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Found 3 papers, 3 papers with code
Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings
Notably, our MoE variants, equipped with $N$ experts, achieve the efficacy of $N$ individual models, heralding a new era of versatile, One-Size-Fits-All transformer networks for various tasks.
Modeling natural language emergence with integral transform theory and reinforcement learning
Zipf's law predicts a power-law relationship between word rank and frequency in language communication systems and has been widely reported in a variety of natural language processing applications.
Polynomial Regression As an Alternative to Neural Nets
Despite the success of neural networks (NNs), there is still a concern among many over their "black box" nature.
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