Authors:
(1) Albert Go, Department of Automated Learning, Carnegie Mellon University;
(2) Tri Dao, Computer Science Department, Princeton University [email protected]and [email protected].
Links table
Abstract and 1. Introduction
2 State space models
3 selective status space models and 3.1 motivation: choice as a means of pressure
3.2 SSMS improvement with selection
3.3 The effective implementation of the selective SSMS
3.4 SSM brown
3.5 The properties of the selection mechanisms
3.6 additional form details
4 experimental evaluation and 4.1 artificial tasks
4.2 Language modeling
4.3 DNA modeling
4.4 Sound and Obstetrics
4.5 Speed and Memory Criteria
4.6 Podly inheritance
5 discussion
6 conclusion, thanks, appreciation and references
Discussion: the selection mechanism
B related work and B.1 S4 variables and derivatives
for
B.3 The relationship with RNNS
B.4 linear attention and long context models B.5
C selective SSMS mechanics
D, a perceived algorithm for devices for the selective SSMS
E. Experimental details, additional results and artificial tasks e.1
E.2 Language modeling
E.3 DNA modeling
E.4 sound details
E.5 The Standard of Efficiency
B.4 linear attention
Katharopoulos et al Many variables have suggested an alternative nucleus and other modifications. The random feature (RFA) (H. Peng et al The performer (Choromanski et al Transnormer (Qin, Han, W. Sun, D. Li, et al CosFormer (Qin, W. Sun, ET Al Written random attention (Zheng, C. Wang, and L
Aside from the attention of the nucleus, there are many other variables of effective attention; Survey Tai, Dahghani, Bahari, and others. (2022) offers a wide rating for many of these.
B.5 long context models
The long context has become a common topic, and many modern models have claimed that it expands a longer and longer sequence. However, this is often from a mathematical point of view and has not been widely verified. These include:
On the other hand, we believe that this work displays one of the first methods to show increasing performance with a longer context.
C selective SSMS mechanics
The estimate step size is
Where we note that the teacher can be considered a learning biases and folded in linear projection. Now the application of my suspended grade assessment (ZOH): ZoH:
Thus, the final separate repetition (2A) is
As desired.
