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..
ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations
Authors: Rishabh Tiwari, Udbhav Bamba, Arnav Chavan, Deepak Gupta
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Chip Net outperforms state-of-the-art structured pruning methods by remarkable margins of up to 16.1% in terms of accuracy. |
| Researcher Affiliation | Collaboration | Transmute AI Research, The Netherlands Indian Institute of Technology, ISM Dhanbad, India Informatics Institute, University of Amsterdam, The Netherlands |
| Pseudocode | Yes | Algorithm 1: Chip Net Pruning Approach |
| Open Source Code | Yes | 1Code is publicly available at https://github.com/transmute AI/Chip Net |
| Open Datasets | Yes | For datasets, we have chosen CIFAR-10/100 (Krizhevsky, 2009) and Tiny Image Net (Wu et al.). |
| Dataset Splits | Yes | Finally, the model with best performance on the validation set is chosen for ο¬ne tuning. |
| Hardware Specification | Yes | All experiments were run on a Google Cloud Platform instance with a NVIDIA V100 GPU (16GB), 16 GB RAM and 4 core processor. |
| Software Dependencies | No | The paper mentions optimizers like 'Adam W' and 'SGD' but does not provide specific version numbers for these or any other software dependencies like deep learning frameworks or programming languages. |
| Experiment Setup | Yes | For the combined loss L in Eq. 1, weights Ξ±1 and Ξ±2 are set to 10 and 30, respectively, across all experiments. [...] WRN-26-12, Mobile Net V2, Res Net-50, Res Net-101, Res Net-110 were trained with batch size of 128 at initial learning rate of 5 10 2 using SGD optimizer with momentum 0.9 and weight decay 10 3. We use step learning rate strategy to decay learning rate by 0.5 after every 30 epochs. |