Summary of On Learnable Parameters Of Optimal and Suboptimal Deep Learning Models, by Ziwei Zheng et al.
On Learnable Parameters of Optimal and Suboptimal Deep Learning Models
by Ziwei Zheng, Huizhi Liang, Vaclav Snasel, Vito Latora, Panos Pardalos, Giuseppe Nicosia, Varun Ojha
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We scrutinize the structural and operational aspects of deep learning models to identify correlations between learnable parameter statistics and network performance. Our analysis spans datasets like MNIST, Fashion-MNIST, and CIFAR-10, as well as deep-learning architectures including DNNs, CNNs, and ViT. By examining the weight patterns of these models, we find that successful networks share similar converged weights statistics and distribution, regardless of dataset or architecture. In contrast, poor-performing networks exhibit varying weight patterns. Our findings highlight critical factors influencing the functionality and efficiency of deep neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well different types of artificial intelligence models perform. We compared lots of these models on various tasks, like recognizing pictures or letters. We found that successful models all share certain characteristics in their internal workings, while unsuccessful ones don’t. This helps us understand what makes a good AI model and why some do better than others. |
Keywords
» Artificial intelligence » Deep learning » Vit
