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..
Learning Instance-wise Sparsity for Accelerating Deep Models
Authors: Chuanjian Liu, Yunhe Wang, Kai Han, Chunjing Xu, Chang Xu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on benchmark datasets and networks demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Collaboration | 1Huawei Noah s Ark Lab 2School of Computer Science, FEIT, University of Sydney, Australia |
| Pseudocode | No | The paper does not contain STRUCTURED PSEUDOCODE OR ALGORITHM BLOCKS (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide CONCRETE ACCESS TO SOURCE CODE (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We extensively evaluate our methods on two popular classification datasets: CIFAR-10 [Krizhevsky, 2009] and Imagenet(ILSVRC2012) [Deng et al., 2009]. |
| Dataset Splits | No | The paper uses well-known datasets (CIFAR-10, ImageNet) and mentions 'CIFAR-10 test set', but does not explicitly provide specific percentages, sample counts, or citations for training, validation, and test splits needed for full reproducibility of the data partitioning, specifically for a validation set. |
| Hardware Specification | No | The paper does not provide SPECIFIC HARDWARE DETAILS (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide SPECIFIC ANCILLARY SOFTWARE DETAILS (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | A number of ℓ2,1-norm regularization factors are considered, λ = 0, 1e-6, 1e-7, 1e-8 respectively. We set a global CV threshold as α... and set a drop threshold β [0, 2). |