Search Results for author: Jayaraman J. Thiagarajan

Found 71 papers, 10 papers with code

Improving Multi-Domain Generalization through Domain Re-labeling

no code implementations17 Dec 2021 Kowshik Thopalli, Sameeksha Katoch, Andreas Spanias, Pavan Turaga, Jayaraman J. Thiagarajan

In this paper, we focus on the challenging problem of multi-source zero-shot DG, where labeled training data from multiple source domains is available but with no access to data from the target domain.

Domain Generalization

Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion

no code implementations24 Nov 2021 Ankita Shukla, Rushil Anirudh, Eugene Kur, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears, Tammy Ma, Pavan Turaga

In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion.

$Δ$-UQ: Accurate Uncertainty Quantification via Anchor Marginalization

no code implementations5 Oct 2021 Rushil Anirudh, Jayaraman J. Thiagarajan

Anchoring works by first transforming the input into a tuple consisting of an anchor point drawn from a prior distribution, and a combination of the input sample with the anchor using a pretext encoding scheme.

Out-of-Distribution Detection

Designing Counterfactual Generators using Deep Model Inversion

no code implementations NeurIPS 2021 Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Deepta Rajan, Jason Liang, Akshay Chaudhari, Andreas Spanias

Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models.

Image Generation

A Biology-Informed Similarity Metric for Simulated Patches of Human Cell Membrane

no code implementations29 Sep 2021 Harsh Bhatia, Jayaraman J. Thiagarajan, Rushil Anirudh, T.S. Jayram, Tomas Oppelstrup, Helgi I. Ingolfsson, Felice C Lightstone, Peer-Timo Bremer

Complex scientific inquiries rely increasingly upon large and autonomous multiscale simulation campaigns, which fundamentally require similarity metrics to quantify "sufficient'' changes among data and/or configurations.

Metric Learning

Interrogating Paradigms in Self-supervised Graph Representation Learning

no code implementations29 Sep 2021 Puja Trivedi, Mark Heimann, Danai Koutra, Jayaraman J. Thiagarajan

Using the recent population augmentation graph-based analysis of self-supervised learning, we show theoretically that the success of GCL with popular augmentations is bounded by the graph edit distance between different classes.

Contrastive Learning Graph Representation Learning +2

Suppressing simulation bias using multi-modal data

no code implementations19 Apr 2021 Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Michael K. G. Kruse, Ryan C. Nora

The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.

Transfer Learning

On the Design of Deep Priors for Unsupervised Audio Restoration

1 code implementation14 Apr 2021 Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias

Unsupervised deep learning methods for solving audio restoration problems extensively rely on carefully tailored neural architectures that carry strong inductive biases for defining priors in the time or spectral domain.

Audio Denoising Denoising

Loss Estimators Improve Model Generalization

no code implementations5 Mar 2021 Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan, Andreas Spanias

With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate predictions but also do not generalize to data regimes where the training data provide no meaningful evidence.

Comparative Code Structure Analysis using Deep Learning for Performance Prediction

no code implementations12 Feb 2021 Nathan Pinnow, Tarek Ramadan, Tanzima Z. Islam, Chase Phelps, Jayaraman J. Thiagarajan

Performance analysis has always been an afterthought during the application development process, focusing on application correctness first.

Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

3 code implementations3 Dec 2020 Tejas Gokhale, Rushil Anirudh, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Chitta Baral, Yezhou Yang

While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes.

A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning

no code implementations NeurIPS 2020 Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer

Using this framework, we show that space-filling sample designs, such as blue noise and Poisson disk sampling, which optimize spectral properties, outperform random designs in terms of the generalization gap and characterize this gain in a closed-form.

Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations

no code implementations26 Oct 2020 Gemma J. Anderson, Jim A. Gaffney, Brian K. Spears, Peer-Timo Bremer, Rushil Anirudh, Jayaraman J. Thiagarajan

Large-scale numerical simulations are used across many scientific disciplines to facilitate experimental development and provide insights into underlying physical processes, but they come with a significant computational cost.

Variational Inference

Using Deep Image Priors to Generate Counterfactual Explanations

no code implementations22 Oct 2020 Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias

Through the use of carefully tailored convolutional neural network architectures, a deep image prior (DIP) can be used to obtain pre-images from latent representation encodings.

Lesion Detection

Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models

no code implementations16 Oct 2020 Jayaraman J. Thiagarajan, Peer-Timo Bremer, Rushil Anirudh, Timothy C. Germann, Sara Y. Del Valle, Frederick H. Streitz

A crucial aspect of managing a public health crisis is to effectively balance prevention and mitigation strategies, while taking their socio-economic impact into account.

Accurate Calibration of Agent-based Epidemiological Models with Neural Network Surrogates

no code implementations13 Oct 2020 Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Timothy C. Germann, Sara Y. Del Valle, Frederick H. Streitz

Here we present a new approach to calibrate an agent-based model -- EpiCast -- using a large set of simulation ensembles for different major metropolitan areas of the United States.

Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks

no code implementations30 Sep 2020 Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias

In this work, we propose Uncertainty Matching GNN (UM-GNN), that is aimed at improving the robustness of GNN models, particularly against poisoning attacks to the graph structure, by leveraging epistemic uncertainties from the message passing framework.

Graph Classification Link Prediction +1

Accurate and Robust Feature Importance Estimation under Distribution Shifts

no code implementations30 Sep 2020 Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Rushil Anirudh, Peer-Timo Bremer, Andreas Spanias

With increasing reliance on the outcomes of black-box models in critical applications, post-hoc explainability tools that do not require access to the model internals are often used to enable humans understand and trust these models.

Feature Importance

Unsupervised Audio Source Separation using Generative Priors

1 code implementation28 May 2020 Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Rushil Anirudh, Andreas Spanias

State-of-the-art under-determined audio source separation systems rely on supervised end-end training of carefully tailored neural network architectures operating either in the time or the spectral domain.

Audio Source Separation

Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models

no code implementations5 May 2020 Jayaraman J. Thiagarajan, Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, Brian Spears

Predictive models that accurately emulate complex scientific processes can achieve exponential speed-ups over numerical simulators or experiments, and at the same time provide surrogates for improving the subsequent analysis.

Small Data Image Classification

Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification

no code implementations3 May 2020 Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris, Satyananda Kashyap

Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support simultaneous prediction of multiple disease conditions.

Data Augmentation Few-Shot Learning +1

Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models

no code implementations27 Apr 2020 Jayaraman J. Thiagarajan, Prasanna Sattigeri, Deepta Rajan, Bindya Venkatesh

The wide-spread adoption of representation learning technologies in clinical decision making strongly emphasizes the need for characterizing model reliability and enabling rigorous introspection of model behavior.

Decision Making Lesion Classification +1

Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration

no code implementations10 Feb 2020 Bindya Venkatesh, Jayaraman J. Thiagarajan, Kowshik Thopalli, Prasanna Sattigeri

The hypothesis that sub-network initializations (lottery) exist within the initializations of over-parameterized networks, which when trained in isolation produce highly generalizable models, has led to crucial insights into network initialization and has enabled efficient inferencing.

Transfer Learning

Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies

1 code implementation17 Dec 2019 Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Brian K. Spears

Neural networks have become very popular in surrogate modeling because of their ability to characterize arbitrary, high dimensional functions in a data driven fashion.

MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking

no code implementations16 Dec 2019 Rushil Anirudh, Jayaraman J. Thiagarajan, Bhavya Kailkhura, Timo Bremer

However, PGD is a brittle optimization technique that fails to identify the right projection (or latent vector) when the observation is corrupted, or perturbed even by a small amount.

Adversarial Defense Anomaly Detection +2

Heteroscedastic Calibration of Uncertainty Estimators in Deep Learning

no code implementations30 Oct 2019 Bindya Venkatesh, Jayaraman J. Thiagarajan

The role of uncertainty quantification (UQ) in deep learning has become crucial with growing use of predictive models in high-risk applications.

Learn-By-Calibrating: Using Calibration as a Training Objective

no code implementations30 Oct 2019 Jayaraman J. Thiagarajan, Bindya Venkatesh, Deepta Rajan

Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks.

Prediction Intervals

Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

2 code implementations3 Oct 2019 Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Brian K. Spears

There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion.

Improving Limited Angle CT Reconstruction with a Robust GAN Prior

no code implementations NeurIPS Workshop Deep_Invers 2019 Rushil Anirudh, Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle M. Champley

Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved.

Function Preserving Projection for Scalable Exploration of High-Dimensional Data

1 code implementation25 Sep 2019 Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer

We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data.

Dimensionality Reduction

Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors

1 code implementation9 Sep 2019 Jayaraman J. Thiagarajan, Bindya Venkatesh, Prasanna Sattigeri, Peer-Timo Bremer

With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties.

Object Localization Prediction Intervals +2

Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation

no code implementations26 Jul 2019 Jayaraman J. Thiagarajan, Satyananda Kashyap, Alexandros Karagyris

Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis.

Breast Cancer Detection Instance Segmentation +3

SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation

no code implementations11 Jun 2019 Kowshik Thopalli, Jayaraman J. Thiagarajan, Rushil Anirudh, Pavan Turaga

This paper represents a hybrid approach, where we assume simplified data geometry in the form of subspaces, and consider alignment as an auxiliary task to the primary task of maximizing performance on the source.

Auxiliary Learning Unsupervised Domain Adaptation

A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis

no code implementations6 Jun 2019 Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Peer-Timo Bremer

This paper provides a general framework to study the effect of sampling properties of training data on the generalization error of the learned machine learning (ML) models.

Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets

no code implementations8 Apr 2019 Vivek Sivaraman Narayanaswamy, Sameeksha Katoch, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias

We also investigate the impact of dense connections on the extraction process that encourage feature reuse and better gradient flow.

Audio Source Separation

Designing an Effective Metric Learning Pipeline for Speaker Diarization

no code implementations1 Nov 2018 Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias

State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data.

Metric Learning Speaker Diarization

Understanding Deep Neural Networks through Input Uncertainties

no code implementations31 Oct 2018 Jayaraman J. Thiagarajan, Irene Kim, Rushil Anirudh, Peer-Timo Bremer

Techniques for understanding the functioning of complex machine learning models are becoming increasingly popular, not only to improve the validation process, but also to extract new insights about the data via exploratory analysis.

Unsupervised Dimension Selection using a Blue Noise Spectrum

no code implementations31 Oct 2018 Jayaraman J. Thiagarajan, Rushil Anirudh, Rahul Sridhar, Peer-Timo Bremer

Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics.

Dimensionality Reduction

Attention Models with Random Features for Multi-layered Graph Embeddings

no code implementations2 Oct 2018 Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias

Though deep network embeddings, e. g. DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective.

Network Embedding Node Classification

Improved Deep Embeddings for Inferencing with Multi-Layered Networks

no code implementations20 Sep 2018 Huan Song, Jayaraman J. Thiagarajan

Inferencing with network data necessitates the mapping of its nodes into a vector space, where the relationships are preserved.

Community Detection Link Prediction +2

Understanding Behavior of Clinical Models under Domain Shifts

no code implementations20 Sep 2018 Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri

The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare.

Multi-Label Classification Unsupervised Domain Adaptation

Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization

1 code implementation5 Sep 2018 Gowtham Muniraju, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Cihan Tepedelenlioglu, Andreas Spanias

Sampling one or more effective solutions from large search spaces is a recurring idea in machine learning, and sequential optimization has become a popular solution.

Data Summarization

Triplet Network with Attention for Speaker Diarization

no code implementations4 Aug 2018 Huan Song, Megan Willi, Jayaraman J. Thiagarajan, Visar Berisha, Andreas Spanias

In automatic speech processing systems, speaker diarization is a crucial front-end component to separate segments from different speakers.

Metric Learning Speaker Diarization

A Generative Modeling Approach to Limited Channel ECG Classification

no code implementations18 Feb 2018 Deepta Rajan, Jayaraman J. Thiagarajan

Processing temporal sequences is central to a variety of applications in health care, and in particular multi-channel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling.

Classification Disease Prediction +3

Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections

no code implementations19 Dec 2017 Jayaraman J. Thiagarajan, Shusen Liu, Karthikeyan Natesan Ramamurthy, Peer-Timo Bremer

Furthermore, we introduce a new approach to discover a diverse set of high quality linear projections and show that in practice the information of $k$ linear projections is often jointly encoded in $\sim k$ axis aligned plots.

A Spectral Approach for the Design of Experiments: Design, Analysis and Algorithms

no code implementations16 Dec 2017 Bhavya Kailkhura, Jayaraman J. Thiagarajan, Charvi Rastogi, Pramod K. Varshney, Peer-Timo Bremer

Third, we propose an efficient estimator to evaluate the space-filling properties of sample designs in arbitrary dimensions and use it to develop an optimization framework to generate high quality space-filling designs.

Image Reconstruction

Optimizing Kernel Machines using Deep Learning

no code implementations15 Nov 2017 Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Andreas Spanias

To this end, we develop the DKMO (Deep Kernel Machine Optimization) framework, that creates an ensemble of dense embeddings using Nystrom kernel approximations and utilizes deep learning to generate task-specific representations through the fusion of the embeddings.

MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis

no code implementations15 Nov 2017 Rushil Anirudh, Jayaraman J. Thiagarajan, Rahul Sridhar, Peer-Timo Bremer

Interpretability has emerged as a crucial aspect of building trust in machine learning systems, aimed at providing insights into the working of complex neural networks that are otherwise opaque to a user.

Attend and Diagnose: Clinical Time Series Analysis using Attention Models

no code implementations10 Nov 2017 Huan Song, Deepta Rajan, Jayaraman J. Thiagarajan, Andreas Spanias

With widespread adoption of electronic health records, there is an increased emphasis for predictive models that can effectively deal with clinical time-series data.

Time Series Time Series Analysis

Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification

no code implementations24 Apr 2017 Rushil Anirudh, Jayaraman J. Thiagarajan

To address this, we propose a bootstrapped version of graph convolutional neural networks (G-CNNs) that utilizes an ensemble of weakly trained G-CNNs, and reduce the sensitivity of models on the choice of graph construction.

Classification General Classification +1

A Deep Learning Approach To Multiple Kernel Fusion

no code implementations28 Dec 2016 Huan Song, Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Andreas Spanias

Kernel fusion is a popular and effective approach for combining multiple features that characterize different aspects of data.

Activity Recognition

Robust Local Scaling using Conditional Quantiles of Graph Similarities

no code implementations14 Dec 2016 Jayaraman J. Thiagarajan, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy, Bhavya Kailkhura

In this paper, we propose the use of quantile analysis to obtain local scale estimates for neighborhood graph construction.

graph construction

Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series

no code implementations29 Nov 2016 Rushil Anirudh, Jayaraman J. Thiagarajan, Irene Kim, Wolfgang Polonik

We present an approach to model time series data from resting state fMRI for autism spectrum disorder (ASD) severity classification.

General Classification Time Series

TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning

no code implementations22 Nov 2016 Jayaraman J. Thiagarajan, Bhavya Kailkhura, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy

In this paper, we take a step in the direction of tackling the problem of interpretability without compromising the model accuracy.

Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach

no code implementations22 Jan 2016 Prashant Khanduri, Bhavya Kailkhura, Jayaraman J. Thiagarajan, Pramod K. Varshney

This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration).

Sparse Learning

Automatic Inference of the Quantile Parameter

no code implementations12 Nov 2015 Karthikeyan Natesan Ramamurthy, Aleksandr Y. Aravkin, Jayaraman J. Thiagarajan

However, loss functions such as quantile and quantile Huber generalize the symmetric $\ell_1$ and Huber losses to the asymmetric setting, for a fixed quantile parameter.

Beyond L2-Loss Functions for Learning Sparse Models

no code implementations26 Mar 2014 Karthikeyan Natesan Ramamurthy, Aleksandr Y. Aravkin, Jayaraman J. Thiagarajan

We propose an algorithm to learn dictionaries and obtain sparse codes when the data reconstruction fidelity is measured using any smooth PLQ cost function.

online learning Sparse Learning +1

Recovering Non-negative and Combined Sparse Representations

no code implementations12 Mar 2013 Karthikeyan Natesan Ramamurthy, Jayaraman J. Thiagarajan, Andreas Spanias

For case (c), we propose the combined orthogonal matching pursuit algorithm for coefficient recovery and derive the deterministic sparsity threshold under which recovery of the unique, sparsest coefficient vector is possible.

Learning Stable Multilevel Dictionaries for Sparse Representations

no code implementations3 Mar 2013 Jayaraman J. Thiagarajan, Karthikeyan Natesan Ramamurthy, Andreas Spanias

Algorithmic stability and generalization are desirable characteristics for dictionary learning algorithms that aim to build global dictionaries which can efficiently model any test data similar to the training samples.

Dictionary Learning

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