Morten Goodwin

research

∙ 03/13/2023

Unsupervised Representation Learning in Partially Observable Atari Games

State representation learning aims to capture latent factors of an envir...

0 Li Meng, et al. ∙

research

∙ 01/02/2023

A contrastive learning approach for individual re-identification in a wild fish population

In both terrestrial and marine ecology, physical tagging is a frequently...

0 Ørjan Langøy Olsen, et al. ∙

research

∙ 12/20/2022

A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems

Recommendation Systems (RSs) are ubiquitous in modern society and are on...

0 Karl Audun Borgersen, et al. ∙

research

∙ 10/03/2022

CaiRL: A High-Performance Reinforcement Learning Environment Toolkit

This paper addresses the dire need for a platform that efficiently provi...

0 Per-Arne Andersen, et al. ∙

research

∙ 10/03/2022

CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning

Reinforcement Learning (RL) is a general framework concerned with an age...

0 Per-Arne Andersen, et al. ∙

research

∙ 10/03/2022

Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders

Deep Reinforcement Learning (RL) is unquestionably a robust framework to...

0 Per-Arne Andersen, et al. ∙

research

∙ 06/30/2022

Deep Reinforcement Learning with Swin Transformer

Transformers are neural network models that utilize multiple layers of s...

17 Li Meng, et al. ∙

research

∙ 03/23/2022

Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization

Recent social networks' misinformation mitigation approaches tend to inv...

0 Ahmed Abouzeid, et al. ∙

research

∙ 03/02/2022

Improving the Diversity of Bootstrapped DQN via Noisy Priors

Q-learning is one of the most well-known Reinforcement Learning algorith...

4 Li Meng, et al. ∙

research

∙ 09/29/2021

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

The deep learning revolution is touching all scientific disciplines and ...

6 Morten Goodwin, et al. ∙

research

∙ 07/22/2021

Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI

Prostate cancer (PCa) is the second most common cancer diagnosed among m...

13 Alvaro Fernandez-Quilez, et al. ∙

research

∙ 06/28/2021

Expert Q-learning: Deep Q-learning With State Values From Expert Examples

We propose a novel algorithm named Expert Q-learning. Expert Q-learning ...

13 Li Meng, et al. ∙

research

∙ 04/14/2021

Distributed Word Representation in Tsetlin Machine

Tsetlin Machine (TM) is an interpretable pattern recognition algorithm b...

0 Rohan Kumar Yadav, et al. ∙

research

∙ 03/27/2021

Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data

Whole gland (WG) segmentation of the prostate plays a crucial role in de...

0 Alvaro Fernandez-Quilez, et al. ∙

research

∙ 02/22/2021

A Relational Tsetlin Machine with Applications to Natural Language Understanding

TMs are a pattern recognition approach that uses finite state machines f...

0 Rupsa Saha, et al. ∙

research

∙ 09/10/2020

Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling

Using logical clauses to represent patterns, Tsetlin machines (TMs) have...

0 K. Darshana Abeyrathna, et al. ∙

research

∙ 07/28/2020

On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators

The Tsetlin Machine (TM) is a recent machine learning algorithm with sev...

0 Xuan Zhang, et al. ∙

research

∙ 07/04/2020

A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning

Due to the high energy consumption and scalability challenges of deep le...

0 K. Darshana Abeyrathna, et al. ∙

research

∙ 05/14/2020

Temperate Fish Detection and Classification: a Deep Learning based Approach

A wide range of applications in marine ecology extensively uses underwat...

22 Kristian Muri Knausgård, et al. ∙

research

∙ 05/11/2020

Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability

Despite significant effort, building models that are both interpretable ...

0 K. Darshana Abeyrathna, et al. ∙

research

∙ 04/07/2020

Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing

The Tsetlin Machine (TM) is a machine learning algorithm founded on the ...

0 Saeed Rahimi Gorji, et al. ∙

research

∙ 02/04/2020

A Regression Tsetlin Machine with Integer Weighted Clauses for Compact Pattern Representation

The Regression Tsetlin Machine (RTM) addresses the lack of interpretabil...

0 K. Darshana Abeyrathna, et al. ∙

research

∙ 09/16/2019

A Tsetlin Machine with Multigranular Clauses

The recently introduced Tsetlin Machine (TM) has provided competitive pa...

0 Saeed Rahimi Gorji, et al. ∙

research

∙ 08/28/2019

Environment Sound Classification using Multiple Feature Channels and Deep Convolutional Neural Networks

In this paper, we propose a model for the Environment Sound Classificati...

8 Jivitesh Sharma, et al. ∙

research

∙ 07/27/2019

Towards Model-based Reinforcement Learning for Industry-near Environments

Deep reinforcement learning has over the past few years shown great pote...

0 Per-Arne Andersen, et al. ∙

research

∙ 07/15/2019

A Neural Turing Machine for Conditional Transition Graph Modeling

Graphs are an essential part of many machine learning problems such as a...

9 Mehdi Ben Lazreg, et al. ∙

research

∙ 05/23/2019

The Convolutional Tsetlin Machine

Deep neural networks have obtained astounding successes for important pa...

0 Ole-Christoffer Granmo, et al. ∙

research

∙ 05/23/2019

Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment

We focus on the important problem of emergency evacuation, which clearly...

0 Jivitesh Sharma, et al. ∙

research

∙ 05/10/2019

The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems

The recently introduced Tsetlin Machine (TM) has provided competitive pa...

0 K. Darshana Abeyrathna, et al. ∙

research

∙ 05/10/2019

A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks

In this paper, we apply a new promising tool for pattern classification,...

0 K. Darshana Abeyrathna, et al. ∙

research

∙ 04/04/2019

Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation

Our understanding and ability to effectively monitor and manage coastal ...

0 Erlend Olsvik, et al. ∙

research

∙ 10/02/2018

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

Reinforcement learning has shown great potential in generalizing over ra...

10 Per-Arne Andersen, et al. ∙

research

∙ 09/12/2018

Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications

Medical applications challenge today's text categorization techniques by...

0 Geir Thore Berge, et al. ∙

research

∙ 08/15/2018

Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games

Reinforcement learning (RL) is an area of research that has blossomed tr...

2 Per-Arne Andersen, et al. ∙

research

∙ 07/16/2018

Combining a Context Aware Neural Network with a Denoising Autoencoder for Measuring String Similarities

Measuring similarities between strings is central for many established a...

0 Mehdi Ben Lazreg, et al. ∙

research

∙ 01/26/2018

FlashRL: A Reinforcement Learning Platform for Flash Games

Reinforcement Learning (RL) is a research area that has blossomed tremen...

0 Per-Arne Andersen, et al. ∙

research

∙ 12/17/2017

Towards a Deep Reinforcement Learning Approach for Tower Line Wars

There have been numerous breakthroughs with reinforcement learning in th...

0 Per-Arne Andersen, et al. ∙

research

∙ 06/23/2016

Adaptive Task Assignment in Online Learning Environments

With the increasing popularity of online learning, intelligent tutoring ...

1 Per-Arne Andersen, et al. ∙

Morten Goodwin

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