Search Results for author:
Found 8 papers, 4 papers with code
On the Complexity of Object Detection on Real-world Public Transportation Images for Social Distancing Measurement
Thus, in this paper, we investigate the challenges of performing accurate social distance measurement on public transportation.
Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks
With the growing significance of graphs as an effective representation of data in numerous applications, efficient graph analysis using modern machine learning is receiving a growing level of attention.
Gradient descent with momentum --- to accelerate or to super-accelerate?
In this work, we show that the algorithm can be improved by extending this `acceleration' --- by using the gradient at an estimated position several steps ahead rather than just one step ahead.
Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions
Graphs have become a crucial way to represent large, complex and often temporal datasets across a wide range of scientific disciplines.
Social and Information Networks
Predicting the Computational Cost of Deep Learning Models
But, also, it has the ability to predict execution times for scenarios unseen in the training data.
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning
Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets.
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments
We demonstrate, through the use of simulation, how we can reduce this wasted energy by targeting tasks at computers less likely to be needed for primary use, predicting this idle time through machine learning.
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
To explore this, we present extensive experimental evaluation from five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features.
