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09/28/2022
A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal
Online continual learning (OCL) aims to train neural networks incrementa...
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05/12/2022
Cross-domain Few-shot Meta-learning Using Stacking
Cross-domain few-shot meta-learning (CDFSML) addresses learning problems...
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12/03/2021
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents
Multi-label learning predicts a subset of labels from a given label set ...
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10/01/2021
Predicting COVID-19 Patient Shielding: A Comprehensive Study
There are many ways machine learning and big data analytics are used in ...
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09/02/2021
Semi-Supervised Learning using Siamese Networks
Neural networks have been successfully used as classification models yie...
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09/02/2021
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification
Deep neural networks produce state-of-the-art results when trained on a ...
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09/02/2021
Better Self-training for Image Classification through Self-supervision
Self-training is a simple semi-supervised learning approach: Unlabelled ...
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01/25/2021
PolyLM: Learning about Polysemy through Language Modeling
To avoid the "meaning conflation deficiency" of word embeddings, a numbe...
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10/30/2020
Machine Learning (In) Security: A Stream of Problems
Machine Learning (ML) has been widely applied to cybersecurity, and is c...
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05/15/2020
Adaptive XGBoost for Evolving Data Streams
Boosting is an ensemble method that combines base models in a sequential...
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03/29/2020
Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes
Machine learning-based multi-label medical text classifications can be u...
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12/26/2019
Classifier Chains: A Review and Perspectives
The family of methods collectively known as classifier chains has become...
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01/27/2019
Automatic end-to-end De-identification: Is high accuracy the only metric?
De-identification of electronic health records (EHR) is a vital step tow...
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01/23/2019
Stochastic Gradient Trees
We present an online algorithm that induces decision trees using gradien...
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10/16/2018
A survey of automatic de-identification of longitudinal clinical narratives
Use of medical data, also known as electronic health records, in researc...
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09/08/2018
On the Calibration of Nested Dichotomies for Large Multiclass Tasks
Nested dichotomies are used as a method of transforming a multiclass cla...
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09/08/2018
Ensembles of Nested Dichotomies with Multiple Subset Evaluation
A system of nested dichotomies is a method of decomposing a multi-class ...
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07/31/2018
Probability Calibration Trees
Obtaining accurate and well calibrated probability estimates from classi...
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04/16/2018
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes
Effective regularisation of neural networks is essential to combat overf...
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04/12/2018
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
We investigate the effect of explicitly enforcing the Lipschitz continui...
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04/07/2016
Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection
A system of nested dichotomies is a method of decomposing a multi-class ...
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11/19/2015
Fast Metric Learning For Deep Neural Networks
Similarity metrics are a core component of many information retrieval an...
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04/23/2015
Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams
In this research, we apply ensembles of Fourier encoded spectra to captu...
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10/19/2012