Fusemoe: Mixture-of-experts transformers for fleximodal fusion
As machine learning models in critical fields increasingly grapple with multimodal data, they
face the dual challenges of handling a wide array of modalities, often incomplete due to …
face the dual challenges of handling a wide array of modalities, often incomplete due to …
Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts?
Dense-to-sparse gating mixture of experts (MoE) has recently become an effective
alternative to a well-known sparse MoE. Rather than fixing the number of activated experts …
alternative to a well-known sparse MoE. Rather than fixing the number of activated experts …
Bayesian likelihood free inference using mixtures of experts
We extend Bayesian Synthetic Likelihood (BSL) methods to non-Gaussian approximations
of the likelihood function. In this setting, we introduce Mixtures of Experts (MoEs), a class of …
of the likelihood function. In this setting, we introduce Mixtures of Experts (MoEs), a class of …
Bayesian nonparametric mixture of experts for inverse problems
Large classes of problems can be formulated as inverse problems, where the goal is to find
parameter values that best explain some observed measures. The relationship between …
parameter values that best explain some observed measures. The relationship between …
Bayesian Likelihood Free Inference using Mixtures of Experts
We extend Bayesian Synthetic Likelihood (BSL) methods to non-Gaussian approximations
of the likelihood function. In this setting, we introduce Mixtures of Experts (MoEs), a class of …
of the likelihood function. In this setting, we introduce Mixtures of Experts (MoEs), a class of …
Bayesian nonparametric mixture of experts for high-dimensional inverse problems
Large classes of problems can be formulated as inverse problems, where the goal is to find
parameter values that best explain some observed measures. The relationship between …
parameter values that best explain some observed measures. The relationship between …