340 citations · 383 across the 11 of their papers we have counts for
12 papers
PaRT: Parallel Learning Towards Robust and Transparent AI
Mahsa Paknezhad, Hamsawardhini Rengarajan, Chenghao Yuan +4
This paper takes a parallel learning approach for robust and transparent AI. A deep neural network is trained in parallel on multiple tasks, where each task is trained only on a su…
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data
Theivendiram Pranavan, Terence Sim, Arulmurugan Ambikapathi +1
Anomaly detection in multi-variate time series (MVTS) data is a huge challenge as it requires simultaneous representation of long term temporal dependencies and correlations across…
Incremental Knowledge Tracing from Multiple Schools
Sujanya Suresh, Savitha Ramasamy, P. N. Suganthan +1
Knowledge tracing is the task of predicting a learner's future performance based on the history of the learner's performance. Current knowledge tracing models are built based on an…
Knowledge Capture and Replay for Continual Learning
Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Haytham Fayek +2
Deep neural networks have shown promise in several domains, and the learned data (task) specific information is implicitly stored in the network parameters. Extraction and utilizat…
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…
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…
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…
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…
Accelerating Photovoltaic Materials Development via High-Throughput Experiments and Machine-Learning-Assisted Diagnosis
Shijing Sun, Noor T. P. Hartono, Zekun D. Ren +15
Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique h…
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…
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…
Online Deep Learning: Growing RBM on the fly
Savitha Ramasamy, Kanagasabai Rajaraman, Pavitra Krishnaswamy +1
We propose a novel online learning algorithm for Restricted Boltzmann Machines (RBM), namely, the Online Generative Discriminative Restricted Boltzmann Machine (OGD-RBM), that prov…