most citedA novel method for extracting interpretable knowledge from a spiking neural classifier with time-varying synaptic weights

8 citations · 14 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG20192 cited

Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series

Yang Guo, Zhengyuan Liu, Pavitra Krishnswamy +1

Real-world clinical time series data sets exhibit a high prevalence of missing values. Hence, there is an increasing interest in missing data imputation. Traditional statistical ap…

cs.CL20194 cited

Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring

Zhengyuan Liu, Hazel Lim, Nur Farah Ain Binte Suhaimi +9

Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare. In this w…

cs.NE20198 cited

A novel method for extracting interpretable knowledge from a spiking neural classifier with time-varying synaptic weights

Abeegithan Jeyasothy, Suresh Sundaram, Savitha Ramasamy +1

This paper presents a novel method for information interpretability in an MC-SEFRON classifier. To develop a method to extract knowledge stored in a trained classifier, first, the…

cs.NE2019

Efficient single input-output layer spiking neural classifier with time-varying weight model

Abeegithan Jeyasothy, Savitha Ramasamy, Suresh Sundaram

This paper presents a supervised learning algorithm, namely, the Synaptic Efficacy Function with Meta-neuron based learning algorithm (SEF-M) for a spiking neural network with a ti…

physics.comp-ph2018

Predicting thermoelectric properties from crystal graphs and material descriptors - first application for functional materials

Leo Laugier, Daniil Bash, Jose Recatala +5

We introduce the use of Crystal Graph Convolutional Neural Networks (CGCNN), Fully Connected Neural Networks (FCNN) and XGBoost to predict thermoelectric properties. The dataset fo…

cs.LG2018

Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams

Mahardhika Pratama, Andri Ashfahani, Yew Soon Ong +2

The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples. None…