Vladimir Andreevich Marochko- Bachelor of Science
- PhD candidate at Technological University Dublin
Vladimir Andreevich Marochko
- Bachelor of Science
- PhD candidate at Technological University Dublin
About
10
Publications
1,298
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18
Citations
Introduction
Vladimir Andreevich Marochko joined PhD research team at the Artificial Intelligence and Cognitive load Research Lab, TU Dublin. Vladimir does research in data mining, artificial neural networks, and artificial intelligence. Their most recent publication is 'An application of Integrated Gradients for the analysis of the P3b event-related potential on a convolutional neural network trained with superlets.'
Skills and Expertise
Current institution
Publications
Publications (10)
Virtual reality (VR) is getting traction in many contexts, allowing users to have a real-life experience in a virtual world. However, its application in the field of Neuroscience, and above all probing newer activity with the analysis of electroencephalographic (EEG) event-related potentials (ERP) is underexplored. This article reviews the state-of...
Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found tha...
Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found tha...
Catastrophic forgetting has a significant negative impact in reinforcement learning. The purpose of this study is to investigate how pseudorehearsal can change performance of an actor-critic agent with neural-network function approximation. We tested agent in a pole balancing task and compared different pseudorehearsal approaches. We have found tha...
Catastrophic forgetting is of special importance in reinforcement learning, as the data distribution is generally non-stationary over time. We study and compare several pseudorehearsal approaches for Q-learning with function approximation in a pole balancing task. We have found that pseudorehearsal seems to assist learning even in such very simple...
Catastrophic forgetting has a serious impact in reinforcement learning, as the data distribution is generally sparse and non-stationary over time. The purpose of this study is to investigate whether pseudorehearsal can increase performance of an actor-critic agent with neural-network based policy selection and function approximation in a pole balan...
Catastrophic forgetting has a serious impact in reinforcement learning, as the data distribution is
generally sparse and non-stationary over time. The purpose of this study is to investigate whether
pseudorehearsal can increase performance of an actor-critic agent with neural-network based policy
selection and function approximation in a pole balan...
Catastrophic forgetting has a serious impact in reinforcement learning, as the data distribution is generally sparse and non-stationary over time. The purpose of this study is to investigate whether pseudorehearsal can increase performance of an actor-critic agent with neural-network based policy selection and function approximation in a pole balan...
Catastrophic forgetting is of special importance in reinforcement learning, as the data distribution is generally non-stationary over time. We study and compare several pseudorehearsal approaches for Q-learning with function approximation in a pole balancing task. We have found that pseudorehearsal seems to assist learning even in such very simple...
