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09/05/2023
DeNISE: Deep Networks for Improved Segmentation Edges
This paper presents Deep Networks for Improved Segmentation Edges (DeNIS...
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08/30/2023
CorrEmbed: Evaluating Pre-trained Model Image Similarity Efficacy with a Novel Metric
Detecting visually similar images is a particularly useful attribute to ...
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05/17/2023
State Representation Learning Using an Unbalanced Atlas
The manifold hypothesis posits that high-dimensional data often lies on ...
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04/25/2023
Loss and Reward Weighing for increased learning in Distributed Reinforcement Learning
This paper introduces two learning schemes for distributed agents in Rei...
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03/26/2023
Contrastive Transformer: Contrastive Learning Scheme with Transformer innate Patches
This paper presents Contrastive Transformer, a contrastive learning sche...
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03/13/2023
Unsupervised Representation Learning in Partially Observable Atari Games
State representation learning aims to capture latent factors of an envir...
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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...
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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...
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10/03/2022
CaiRL: A High-Performance Reinforcement Learning Environment Toolkit
This paper addresses the dire need for a platform that efficiently provi...
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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...
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10/03/2022
Interpretable Option Discovery using Deep Q-Learning and Variational Autoencoders
Deep Reinforcement Learning (RL) is unquestionably a robust framework to...
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06/30/2022
Deep Reinforcement Learning with Swin Transformer
Transformers are neural network models that utilize multiple layers of s...
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03/23/2022
Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization
Recent social networks' misinformation mitigation approaches tend to inv...
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03/02/2022
Improving the Diversity of Bootstrapped DQN via Noisy Priors
Q-learning is one of the most well-known Reinforcement Learning algorith...
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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 ...
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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...
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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 ...
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04/14/2021
Distributed Word Representation in Tsetlin Machine
Tsetlin Machine (TM) is an interpretable pattern recognition algorithm b...
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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...
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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...
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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...
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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...
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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...
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05/14/2020
Temperate Fish Detection and Classification: a Deep Learning based Approach
A wide range of applications in marine ecology extensively uses underwat...
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05/11/2020
Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretability
Despite significant effort, building models that are both interpretable ...
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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 ...
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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...
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09/16/2019
A Tsetlin Machine with Multigranular Clauses
The recently introduced Tsetlin Machine (TM) has provided competitive pa...
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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...
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07/27/2019
Towards Model-based Reinforcement Learning for Industry-near Environments
Deep reinforcement learning has over the past few years shown great pote...
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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...
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05/23/2019
The Convolutional Tsetlin Machine
Deep neural networks have obtained astounding successes for important pa...
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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...
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05/10/2019
The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems
The recently introduced Tsetlin Machine (TM) has provided competitive pa...
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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,...
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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 ...
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10/02/2018
The Dreaming Variational Autoencoder for Reinforcement Learning Environments
Reinforcement learning has shown great potential in generalizing over ra...
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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...
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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...
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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...
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01/26/2018
FlashRL: A Reinforcement Learning Platform for Flash Games
Reinforcement Learning (RL) is a research area that has blossomed tremen...
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12/17/2017
Towards a Deep Reinforcement Learning Approach for Tower Line Wars
There have been numerous breakthroughs with reinforcement learning in th...
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06/23/2016