Search Results for author:
Found 15 papers, 10 papers with code
Predicting the Performance of a Computing System with Deep Networks
Existing approaches to understanding the performance of hardware largely focus around benchmarking -- leveraging standardised workloads which seek to be representative of an end-user's needs.
Siamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets
Instead, we evaluate Siamese Neural Networks (SNNs), which not only allows us to classify images of skin lesions, but also allow us to identify those images which are different from the trained classes - allowing us to determine that an image is not an example of our training classes.
Analysis of Reinforcement Learning for determining task replication in workflows
We show, through simulation, that we can save 34% of the energy consumption using RL compared to a fixed number of replicas with only a 4% decrease in workflows achieving a pre-defined overhead bound.
Long-term Reproducibility for Neural Architecture Search
It is a sad reflection of modern academia that code is often ignored after publication -- there is no academic 'kudos' for bug fixes / maintenance.
SpiderNet: Hybrid Differentiable-Evolutionary Architecture Search via Train-Free Metrics
Neural Architecture Search (NAS) algorithms are intended to remove the burden of manual neural network design, and have shown to be capable of designing excellent models for a variety of well-known problems.
Application of deep learning to camera trap data for ecologists in planning / engineering -- Can captivity imagery train a model which generalises to the wild?
This is the first research which attempts to generate a training set based on captivity data and the first to explore the development of such models in the context of ecologists in planning/engineering.
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.
Rank over Class: The Untapped Potential of Ranking in Natural Language Processing
Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection.
Bonsai-Net: One-Shot Neural Architecture Search via Differentiable Pruners
One-shot Neural Architecture Search (NAS) aims to minimize the computational expense of discovering state-of-the-art models.
Ranked #39 on
Neural Architecture Search
on CIFAR-10
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
A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation
In this paper, we investigate the possibility of classifying the ransomware a system is infected with simply based on a screenshot of the splash screen or the ransom note captured using a consumer camera commonly found in any modern mobile device.
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text
In this work, we present a generic process flow for text recognition in scanned documents containing mixed handwritten and machine-printed text without the need to classify text in advance.
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.
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.
