no code implementations • 19 Jan 2023 • K. Darshana Abeyrathna, Ahmed Abdulrahem Othman Abouzeid, Bimal Bhattarai, Charul Giri, Sondre Glimsdal, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Jivitesh Sharma, Svein Anders Tunheim, Xuan Zhang
This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size.
5 code implementations • 17 Aug 2021 • Sondre Glimsdal, Ole-Christoffer Granmo
While TM and CoTM accuracy is similar when using more than $1$K clauses per class, CoTM reaches peak accuracy $3\times$ faster on MNIST with $8$K clauses.
1 code implementation • 7 Apr 2020 • Saeed Rahimi Gorji, Ole-Christoffer Granmo, Sondre Glimsdal, Jonathan Edwards, Morten Goodwin
Instead we use a simple look-up table that indexes the clauses on the features that falsify them.
8 code implementations • arXiv 2019 • Ole-Christoffer Granmo, Sondre Glimsdal, Lei Jiao, Morten Goodwin, Christian W. Omlin, Geir Thore Berge
Whereas the TM categorizes an image by employing each clause once to the whole image, the CTM uses each clause as a convolution filter.
Ranked #14 on Image Classification on Fashion-MNIST
no code implementations • 5 Aug 2017 • Sondre Glimsdal, Ole-Christoffer Granmo
In this paper, we address a particularly intriguing variant of the multi-armed bandit problem, referred to as the {\it Stochastic Point Location (SPL) Problem}.
no code implementations • 11 Jul 2017 • Sondre Glimsdal, Ole-Christoffer Granmo
This problem is generally referred to as the Object Partitioning Problem (OPP) and is known to be NP-hard.