arXiv:2511.21466v3 Announce Type: replace Abstract: We study Consensus-Based Optimization (CBO) for two-layer neural network training. We compare the performance of CBO against Adam on two test cases and demonstrate how a hybrid approach, combining CBO with Adam, provides faster convergence than CBO. Additionally, in the context of multi-task learning, we recast CBO into a formulation that offers less memory overhead. The CBO method allows for a mean-field model formulation, which we couple with the mean-field model of the neural network. To this end, we first reformulate CBO within the optima
This research is part of an ongoing trend to improve training efficiency and memory usage for neural networks, directly addressing current computational challenges.
Improved optimization techniques for neural networks can lead to faster development cycles, lower computational costs, and enable more complex AI models.
The potential for more efficient and less memory-intensive training methods could broaden the accessibility and scalability of advanced AI architectures.
- · AI researchers
- · Cloud AI providers
- · Hardware manufacturers (indirect)
- · AI-driven industries
- · Inefficient optimization methods
- · Developers reliant on ad-hoc solutions
Faster and cheaper development of two-layer neural networks.
Increased adoption of more sophisticated deep learning models due to reduced computational barriers.
Acceleration of AI research and deployment across various sectors, potentially enabling new AI agent capabilities with less resource overhead.
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Read at arXiv cs.LG