Summary of Understanding the Expressive Power and Mechanisms Of Transformer For Sequence Modeling, by Mingze Wang et al.
Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modelingby Mingze Wang, Weinan EFirst…
Understanding the Expressive Power and Mechanisms of Transformer for Sequence Modelingby Mingze Wang, Weinan EFirst…
Positional Encoding Helps Recurrent Neural Networks Handle a Large Vocabularyby Takashi MoritaFirst submitted to arxiv…
MouSi: Poly-Visual-Expert Vision-Language Modelsby Xiaoran Fan, Tao Ji, Changhao Jiang, Shuo Li, Senjie Jin, Sirui…
Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolationby Zhenyu He, Guhao…
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detectionby Seyed Amirhossein Najafi, Mohammad Hassan…
On Optimal Sampling for Learning SDF Using MLPs Equipped with Positional Encodingby Guying Lin, Lei…
Diagnostic Spatio-temporal Transformer with Faithful Encodingby Jokin Labaien, Tsuyoshi Idé, Pin-Yu Chen, Ekhi Zugasti, Xabier…