Oct 26, 2021 · Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers. Authors:Albert Gu, Isys Johnson, Karan Goel, ...
We introduce Linear State-Space Layers (LSSLs), a simple sequence-to-sequence transformation that shares the modeling advantages of recurrent, convolutional, ...
Nov 9, 2021 · We introduce a new continuous-time sequence model based on state spaces that has properties of recurrence and convolutions and performs very ...
Jun 10, 2024 · Empirically, stacking LSSL layers into a simple deep neural network obtains state-of-the-art results across time series benchmarks for long ...
We introduce Linear State-Space Layers (LSSLs), a simple sequence-to-sequence transformation that shares the modeling advantages of recurrent, convolutional, ...
Jan 14, 2022 · In our work, we simply use the state space as a black box representation in the spirit of deep learning, where we view an SSM as a function-to- ...
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A simple sequence model inspired by control systems that generalizes RNN heuristics, temporal convolutions, and neural differential equations while ...
We introduce a simple sequence model inspired by control systems that generalizes these approaches while addressing their shortcomings. The Linear State-Space ...
Empirically, stacking LSSL layers into a simple deep neural network obtains state-of-the-art results across time series benchmarks for long dependencies in ...
Jul 19, 2024 · Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers by Albert GU, Isys JOHNSON, Karan GOEL, Khaled ...
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