4 citations · 8 across the 4 of their papers we have counts for
7 papers
Aligned Weight Regularizers for Pruning Pretrained Neural Networks
James O' Neill, Sourav Dutta, Haytham Assem
While various avenues of research have been explored for iterative pruning, little is known what effect pruning has on zero-shot test performance and its potential implications on…
Deep Neural Compression Via Concurrent Pruning and Self-Distillation
James O' Neill, Sourav Dutta, Haytham Assem
Pruning aims to reduce the number of parameters while maintaining performance close to the original network. This work proposes a novel \emph{self-distillation} based pruning strat…
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…
-Neighbor Based Curriculum Sampling for Sequence Prediction
James O' Neill, Danushka Bollegala
Multi-step ahead prediction in language models is challenging due to the discrepancy between training and test time processes. At test time, a sequence predictor is required to mak…
Meta-Embedding as Auxiliary Task Regularization
James O' Neill, Danushka Bollegala
Word embeddings have been shown to benefit from ensambling several word embedding sources, often carried out using straightforward mathematical operations over the set of word vect…
Siamese Capsule Networks
James O' Neill
Capsule Networks have shown encouraging results on \textit{defacto} benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on ta…