如此,S4的定义就出来了:序列的结构化状态空间——Structured State Space for Sequences,一类可以有效处理长序列的 SSM(S4所对应的论文为:Efficiently Modeling Long Sequences with Structured State Spaces) 参考博客: Albert Gu本人的scratch tuturial 很详细 csdn某大佬总结 论文: S4 HiPPO 本文使用 Zhihu On VSCod...
学习Mamba之前呢,不妨了解一下S4,他们都有一个共同的作者Albert Gu 。 State Space Model 首先,state space model可以定义成下式 x′(t)=Ax(t)+Bu(t)y(t)=Cx(t)+Du(t) 其中x是state vector, u为input,y为output,D视为0矩阵。 在文章中,作者利用bilinear method做discretization(涉及到解微分方程和一...
Here, we introduce a recent deep learning architecture, termed Structured StateSpace Sequence (S4) model, into de novo drug design. In addition to its unprecedented performance in various fields, S4 has shown remarkable capabilities to learn the global properties of sequences. This aspect is ...
Structured state space sequence models. Contribute to Sandy4321/s4-Long-Sequences-SSS development by creating an account on GitHub.
Structured State Spaces for Sequence Modeling This repository provides implementations and experiments for the following papers. S4D On the Parameterization and Initialization of Diagonal State Space Models Albert Gu, Ankit Gupta, Karan Goel, Christopher Ré ...
In this work, we show that one can match the performance of S4 even without the low rank correction and thus assuming the state matrices to be diagonal. Our Diagonal State SpaceDiagonal State Space (DSS) model matches the performance of S4 on Long Range Arena tasks, speech classification o...
We propose the Structured State Space sequence model (S4) based on a new parameterization for the SSM, and show that it can be computed much more efficiently than prior approaches while preserving their theoretical strengths. Our technique involves conditioning \( A \) with a low-rank ...
Nano-structured Solid-state Gas Sensors with Superior Performance (NANOS4)Project summary The main objective of NANOS4 is a breakthrough in advanced micro-and nano-technologies for developing innovative metal-oxide gas sensing systems based on mesoscopic sensors. The sensors will be fabricated by ...
Given that Telemedicine are widespread in China, and we have developed home enteral nutrition service with telemedicine (HENST) to satisfy HEN requirement of patients. However, only little is known about patient’s experience of HENST model. The objectiv
and then applied the trained model to process noisy SIM images of different signal levels. We demonstrated that a well-trained PRS-SIM model is applicable with a wide range of input SNRs, and significantly outperforms the conventional SIM reconstruction algorithm in all signal level conditions (Fig...