Summary of Cronos: Enhancing Deep Learning with Scalable Gpu Accelerated Convex Neural Networks, by Miria Feng et al.
CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
by Miria Feng, Zachary Frangella, Mert Pilanci
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 The CRONOS algorithm for convex optimization of two-layer neural networks addresses the limitation of prior work being restricted to downsampled versions of MNIST and CIFAR-10. This novel approach enables scaling to high-dimensional datasets like ImageNet, which are ubiquitous in modern deep learning. Building upon CRONOS, the authors develop a new algorithm called CRONOS-AM, combining CRONOS with alternating minimization for training multi-layer networks with arbitrary architectures. Theoretical analysis proves CRONOS converges to the global minimum of the convex reformulation under mild assumptions. Experimental results using GPU acceleration in JAX demonstrate CRONOS-AM’s efficacy on vision and language tasks with benchmark datasets like ImageNet and IMDb, achieving comparable or better validation accuracy than state-of-the-art deep learning optimizers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CRONOS is a new way to train neural networks that lets them work with huge amounts of data. This is important because many real-world problems involve lots of data, such as recognizing objects in pictures. The algorithm starts by simplifying the problem and then uses a clever trick called alternating minimization to speed up the training process. The authors tested CRONOS on several big datasets and found that it works just as well or even better than other popular training methods. |
Keywords
* Artificial intelligence * Deep learning * Optimization
