853 citations · 1.3k across the 13 of their papers we have counts for
13 papers
Word2rate: training and evaluating multiple word embeddings as statistical transitions
Gary Phua, Shaowei Lin, Dario Poletti
Using pretrained word embeddings has been shown to be a very effective way in improving the performance of natural language processing tasks. In fact almost any natural language ta…
Biologically Plausible Sequence Learning with Spiking Neural Networks
Zuozhu Liu, Thiparat Chotibut, Christopher Hillar +1
Motivated by the celebrated discrete-time model of nervous activity outlined by McCulloch and Pitts in 1943, we propose a novel continuous-time model, the McCulloch-Pitts network (…
Variational Probability Flow for Biologically Plausible Training of Deep Neural Networks
Zuozhu Liu, Tony Q. S. Quek, Shaowei Lin
The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the ho…
Fast Adaptation of Activity Sensing Policies in Mobile Devices
Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin +2
With the proliferation of sensors, such as accelerometers, in mobile devices, activity and motion tracking has become a viable technology to understand and create an engaging user…
Rate-distortion Balanced Data Compression for Wireless Sensor Networks
Mohammad Abu Alsheikh, Shaowei Lin, Dusit Niyato +1
This paper presents a data compression algorithm with error bound guarantee for wireless sensor networks (WSNs) using compressing neural networks. The proposed algorithm minimizes…
Deep Activity Recognition Models with Triaxial Accelerometers
Mohammad Abu Alsheikh, Ahmed Selim, Dusit Niyato +3
Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor…