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..
Understanding weight-magnitude hyperparameters in training binary networks
Authors: Joris Quist, Yunqiang Li, Jan van Gemert
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS We empirically validate our analysis on CIFAR-10, using the Bi Real Net-20 architecture (Liu et al., 2018). |
| Researcher Affiliation | Collaboration | Joris Quist1, Yunqiang Li1,2, Jan van Gemert1 1. Computer Vision Lab, Delft University of technology; 2. Axelera AI |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https: //github.com/jorisquist/Understanding-WM-HP-in-BNNs |
| Open Datasets | Yes | We empirically validate our analysis on CIFAR-10, using the Bi Real Net-20 architecture (Liu et al., 2018). and Imagenet: We follow Liu et al. (2021a): We train for 600K iterations with a batch size of 510. |
| Dataset Splits | No | The paper uses "Validation Accuracy (%)" in its figures but does not explicitly state the training, validation, or test dataset splits (e.g., percentages or sample counts) used for reproduction. It implies standard splits for CIFAR-10 and ImageNet but does not specify them. |
| Hardware Specification | Yes | We trained on 3 NVIDIA A40 GPUs, each A40 has 48 GB of GPU memory, with a batch size of 170 per GPU for as much as ten days. |
| Software Dependencies | No | The paper mentions "NVIDIA DALI dataloader" and "Py Torch dataloader" but does not specify any version numbers for these software components. |
| Experiment Setup | Yes | Unless mentioned otherwise the networks were optimized using SGD for both the real-valued and binary parameters with as hyperparameters: learning rate=0.1, momentum with γ = (1 0.9), weight decay=10 4, batch size=256 and cosine learning rate decay and cosine alpha decay. ... For our filtering-based optimizer we used an alpha of 10 3 with cosine decay and a gamma of 10 1. |