38 citations · 81 across the 19 of their papers we have counts for
19 papers
SuperF: Neural Implicit Fields for Multi-Image Super-Resolution
Sander Riisøen Jyhne, Christian Igel, Morten Goodwin +3
High-resolution imagery is often hindered by limitations in sensor technology, atmospheric conditions, and costs. Such challenges occur in satellite remote sensing, but also with h…
MapAI: Precision in Building Segmentation
Sander Riisøen Jyhne, Morten Goodwin, Per Arne Andersen +5
MapAI: Precision in Building Segmentation is a competition arranged with the Norwegian Artificial Intelligence Research Consortium (NORA) in collaboration with Centre for Artificia…
Harnessing Attention Mechanisms: Efficient Sequence Reduction using Attention-based Autoencoders
Daniel Biermann, Fabrizio Palumbo, Morten Goodwin +1
Many machine learning models use the manipulation of dimensions as a driving force to enable models to identify and learn important features in data. In the case of sequential data…
State Representation Learning Using an Unbalanced Atlas
Li Meng, Morten Goodwin, Anis Yazidi +1
The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient repr…
Unsupervised Representation Learning in Partially Observable Atari Games
Li Meng, Morten Goodwin, Anis Yazidi +1
State representation learning aims to capture latent factors of an environment. Contrastive methods have performed better than generative models in previous state representation le…
A Contrastive Learning Scheme with Transformer Innate Patches
Sander Riisøen Jyhne, Per-Arne Andersen, Morten Goodwin
This paper presents Contrastive Transformer, a contrastive learning scheme using the Transformer innate patches. Contrastive Transformer enables existing contrastive learning techn…
CaiRL: A High-Performance Reinforcement Learning Environment Toolkit
Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
This paper addresses the dire need for a platform that efficiently provides a framework for running reinforcement learning (RL) experiments. We propose the CaiRL Environment Toolki…
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders
Per-Arne Andersen, Ole-Christoffer Granmo, Morten Goodwin
Deep Reinforcement Learning (RL) is unquestionably a robust framework to train autonomous agents in a wide variety of disciplines. However, traditional deep and shallow model-free…
Deep Reinforcement Learning with Swin Transformers
Li Meng, Morten Goodwin, Anis Yazidi +1
Transformers are neural network models that utilize multiple layers of self-attention heads and have exhibited enormous potential in natural language processing tasks. Meanwhile, t…
Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization
Ahmed Abouzeid, Ole-Christoffer Granmo, Christian Webersik +1
Recent social networks' misinformation mitigation approaches tend to investigate how to reduce misinformation by considering a whole-network statistical scale. However, unbalanced…
Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise
Li Meng, Morten Goodwin, Anis Yazidi +1
Q-learning is one of the most well-known Reinforcement Learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped Deep Q-…
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples
Li Meng, Anis Yazidi, Morten Goodwin +1
In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims at incorporating…
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
Rohan Kumar Yadav, Lei Jiao, Ole-Christoffer Granmo +1
Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based on propositional logic, which has demonstrated competitive performance in many Natural Language Process…
Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data
Alvaro Fernandez-Quilez, Steinar Valle Larsen, Morten Goodwin +3
Whole gland (WG) segmentation of the prostate plays a crucial role in detection, staging and treatment planning of prostate cancer (PCa). Despite promise shown by deep learning (DL…
A Relational Tsetlin Machine with Applications to Natural Language Understanding
Rupsa Saha, Ole-Christoffer Granmo, Vladimir I. Zadorozhny +1
TMs are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In addition to being natively interpretable, they…
A Novel Multi-Step Finite-State Automaton for Arbitrarily Deterministic Tsetlin Machine Learning
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Rishad Shafik +4
Due to the high energy consumption and scalability challenges of deep learning, there is a critical need to shift research focus towards dealing with energy consumption constraints…
On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators
Xuan Zhang, Lei Jiao, Ole-Christoffer Granmo +1
The Tsetlin Machine (TM) is a recent machine learning algorithm with several distinct properties, such as interpretability, simplicity, and hardware-friendliness. Although numerous…
Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability
K. Darshana Abeyrathna, Ole-Christoffer Granmo, Morten Goodwin
Despite significant effort, building models that are both interpretable and accurate is an unresolved challenge for many pattern recognition problems. In general, rule-based and li…
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applications
Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjørn Tveit +3
Medical applications challenge today's text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap ahead i…