DeepAI

Universitetet Agder

∙ Kristiansand , Norway ∙ 0 followers

003222 The University of Agder, formerly known as Agder College and Agder University College, is a public university with campuses in Kristiansand and Grimstad, Norway.

Featured Researchers

Ole-Christoffer Granmo
45 publications
Morten Goodwin
37 publications
Lei Jiao
23 publications
Per-Arne Andersen
11 publications
Baltasar Beferull-Lozano
10 publications
Daniel Romero
9 publications
K. Darshana Abeyrathna
8 publications
Jivitesh Sharma
8 publications
Charl Maree
8 publications
Christian W. Omlin
7 publications
Bimal Bhattarai
6 publications

∙ 07/27/2020

Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data

Tsetlin Machines (TMs) capture patterns using conjunctive clauses in pro...

Christian D. Blakely, et al. ∙

∙ 10/02/2018

The Dreaming Variational Autoencoder for Reinforcement Learning Environments

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

Per-Arne Andersen, et al. ∙

∙ 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...

Jivitesh Sharma, et al. ∙

∙ 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 ...

Morten Goodwin, et al. ∙

∙ 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...

Per-Arne Andersen, et al. ∙

∙ 06/23/2016

Adaptive Task Assignment in Online Learning Environments

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

Per-Arne Andersen, et al. ∙

∙ 08/26/2022

Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models

The proliferation of artificial intelligence is increasingly dependent o...

Charl Maree, et al. ∙

∙ 11/05/2021

Transient Performance Modelling of 5G Slicing with Mixed Numerologies for Smart Grid Traffic

Network slicing enabled by fifth generation (5G) systems has the potenti...

H. V. Kalpanie Mendis, et al. ∙

∙ 11/11/2021

Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities

The past decade has seen significant progress in artificial intelligence...

Waddah Saeed, et al. ∙

∙ 12/22/2021

Safety assurance of an industrial robotic control system using hardware/software co-verification

As a general trend in industrial robotics, an increasing number of safet...

Yvonne Murray, et al. ∙

∙ 01/21/2022

Reinforcement Learning Your Way: Agent Characterization through Policy Regularization

The increased complexity of state-of-the-art reinforcement learning (RL)...

Charl Maree, et al. ∙

∙ 02/18/2022

Can Interpretable Reinforcement Learning Manage Assets Your Way?

Personalisation of products and services is fast becoming the driver of ...

Charl Maree, et al. ∙

∙ 03/08/2022

Logic-based AI for Interpretable Board Game Winner Prediction with Tsetlin Machine

Hex is a turn-based two-player connection game with a high branching fac...

Charul Giri, et al. ∙

∙ 03/23/2022

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

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

Ahmed Abouzeid, et al. ∙

∙ 04/20/2022

Reinforcement Learning with Intrinsic Affinity for Personalized Asset Management

The common purpose of applying reinforcement learning (RL) to asset mana...

Charl Maree, et al. ∙

∙ 01/02/2023

Tsetlin Machine Embedding: Representing Words Using Logical Expressions

Embedding words in vector space is a fundamental first step in state-of-...

Bimal Bhattarai, et al. ∙

∙ 08/30/2022

Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learning

Machine ethics has received increasing attention over the past few years...

Ajay Vishwanath, et al. ∙

∙ 10/03/2022

Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders

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

Per-Arne Andersen, et al. ∙

∙ 10/03/2022

CaiRL: A High-Performance Reinforcement Learning Environment Toolkit

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

Per-Arne Andersen, et al. ∙

∙ 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...

Per-Arne Andersen, et al. ∙

∙ 11/08/2022

Spoofing Attack Detection in the Physical Layer with Commutative Neural Networks

In a spoofing attack, an attacker impersonates a legitimate user to acce...

Daniel Romero, et al. ∙

∙ 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...

Karl Audun Borgersen, et al. ∙

∙ 12/27/2022

On the Equivalence of the Weighted Tsetlin Machine and the Perceptron

Tsetlin Machine (TM) has been gaining popularity as an inherently interp...

Jivitesh Sharma, et al. ∙

∙ 11/25/2017

Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering

Inference of space-time varying signals on graphs emerges naturally in a...

Vassilis N. Ioannidis, et al. ∙

∙ 07/27/2019

Towards Model-based Reinforcement Learning for Industry-near Environments

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

Per-Arne Andersen, et al. ∙

∙ 08/05/2017

Thompson Sampling Guided Stochastic Searching on the Line for Deceptive Environments with Applications to Root-Finding Problems

The multi-armed bandit problem forms the foundation for solving a wide r...

Sondre Glimsdal, et al. ∙

∙ 07/11/2017

An Optimal Bayesian Network Based Solution Scheme for the Constrained Stochastic On-line Equi-Partitioning Problem

A number of intriguing decision scenarios revolve around partitioning a ...

Sondre Glimsdal, et al. ∙

∙ 05/28/2017

Bayesian Unification of Gradient and Bandit-based Learning for Accelerated Global Optimisation

Bandit based optimisation has a remarkable advantage over gradient based...

Ole-Christoffer Granmo, et al. ∙

∙ 06/07/2016

Learning Power Spectrum Maps from Quantized Power Measurements

Power spectral density (PSD) maps providing the distribution of RF power...

Daniel Romero, et al. ∙

∙ 04/04/2018

The Tsetlin Machine - A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic

Although simple individually, artificial neurons provide state-of-the-ar...

Ole-Christoffer Granmo, et al. ∙

∙ 02/13/2019

Concurrent Computing with Shared Replicated Memory

The behavioural theory of concurrent systems states that any concurrent ...

Klaus-Dieter Schewe, et al. ∙

∙ 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,...

K. Darshana Abeyrathna, et al. ∙

∙ 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...

Jivitesh Sharma, et al. ∙

∙ 10/26/2021

Enhanced User Grouping and Pairing Schemes for CoMP NOMA based Cellular Networks

Non-orthogonal multiple access (NOMA) has been identified as one of the ...

Akhileswar Chowdary, et al. ∙

∙ 07/04/2019

Performance Evaluation of the LoRa Protocol in the context of Smart Meter

In recent years, the use of Low Power Wide Area Network (LPWAN) is incre...

Muhammad Nouman Rafi, et al. ∙

∙ 09/16/2019

A Tsetlin Machine with Multigranular Clauses

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

Saeed Rahimi Gorji, et al. ∙

∙ 04/24/2020

Accurate Graph Filtering in Wireless Sensor Networks

Wireless sensor networks (WSNs) are considered as a major technology ena...

Leila Ben Saad, et al. ∙

∙ 05/11/2020

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

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

K. Darshana Abeyrathna, et al. ∙

∙ 05/12/2020

An Exploratory Study of COVID-19 Misinformation on Twitter

Although a lot of correct and useful information is shared through chann...

Gautam Kishore Shahi, et al. ∙

∙ 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...

K. Darshana Abeyrathna, et al. ∙

∙ 09/24/2021

Discovering Novel Customer Features with Recurrent Neural Networks for Personality Based Financial Services

The micro-segmentation of customers in the finance sector is a non-trivi...

Charl Maree, et al. ∙