47 citations · 94 across the 21 of their papers we have counts for
22 papers
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…
A contrastive learning approach for individual re-identification in a wild fish population
Ørjan Langøy Olsen, Tonje Knutsen Sørdalen, Morten Goodwin +3
In both terrestrial and marine ecology, physical tagging is a frequently used method to study population dynamics and behavior. However, such tagging techniques are increasingly be…
A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems
Karl Audun Borgersen, Morten Goodwin, Jivitesh Sharma
Recommendation Systems (RSs) are ubiquitous in modern society and are one of the largest points of interaction between humans and AI. Modern RSs are often implemented using deep le…
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…
Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling
K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin +5
Using logical clauses to represent patterns, Tsetlin Machines (TMs) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning spee…
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…
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…
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…
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
Erlend Olsvik, Christian M. D. Trinh, Kristian Muri Knausgård +5
Our understanding and ability to effectively monitor and manage coastal ecosystems are severely limited by observation methods. Automatic recognition of species in natural environm…
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…
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in…
Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String Similarities
Mehdi Ben Lazreg, Morten Goodwin
Measuring similarities between strings is central for many established and fast growing research areas including information retrieval, biology, and natural language processing. Th…
FlashRL: A Reinforcement Learning Platform for Flash Games
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential in among others successfully playing computer game…
Towards a Deep Reinforcement Learning Approach for Tower Line Wars
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
There have been numerous breakthroughs with reinforcement learning in the recent years, perhaps most notably on Deep Reinforcement Learning successfully playing and winning relativ…
Adaptive Task Assignment in Online Learning Environments
Per-Arne Andersen, Christian Kråkevik, Morten Goodwin +1
With the increasing popularity of online learning, intelligent tutoring systems are regaining increased attention. In this paper, we introduce adaptive algorithms for personalized…