47 citations · 94 across the 21 of their papers we have counts for
10 papers · 1 filter
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning
Martin Holen, Per-Arne Andersen, Kristian Muri Knausgård +1
This paper introduces two learning schemes for distributed agents in Reinforcement Learning (RL) environments, namely Reward-Weighted (R-Weighted) and Loss-Weighted (L-Weighted) gr…
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement Learning (RL) is a general framework concerned with an agent that seeks to maximize rewards in an environment. The learning typically happens through trial and error…
Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook
Morten Goodwin, Kim Tallaksen Halvorsen, Lei Jiao +7
The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These n…
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
Saeed Rahimi Gorji, Ole-Christoffer Granmo, Sondre Glimsdal +2
The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource…
A Regression Tsetlin Machine with Integer Weighted Clauses for Compact Pattern Representation
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Morten Goodwin
The Regression Tsetlin Machine (RTM) addresses the lack of interpretability impeding state-of-the-art nonlinear regression models. It does this by using conjunctive clauses in prop…
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…
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…
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…
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in th…