方法证据记录
Time-MoE
Time-MoE is a billion-scale autoregressive foundation model for universal time-series forecasting, introduced by Shi et al. in 2024 and accepted at ICLR 2025. It combines a decoder-only transformer architecture with sparse Mixture-of-Experts (MoE) feed-forward layers, enabling the model to scale to billions of parameters while activating only a small subset of expert networks per token—dramatically increasing capacity without proportional compute cost.
源记录
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Time-MoE (Mixture-of-Experts Time-Series Foundation Model)
分类方法记录 · ml-model / deep-learning
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