Search Results for author: Savitha Ramasamy

Found 13 papers, 3 papers with code

Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data

no code implementations8 Feb 2022 THEIVENDIRAM PRANAVAN, Terence Sim, ArulMurugan Ambikapathi, Savitha Ramasamy

Next, the latent representations for the succeeding instants obtained through non-linear transformations of these context vectors, are contrasted with the latent representations of the encoder for the multi-variables such that the density for the positive pair is maximized.

Anomaly Detection Time Series

Incremental Knowledge Tracing from Multiple Schools

no code implementations7 Jan 2022 Sujanya Suresh, Savitha Ramasamy, P. N. Suganthan, Cheryl Sze Yin Wong

Knowledge tracing is the task of predicting a learner's future performance based on the history of the learner's performance.

Continual Learning Knowledge Tracing

An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series

1 code implementation23 Sep 2021 Astha Garg, Wenyu Zhang, Jules Samaran, Savitha Ramasamy, Chuan-Sheng Foo

Several techniques for multivariate time series anomaly detection have been proposed recently, but a systematic comparison on a common set of datasets and metrics is lacking.

Anomaly Detection Time Series Anomaly Detection

Knowledge Capture and Replay for Continual Learning

no code implementations12 Dec 2020 Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Haytham Fayek, Savitha Ramasamy, ArulMurugan Ambikapathi

Deep neural networks have shown promise in several domains, and the learned data (task) specific information is implicitly stored in the network parameters.

Continual Learning Denoising +1

Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks

2 code implementations npj Computational Materials 2019 Felipe Oviedo, Zekun Ren, Shijing Sun, Charles Settens, Zhe Liu, Noor Titan Putri Hartono, Savitha Ramasamy, Brian L. DeCost, Siyu I. P. Tian, Giuseppe Romano, Aaron Gilad Kusne, Tonio Buonassisi

We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.

BIG-bench Machine Learning Data Augmentation +6

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

no code implementations21 Mar 2019 Abeegithan Jeyasothy, Savitha Ramasamy, Suresh Sundaram

The performance of SEF-M is evaluated against state-of-the-art spiking neural network learning algorithms on 10 benchmark datasets from UCI machine learning repository.

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

no code implementations15 Nov 2018 Leo Laugier, Daniil Bash, Jose Recatala, Hong Kuan Ng, Savitha Ramasamy, Chuan-Sheng Foo, Vijay R. Chandrasekhar, Kedar Hippalgaonkar

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

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

no code implementations24 Sep 2018 Mahardhika Pratama, Andri Ashfahani, Yew Soon Ong, Savitha Ramasamy, Edwin Lughofer

The generative learning phase of Autoencoder (AE) and its successor Denosing Autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabelled samples.

Denoising Incremental Learning +1

Online Deep Learning: Growing RBM on the fly

no code implementations6 Mar 2018 Savitha Ramasamy, Kanagasabai Rajaraman, Pavitra Krishnaswamy, Vijay Chandrasekhar

The online generative training begins with zero neurons in the hidden layer, adds and updates the neurons to adapt to statistics of streaming data in a single pass unsupervised manner, resulting in a feature representation best suited to the data.

General Classification online learning

Cannot find the paper you are looking for? You can Submit a new open access paper.