We use a learning rate of 1.2 10 3 for FCN and 3 10 4 for all other models. For FCN, we use [50k, 50k] for the initial training phase and retraining phase. For Conv-6, we use [30k, 20k] steps. |