Search Results for author:
Found 5 papers, 2 papers with code
WeLa-VAE: Learning Alternative Disentangled Representations Using Weak Labels
Learning disentangled representations without supervision or inductive biases, often leads to non-interpretable or undesirable representations.
Unsupervised Severe Weather Detection Via Joint Representation Learning Over Textual and Weather Data
When observing a phenomenon, severe cases or anomalies are often characterised by deviation from the expected data distribution.
Learning Improved Representations by Transferring Incomplete Evidence Across Heterogeneous Tasks
Evidence transfer is a robust solution against external unknown categorical evidence that can introduce noise or uncertainty.
Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence
In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome.
ANNETT-O: An Ontology for Describing Artificial Neural Network Evaluation, Topology and Training
Deep learning models, while effective and versatile, are becoming increasingly complex, often including multiple overlapping networks of arbitrary depths, multiple objectives and non-intuitive training methodologies.
