9 citations · 14 across the 8 of their papers we have counts for
6 papers · 1 filter
Pseudorehearsal in actor-critic agents with neural network function approximation
Vladimir Marochko, Leonard Johard, Manuel Mazzara +1
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 ac…
Self-adaptive node-based PCA encodings
Leonard Johard, Victor Rivera, Manuel Mazzara +1
In this paper we propose an algorithm, Simple Hebbian PCA, and prove that it is able to calculate the principal component analysis (PCA) in a distributed fashion across nodes. It s…
Translating Event-B machines to Eiffel programs
Victor Rivera, JooYoung Lee, Manuel Mazzara +1
Formal modelling languages play a key role in the development of software since they enable users to prove correctness of system properties. However, there is still not a clear und…
Pseudorehearsal in actor-critic agents
Marochko Vladimir, Leonard Johard, Manuel Mazzara
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 i…
Initial steps towards assessing the usability of a verification tool
Mansur Khazeev, Victor Rivera, Manuel Mazzara +1
In this paper we report the experience of using AutoProof to statically verify a small object oriented program. We identified the problems that emerged by this activity and we clas…
Pseudorehearsal in value function approximation
Vladimir Marochko, Leonard Johard, Manuel Mazzara
Catastrophic forgetting is of special importance in reinforcement learning, as the data distribution is generally non-stationary over time. We study and compare several pseudorehea…