Search Results for author: Varun Chandola

Found 16 papers, 3 papers with code

Large Deviations for Accelerating Neural Networks Training

no code implementations2 Mar 2023 Sreelekha Guggilam, Varun Chandola, Abani Patra

For this, we propose the LAD Improved Iterative Training (LIIT), a novel training approach for ANN using large deviations principle to generate and iteratively update training samples in a fast and efficient setting.

Dimensionality Reduction

Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility

no code implementations27 Nov 2022 Syed Mohammed Arshad Zaidi, Varun Chandola, EunHye Yoo

Deep learning approaches for spatio-temporal prediction problems such as crowd-flow prediction assumes data to be of fixed and regular shaped tensor and face challenges of handling irregular, sparse data tensor.

An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds

no code implementations25 Nov 2022 Amol Salunkhe, Georgios Georgalis, Abani Patra, Varun Chandola

We investigate two strategies of creating these ensemble models: one that keeps the flamelet origin information (Flamelets strategy) and one that ignores the origin and considers all the data independently (Points strategy).

Uncertainty Quantification

Physics Informed Machine Learning for Chemistry Tabulation

no code implementations6 Nov 2022 Amol Salunkhe, Dwyer Deighan, Paul DesJardin, Varun Chandola

Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow.

Physics-informed machine learning

ChemTab: A Physics Guided Chemistry Modeling Framework

no code implementations20 Feb 2022 Amol Salunkhe, Dwyer Deighan, Paul DesJardin, Varun Chandola

Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow.

Anomaly Detection for High-Dimensional Data Using Large Deviations Principle

no code implementations28 Sep 2021 Sreelekha Guggilam, Varun Chandola, Abani Patra

We propose an anomaly detection algorithm that can scale to high-dimensional data using concepts from the theory of large deviations.

Anomaly Detection Time Series +2

From images in the wild to video-informed image classification

no code implementations24 Sep 2021 Marc Böhlen, Varun Chandola, Wawan Sujarwo, Raunaq Jain

Image classifiers work effectively when applied on structured images, yet they often fail when applied on images with very high visual complexity.

Classification Image Classification

Integrated Clustering and Anomaly Detection (INCAD) for Streaming Data (Revised)

1 code implementation1 Nov 2019 Sreelekha Guggilam, Syed M. A. Zaidi, Varun Chandola, Abani K. Patra

Most current clustering based anomaly detection methods use scoring schema and thresholds to classify anomalies.

Anomaly Detection Clustering

Bayesian Anomaly Detection Using Extreme Value Theory

1 code implementation29 May 2019 Sreelekha Guggilam, S. M. Arshad Zaidi, Varun Chandola, Abani Patra

Data-driven anomaly detection methods typically build a model for the normal behavior of the target system, and score each data instance with respect to this model.

Anomaly Detection Clustering

Learning Deep Representations from Clinical Data for Chronic Kidney Disease

no code implementations1 Oct 2018 Duc Thanh Anh Luong, Varun Chandola

We study the behavior of a Time-Aware Long Short-Term Memory Autoencoder, a state-of-the-art method, in the context of learning latent representations from irregularly sampled patient data.

Time Series Analysis

Learning Manifolds from Non-stationary Streaming Data

no code implementations24 Apr 2018 Suchismit Mahapatra, Varun Chandola

We present theoretical results to show that the quality of a manifold asymptotically converges as the size of data increases.

Dimensionality Reduction GPR +1

Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes

no code implementations19 Feb 2018 Frank Schoeneman, Varun Chandola, Nils Napp, Olga Wodo, Jaroslaw Zola

Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters.

Dimensionality Reduction Vocal Bursts Intensity Prediction

Server, server in the cloud. Who is the fairest in the crowd?

1 code implementation23 Nov 2017 Marc Böhlen, Varun Chandola, Amol Salunkhe

This paper follows the recent history of automated beauty competitions to discuss how machine learning techniques, in particular neural networks, alter the way attractiveness is handled and how this impacts the cultural landscape.

Computers and Society

S-Isomap++: Multi Manifold Learning from Streaming Data

no code implementations17 Oct 2017 Suchismit Mahapatra, Varun Chandola

Manifold learning based methods have been widely used for non-linear dimensionality reduction (NLDR).

Dimensionality Reduction

Error Metrics for Learning Reliable Manifolds from Streaming Data

no code implementations13 Nov 2016 Frank Schoeneman, Suchismit Mahapatra, Varun Chandola, Nils Napp, Jaroslaw Zola

In this paper, we argue that a stable manifold can be learned using only a fraction of the stream, and the remaining stream can be mapped to the manifold in a significantly less costly manner.

Dimensionality Reduction

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