Search Results for author: George Fletcher

Found 8 papers, 2 papers with code

GNN Transformation Framework for Improving Efficiency and Scalability

1 code implementation25 Jul 2022 Seiji Maekawa, Yuya Sasaki, George Fletcher, Makoto Onizuka

We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs.

Survey on Fair Reinforcement Learning: Theory and Practice

no code implementations20 May 2022 Pratik Gajane, Akrati Saxena, Maryam Tavakol, George Fletcher, Mykola Pechenizkiy

In this article, we provide an extensive overview of fairness approaches that have been implemented via a reinforcement learning (RL) framework.

Decision Making Fairness +2

GGDs: Graph Generating Dependencies

1 code implementation21 Apr 2020 Larissa C. Shimomura, George Fletcher, Nikolay Yakovets

We propose Graph Generating Dependencies (GGDs), a new class of dependencies for property graphs.

Databases H.2

Novelty Producing Synaptic Plasticity

no code implementations10 Feb 2020 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy

A learning process with the plasticity property often requires reinforcement signals to guide the process.

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

no code implementations2 Apr 2019 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler, George Fletcher, Mykola Pechenizkiy

Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons.

Learning with Delayed Synaptic Plasticity

no code implementations22 Mar 2019 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy

Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i. e. rules that update synapses based on the neuron activations and reinforcement signals.

struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding

no code implementations25 May 2018 Yulong Pei, Xin Du, Jianpeng Zhang, George Fletcher, Mykola Pechenizkiy

Almost all previous methods represent a node into a point in space and focus on local structural information, i. e., neighborhood information.

Network Embedding

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