Songlin Yang
Songlin (松琳) is currently a second-year PhD student at MIT CSAIL, advised by Prof. Yoon Kim.
Previously, she obtained her bachelar’s degree from SUSTech in 2020 and her master’s degree from ShanghaiTech in 2023, where she was advised by Prof. Kewei Tu.
Her research is centered on the intersection of machine learning system and large language model, with a specific focus on the hardware-aware algorithm design for efficient sequence modeling.
news
Sep 25, 2024 | GSA and DeltaNet have been accepted to NeurIPS’24 |
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Aug 20, 2024 | Gave a talk at Stanford HazyResearch, “Linear Transformers for Efficient Sequence Modeling” |
Jun 10, 2024 | New arxiv “Parallelizing Linear Transformers with the Delta Rule over Sequence Length” with a very beautiful algorithm in it ! |
May 2, 2024 | Gated Linear Attention Transformers (GLA) is accepted to ICML 2024 Code is available at here. |
Apr 25, 2024 | Gave a talk at Cornell Tech, “Gated linear Recurrence for Efficient Sequence Modeling” |
Jan 1, 2024 | Introducing our open-source project flash-linear-attention . Join Discord if you are interested in linear attention/RNN! |