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Closed-Form Expressions for Global and Local Interpretation of Tsetlin
  Machines with Applications to Explaining High-Dimensional Data

Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data

27 July 2020
Christopher D. Blakely
Ole-Christoffer Granmo
ArXivPDFHTML

Papers citing "Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data"

4 / 4 papers shown
Title
Self-timed Reinforcement Learning using Tsetlin Machine
Self-timed Reinforcement Learning using Tsetlin Machine
A. Wheeldon
Alex Yakovlev
R. Shafik
22
9
0
02 Sep 2021
On the Convergence of Tsetlin Machines for the XOR Operator
On the Convergence of Tsetlin Machines for the XOR Operator
Lei Jiao
Xuan Zhang
Ole-Christoffer Granmo
Kuruge Darshana Abeyrathna
36
30
0
07 Jan 2021
Interpretable Machine Learning with an Ensemble of Gradient Boosting
  Machines
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines
A. Konstantinov
Lev V. Utkin
FedML
AI4CE
10
138
0
14 Oct 2020
Massively Parallel and Asynchronous Tsetlin Machine Architecture
  Supporting Almost Constant-Time Scaling
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
Kuruge Darshana Abeyrathna
Bimal Bhattarai
Morten Goodwin
S. Gorji
Ole-Christoffer Granmo
Lei Jiao
Rupsa Saha
Rohan Kumar Yadav
LRM
19
36
0
10 Sep 2020
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