98 citations · 339 across the 69 of their papers we have counts for
9 papers · 1 filter
The Weighted Tsetlin Machine: Compressed Representations with Weighted Clauses
Adrian Phoulady, Ole-Christoffer Granmo, Saeed Rahimi Gorji +1
The Tsetlin Machine (TM) is an interpretable mechanism for pattern recognition that constructs conjunctive clauses from data. The clauses capture frequent patterns with high discri…
A Tsetlin Machine with Multigranular Clauses
Saeed Rahimi Gorji, Ole-Christoffer Granmo, Adrian Phoulady +1
The recently introduced Tsetlin Machine (TM) has provided competitive pattern recognition accuracy in several benchmarks, however, requires a 3-dimensional hyperparameter search. I…
Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network
Jivitesh Sharma, Ole-Christoffer Granmo, Morten Goodwin
In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Netwo…
Towards Model-based Reinforcement Learning for Industry-near Environments
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Deep reinforcement learning has over the past few years shown great potential in learning near-optimal control in complex simulated environments with little visible information. Ra…
A Neural Turing~Machine for Conditional Transition Graph Modeling
Mehdi Ben Lazreg, Morten Goodwin, Ole-Christoffer Granmo
Graphs are an essential part of many machine learning problems such as analysis of parse trees, social networks, knowledge graphs, transportation systems, and molecular structures.…
The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Lei Jiao +1
The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive c…
A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Xuan Zhang +1
In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it i…
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
Jivitesh Sharma, Per-Arne Andersen, Ole-Chrisoffer Granmo +1
We focus on the important problem of emergency evacuation, which clearly could benefit from reinforcement learning that has been largely unaddressed. Emergency evacuation is a comp…
The Convolutional Tsetlin Machine
Ole-Christoffer Granmo, Sondre Glimsdal, Lei Jiao +3
Convolutional neural networks (CNNs) have obtained astounding successes for important pattern recognition tasks, but they suffer from high computational complexity and the lack of…