arXiv:2606.30384v1 Announce Type: new Abstract: Training in artificial neural networks can be viewed as a trajectory evolving through a high-dimensional loss landscape. However, the large number of trainable parameters makes the direct analysis of these dynamics challenging. In this work, we treat such training trajectories as temporal networks and apply recently proposed strategies for the scalar embedding of temporal networks. We investigate whether such a scalar embedding provides a meaningful low-dimensional representation of neural network training dynamics. Using a multilayer perceptron
The increasing complexity and scale of AI models necessitate more efficient methods to understand and optimize their training dynamics. This research offers a new analytical lens at a time when 'black box' AI development faces growing scrutiny.
This research provides fundamental insights into the training processes of neural networks, potentially leading to more stable, efficient, and explainable AI systems. A deeper understanding of training dynamics could unlock significant performance improvements and reduce computational waste.
The ability to represent complex neural network training trajectories as scalar embeddings changes how researchers can analyze, compare, and debug AI models. It offers a low-dimensional and interpretable view of high-dimensional processes.
- · AI researchers
- · Machine learning engineers
- · Developers of large language models
- · AI hardware manufacturers
More efficient and interpretable AI model development becomes possible due to quantifiable training dynamics.
This improved understanding could lead to new optimization algorithms or architectural designs that significantly reduce training times and energy consumption for AI.
Reduced compute requirements for AI training could alleviate pressure on energy grids and semiconductor manufacturing, impacting the broader compute supply chain.
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Read at arXiv cs.LG