40 citations · 152 across the 36 of their papers we have counts for
6 papers · 1 filter
A Neighbourhood-Aware Differential Privacy Mechanism for Static Word Embeddings
Danushka Bollegala, Shuichi Otake, Tomoya Machide +1
We propose a Neighbourhood-Aware Differential Privacy (NADP) mechanism considering the neighbourhood of a word in a pretrained static word embedding space to determine the minimal…
Semantically-Conditioned Negative Samples for Efficient Contrastive Learning
James O' Neill, Danushka Bollegala
Negative sampling is a limiting factor w.r.t. the generalization of metric-learned neural networks. We show that uniform negative sampling provides little information about the cla…
Transfer Reward Learning for Policy Gradient-Based Text Generation
James O' Neill, Danushka Bollegala
Task-specific scores are often used to optimize for and evaluate the performance of conditional text generation systems. However, such scores are non-differentiable and cannot be u…
Error-Correcting Neural Sequence Prediction
James O' Neill, Danushka Bollegala
We propose a novel neural sequence prediction method based on \textit{error-correcting output codes} that avoids exact softmax normalization and allows for a tradeoff between speed…
Curriculum-Based Neighborhood Sampling For Sequence Prediction
James O' Neill, Danushka Bollegala
The task of multi-step ahead prediction in language models is challenging considering the discrepancy between training and testing. At test time, a language model is required to ma…
Dynamic Feature Scaling for Online Learning of Binary Classifiers
Danushka Bollegala
Scaling feature values is an important step in numerous machine learning tasks. Different features can have different value ranges and some form of a feature scaling is often requi…