The content discusses the misconception of memory in artificial intelligence research, emphasizing that modeling memory is not about storage and retrieval but a mathematical construct enabling models to assimilate non-local influences. The research delves into using fractional Laplacians to quantify long-range dependencies within datasets, enhancing AI models’ capacity to model long-range interactions. By leveraging spectral representation and computational efficiency techniques like FFT and Chebyshev polynomial approximations, the research aims to optimize the application of fractional Laplacian operators in deep neural networks. The regularization effect of fractional Laplacians can prevent overfitting and improve generalization performance, but further research is needed to understand the optimal degree of regularization. Overall, the integration of fractional Laplacians into AI models has the potential to revolutionize modeling and understanding complex systems, leading to improved performance, interpretability, and generalization in artificial intelligence systems. The article invites readers to share their thoughts on this research.
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Source link: https://medium.com/autonomous-agents/rethinking-memory-in-ai-fractional-laplacians-and-long-range-interactions-3dd9c31fd10b?source=rss——artificial_intelligence-5
in AI Medium
AI Memory: Fractional Laplacians, Long-Range Interactions, Rethinking #MemoryAI
Preeti
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