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Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?
We demonstrate that due to interactions between the temperature and other model parameters via some partial differential equations, the convergence rates of parameter estimations are slower than any polynomial rates, and could be as slow as $\mathcal{O}(1/\log(n))$, where $n$ denotes the sample size.
A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts
Mixture-of-experts (MoE) model incorporates the power of multiple submodels via gating functions to achieve greater performance in numerous regression and classification applications.
Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts
When the true number of experts $k_{\ast}$ is known, we demonstrate that the convergence rates of density and parameter estimations are both parametric on the sample size.
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