8 citations · 18 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…