Search Results for author: Peer-Timo Bremer

Found 23 papers, 8 papers with code

Transfer learning suppresses simulation bias in predictive models built from sparse, 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

The transfer learning method can be applied to other problems that require transferring knowledge from simulations to the domain of real observations.

Transfer Learning

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

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.

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

Scalable Comparative Visualization of Ensembles of Call Graphs

1 code implementation1 Jul 2020 Suraj P. Kesavan, Harsh Bhatia, Abhinav Bhatele, Todd Gamblin, Peer-Timo Bremer, Kwan-Liu Ma

Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources.

Distributed, Parallel, and Cluster Computing Performance

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

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.

Enabling Machine Learning-Ready HPC Ensembles with Merlin

no code implementations5 Dec 2019 J. Luc Peterson, Ben Bay, Joe Koning, Peter Robinson, Jessica Semler, Jeremy White, Rushil Anirudh, Kevin Athey, Peer-Timo Bremer, Francesco Di Natale, David Fox, Jim A. Gaffney, Sam A. Jacobs, Bhavya Kailkhura, Bogdan Kustowski, Steven Langer, Brian Spears, Jayaraman Thiagarajan, Brian Van Essen, Jae-Seung Yeom

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data.

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

2 code implementations5 Oct 2019 Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagaranjan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter Robinson, Luc Peterson, Brian Spears

Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the process.

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.

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

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.

Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension

no code implementations EMNLP 2018 Shusen Liu, Tao Li, Zhimin Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer

Neural networks models have gained unprecedented popularity in natural language processing due to their state-of-the-art performance and the flexible end-to-end training scheme.

Decision Making Natural Language Inference +1

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

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

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

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.

A Randomized Ensemble Approach to Industrial CT Segmentation

no code implementations ICCV 2015 Hyojin Kim, Jayaraman Jayaraman J. Thiagarajan, Peer-Timo Bremer

Tuning the models and parameters of common segmentation approaches is challenging especially in the presence of noise and artifacts.

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