Search Results for author: Morten Goodwin

Found 48 papers, 24 papers with code

A Manifold Representation of the Key in Vision Transformers

no code implementations1 Feb 2024 Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad

The query, key, and value are often intertwined and generated within those blocks via a single, shared linear transformation.

Instance Segmentation object-detection +2

Harnessing Attention Mechanisms: Efficient Sequence Reduction using Attention-based Autoencoders

no code implementations23 Oct 2023 Daniel Biermann, Fabrizio Palumbo, Morten Goodwin, Ole-Christoffer Granmo

As far as we are aware, no model uses the sequence length reduction step as an additional opportunity to tune the models performance.

DeNISE: Deep Networks for Improved Segmentation Edges

no code implementations5 Sep 2023 Sander Riisøen Jyhne, Per-Arne Andersen, Morten Goodwin

This paper presents Deep Networks for Improved Segmentation Edges (DeNISE), a novel data enhancement technique using edge detection and segmentation models to improve the boundary quality of segmentation masks.

Edge Detection Segmentation

CorrEmbed: Evaluating Pre-trained Model Image Similarity Efficacy with a Novel Metric

1 code implementation30 Aug 2023 Karl Audun Kagnes Borgersen, Morten Goodwin, Jivitesh Sharma, Tobias Aasmoe, Mari Leonhardsen, Gro Herredsvela Rørvik

In this paper, we evaluate the viability of the image embeddings from numerous pre-trained computer vision models using a novel approach named CorrEmbed.

Attribute Image Similarity Search +1

State Representation Learning Using an Unbalanced Atlas

no code implementations17 May 2023 Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad

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 representations.

Dimensionality Reduction Representation Learning +1

Loss and Reward Weighing for increased learning in Distributed Reinforcement Learning

no code implementations25 Apr 2023 Martin Holen, Per-Arne Andersen, Kristian Muri Knausgård, Morten Goodwin

This paper introduces two learning schemes for distributed agents in Reinforcement Learning (RL) environments, namely Reward-Weighted (R-Weighted) and Loss-Weighted (L-Weighted) gradient merger.

reinforcement-learning Reinforcement Learning (RL)

A Contrastive Learning Scheme with Transformer Innate Patches

1 code implementation26 Mar 2023 Sander Riisøen Jyhne, Per-Arne Andersen, Morten Goodwin

Contrastive Transformer enables existing contrastive learning techniques, often used for image classification, to benefit dense downstream prediction tasks such as semantic segmentation.

Contrastive Learning Image Segmentation +2

Unsupervised Representation Learning in Partially Observable Atari Games

1 code implementation13 Mar 2023 Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad

Contrastive methods have performed better than generative models in previous state representation learning research.

Atari Games Representation Learning

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

no code implementations3 Oct 2022 Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo

There are two approaches, model-based and model-free reinforcement learning, that show concrete results in several disciplines.

reinforcement-learning Reinforcement Learning (RL)

CaiRL: A High-Performance Reinforcement Learning Environment Toolkit

1 code implementation3 Oct 2022 Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo

CaiRL also presents the first reinforcement learning toolkit with a built-in JVM and Flash support for running legacy flash games for reinforcement learning research.

OpenAI Gym reinforcement-learning +2

Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders

no code implementations3 Oct 2022 Per-Arne Andersen, Ole-Christoffer Granmo, Morten Goodwin

We show that the DVQN algorithm is a promising approach for identifying initiation and termination conditions for option-based reinforcement learning.

Q-Learning reinforcement-learning +1

Deep Reinforcement Learning with Swin Transformers

1 code implementation30 Jun 2022 Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad

Transformers are neural network models that utilize multiple layers of self-attention heads and have exhibited enormous potential in natural language processing tasks.

Atari Games reinforcement-learning +1

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

1 code implementation23 Mar 2022 Ahmed Abouzeid, Ole-Christoffer Granmo, Christian Webersik, Morten Goodwin

We further propose a generic misinformation mitigation algorithm that is robust to different social networks' misinformation statistics, allowing a promising impact in real-world scenarios.

Fairness Misinformation +1

improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise

no code implementations2 Mar 2022 Li Meng, Morten Goodwin, Anis Yazidi, Paal Engelstad

In this article, we further explore the possibility of replacing priors with noise and sample the noise from a Gaussian distribution to introduce more diversity into this algorithm.

Atari Games Q-Learning

MedAI: Transparency in Medical Image Segmentation

1 code implementation Nordic Machine Intelligence 2021 Steven Hicks, Debesh Jha, Vajira Thambawita, Pål Halvorsen, Bjørn-Jostein Singstad, Sachin Gaur, Klas Pettersen, Morten Goodwin, Sravanthi Parasa, Thomas de Lange, Michael Riegler

MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems.

Image Segmentation Medical Image Segmentation +2

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

no code implementations29 Sep 2021 Morten Goodwin, Kim Tallaksen Halvorsen, Lei Jiao, Kristian Muri Knausgård, Angela Helen Martin, Marta Moyano, Rebekah A. Oomen, Jeppe Have Rasmussen, Tonje Knutsen Sørdalen, Susanna Huneide Thorbjørnsen

We provide insight into popular deep learning approaches for ecological data analysis in plain language, focusing on the techniques of supervised learning with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of deep learning to marine ecology.

Management object-detection +1

Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples

no code implementations28 Jun 2021 Li Meng, Anis Yazidi, Morten Goodwin, Paal Engelstad

Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a combination of Double Q-learning and Dueling Q-learning.

Imitation Learning Q-Learning +2

A Relational Tsetlin Machine with Applications to Natural Language Understanding

5 code implementations22 Feb 2021 Rupsa Saha, Ole-Christoffer Granmo, Vladimir I. Zadorozhny, Morten Goodwin

TMs are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns.

Natural Language Understanding Question Answering

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

2 code implementations10 Sep 2020 K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan K. Yadav

We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy.

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

no code implementations28 Jul 2020 Xuan Zhang, Lei Jiao, Ole-Christoffer Granmo, Morten Goodwin

The analysis of the convergence of the two basic operators lays the foundation for analyzing other logical operators.

Operator learning

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

no code implementations4 Jul 2020 K. Darshana Abeyrathna, Ole-Christoffer Granmo, Rishad Shafik, Alex Yakovlev, Adrian Wheeldon, Jie Lei, Morten Goodwin

However, TMs rely heavily on energy-costly random number generation to stochastically guide a team of Tsetlin Automata to a Nash Equilibrium of the TM game.

BIG-bench Machine Learning

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

4 code implementations11 May 2020 K. Darshana Abeyrathna, Ole-Christoffer Granmo, Morten Goodwin

Here, we address the accuracy-interpretability challenge in machine learning by equipping the TM clauses with integer weights.

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

4 code implementations4 Feb 2020 K. Darshana Abeyrathna, Ole-Christoffer Granmo, Morten Goodwin

Although the RTM has solved non-linear regression problems with competitive accuracy, the resolution of the output is proportional to the number of clauses employed.

regression Unity

A Tsetlin Machine with Multigranular Clauses

4 code implementations16 Sep 2019 Saeed Rahimi Gorji, Ole-Christoffer Granmo, Adrian Phoulady, Morten Goodwin

The recently introduced Tsetlin Machine (TM) has provided competitive pattern recognition accuracy in several benchmarks, however, requires a 3-dimensional hyperparameter search.

Specificity

Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network

no code implementations28 Aug 2019 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 Network (CNN) with Attention mechanism.

Data Augmentation Environment Sound Classification +2

Towards Model-based Reinforcement Learning for Industry-near Environments

1 code implementation27 Jul 2019 Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo

If these environment dynamics are adequately learned, a model-based approach is perhaps the most sample efficient method for learning agents to act in an environment optimally.

Model-based Reinforcement Learning Q-Learning +2

A Neural Turing~Machine for Conditional Transition Graph Modeling

no code implementations15 Jul 2019 Mehdi Ben Lazreg, Morten Goodwin, Ole-Christoffer Granmo

However, learning the graph structure is often complex, particularly when the graph is cyclic, and the transitions from one node to another are conditioned such as graphs used to represent a finite state machine.

BIG-bench Machine Learning Information Retrieval +2

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

no code implementations23 May 2019 Jivitesh Sharma, Per-Arne Andersen, Ole-Chrisoffer Granmo, Morten Goodwin

We also propose a new reinforcement learning approach that entails pretraining the network weights of a DQN based agents to incorporate information on the shortest path to the exit.

OpenAI Gym Q-Learning +3

The Convolutional Tsetlin Machine

8 code implementations arXiv 2019 Ole-Christoffer Granmo, Sondre Glimsdal, Lei Jiao, Morten Goodwin, Christian W. Omlin, Geir Thore Berge

Whereas the TM categorizes an image by employing each clause once to the whole image, the CTM uses each clause as a convolution filter.

Image Classification

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

1 code implementation10 May 2019 K. Darshana Abeyrathna, Ole-Christoffer Granmo, Lei Jiao, Morten Goodwin

We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns; (2) mapping these patterns to a continuous output through a novel voting and normalization mechanism; and (3) employing a feedback scheme that updates the TM clauses to minimize the regression error.

General Classification regression

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

4 code implementations10 May 2019 K. Darshana Abeyrathna, Ole-Christoffer Granmo, Xuan Zhang, Morten Goodwin

In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting.

Disease Prediction

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

1 code implementation2 Oct 2018 Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo

It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms.

Management reinforcement-learning +1

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

1 code implementation12 Sep 2018 Geir Thore Berge, Ole-Christoffer Granmo, Tor Oddbjørn Tveit, Morten Goodwin, Lei Jiao, Bernt Viggo Matheussen

The Tsetlin Machine either performs on par with or outperforms all of the evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a non-public clinical dataset.

Natural Language Understanding Text Categorization

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

1 code implementation15 Aug 2018 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 computer games.

Reinforcement Learning (RL) Starcraft +1

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

1 code implementation16 Jul 2018 Mehdi Ben Lazreg, Morten Goodwin

This paper proposes a string metric that encompasses similarities between strings based on (1) the character similarities between the words including.

Denoising Information Retrieval +1

FlashRL: A Reinforcement Learning Platform for Flash Games

no code implementations26 Jan 2018 Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo

This paper introduces the Flash Reinforcement Learning platform (FlashRL) which attempts to fill this gap by providing an environment for thousands of Flash games on a novel platform for Flash automation.

reinforcement-learning Reinforcement Learning (RL)

Towards a Deep Reinforcement Learning Approach for Tower Line Wars

no code implementations17 Dec 2017 Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo

We propose a game environment in between Atari 2600 and Starcraft II, particularly targeting Deep Reinforcement Learning algorithm research.

Q-Learning reinforcement-learning +3

Adaptive Task Assignment in Online Learning Environments

no code implementations23 Jun 2016 Per-Arne Andersen, Christian Kråkevik, Morten Goodwin, Anis Yazidi

As main contribution of this paper, we propose a a novel Skill-Based Task Selector (SBTS) algorithm which is able to approximate a student's skill level based on his performance and consequently suggest adequate assignments.

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