[TSF] A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challenges
时序预测2025综述 A Comprehensive Survey of Deep Learning for … 阅读更多
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[TSF] LTSF-Linear (Are Transformers Effective for Time Series Forecasting?)
本文提出了DLinear，其通道独立策略启发了PatchTST的patch-patch注意力机制。 Are T … 阅读更多
[TSF] SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
Transformer系列时序预测改进模型——SageFormer SageFormer: Series-Aw … 阅读更多
[TSF] iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
Transformer系列时序预测改进模型——iTransformer iTransformer: Inver … 阅读更多
[TSF] PatchTST (A Time Series is Worth 64 Words: Long-term Forecasting with Transformers)
Transformer系列时序预测改进模型——PatchTST A Time Series is Worth … 阅读更多
[TSF] Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting
Transformer系列时序预测改进模型——Autoformer Autoformer: Decomposi … 阅读更多
[LLM] [TSF] 原版Transformer (Attention Is All You Need)
Vanilla Transformer Attention Is All You Need（2017） 相关内 … 阅读更多
[Phys] HPC与深度学习在核聚变研究中的融合创新
基于JET装置的CNN/RNN/VAE应用案例 D. R. Ferreira, “Using HPC infr … 阅读更多
[CV] PointNet、PointNet++网络结构详解&PyTorch实现
点云深度学习开山之作——PointNet、PointNet++。使用Python（PyTorch）实现物体分类 … 阅读更多