7 citations · 12 across the 5 of their papers we have counts for
5 papers
Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks
Shiyu Liu, Rohan Ghosh, John Tan Chong Min +1
The importance of learning rate (LR) schedules on network pruning has been observed in a few recent works. As an example, Frankle and Carbin (2019) highlighted that winning tickets…
Improving Mutual Information based Feature Selection by Boosting Unique Relevance
Shiyu Liu, Mehul Motani
Mutual Information (MI) based feature selection makes use of MI to evaluate each feature and eventually shortlists a relevant feature subset, in order to address issues associated…
AP: Selective Activation for De-sparsifying Pruned Neural Networks
Shiyu Liu, Rohan Ghosh, Dylan Tan +1
The rectified linear unit (ReLU) is a highly successful activation function in neural networks as it allows networks to easily obtain sparse representations, which reduces overfitt…
Long-range Prediction of Vital Signs Using Generative Boosting via LSTM Networks
Shiyu Liu, Mehul Motani
Vital signs including heart rate, respiratory rate, body temperature and blood pressure, are critical in the clinical decision making process. Effective early prediction of vital s…
Feature Selection Based on Unique Relevant Information for Health Data
Shiyu Liu, Mehul Motani
Feature selection, which searches for the most representative features in observed data, is critical for health data analysis. Unlike feature extraction, such as PCA and autoencode…