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..
Dynamic Sparsity Is Channel-Level Sparsity Learner
Authors: Lu Yin, Gen Li, Meng Fang, Li Shen, Tianjin Huang, Zhangyang "Atlas" Wang, Vlado Menkovski, Xiaolong Ma, Mykola Pechenizkiy, Shiwei Liu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that Chase achieves 1.7 inference throughput speedup on common GPU devices without compromising accuracy with Res Net-50 on Image Net. We release our codes in https://github.com/luuyin/chase. |
| Researcher Affiliation | Collaboration | 1Eindhoven University of Technology, 2Clemson University 3University of Liverpool, 4JD Explore Academy, 5University of Texas at Austin |
| Pseudocode | Yes | Algorithm 1: Pseudocode of Chase. Algorithm 2: Overview of Global Parameter Exploration |
| Open Source Code | Yes | We release our codes in https://github.com/luuyin/chase. |
| Open Datasets | Yes | Our evaluation is conducted with two widely used model architectures VGG-19 [48] and Res Net-50 [15] on across various datasets including CIFAR-10/100 and Image Net |
| Dataset Splits | Yes | Our evaluation is conducted with two widely used model architectures VGG-19 [48] and Res Net-50 [15] on across various datasets including CIFAR-10/100 and Image Net |
| Hardware Specification | Yes | All results are averaged from 100 individual runs with one NVIDIA 2080TI GPU in float32 on Py Torch. We set the batch size to 128 for CIFAR-100 and 2 for Image Net, when evaluating the latency. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify its version number. No other specific software dependencies with version numbers are provided. |
| Experiment Setup | Yes | Table 9: Implementation hyperparameters of Chase in Table 3, on CIFAR-10/100. Table 10: Implementation hyperparameters of Chase in Table 4, on Image Net. These tables detail hyperparameters such as τtotal (epochs), τstop (epochs), T (iterations), Tp (iterations), β, BS (batch size), LR (learning rate), LR Drop, Optimizer, Momentum, WD (weight decay), and Sparse Init. |