Search Results for author:
Found 8 papers, 3 papers with code
Using a Variational Autoencoder to Learn Valid Search Spaces of Safely Monitored Autonomous Robots for Last-Mile Delivery
We also show that when COIL has learned its latent representation, it can optimize 10% faster than the GA, making it a good choice for daily re-optimization of robots where delivery requests for each day are allocated to robots while maintaining safe numbers of robots running at once.
The Agent-based Modelling for Human Behaviour Special Issue
If human societies are so complex, then how can we hope to understand them?
Temporally Extended Successor Representations
We show that in environments with dynamic reward structure, t-SR is able to leverage both the flexibility of the successor representation and the abstraction afforded by temporally extended actions.
Kill Chaos with Kindness: Agreeableness Improves Team Performance Under Uncertainty
Our finding is that the dependency between team performance and Agreeableness is moderated by task uncertainty.
PiNet: Attention Pooling for Graph Classification
We propose PiNet, a generalised differentiable attention-based pooling mechanism for utilising graph convolution operations for graph level classification.
PiNet: A Permutation Invariant Graph Neural Network for Graph Classification
We propose an end-to-end deep learning learning model for graph classification and representation learning that is invariant to permutation of the nodes of the input graphs.
Ranked #60 on
Graph Classification
on PROTEINS
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations
Building on prior work combining explicit tensor representations with a standard image-based classifier, we propose a model to perform graph classification by extracting fixed size tensorial information from each graph in a given set, and using a Capsule Network to perform classification.
Ranked #14 on
Graph Classification
on PTC
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
