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03/31/2023
Workflows Community Summit 2022: A Roadmap Revolution
Scientific workflows have become integral tools in broad scientific comp...
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07/14/2022
Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning
Improved understanding of the relation between the behavior of RAS and R...
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07/09/2022
Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints
Capturing intricate biological phenomena often requires multiscale model...
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06/16/2022
"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches
Deep learning approaches have provided state-of-the-art performance in m...
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07/30/2020
AMM: Adaptive Multilinear Meshes
We present Adaptive Multilinear Meshes (AMM), a new framework that signi...
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07/01/2020
Scalable Comparative Visualization of Ensembles of Call Graphs
Optimizing the performance of large-scale parallel codes is critical for...
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05/05/2020
Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models
Predictive models that accurately emulate complex scientific processes c...
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12/17/2019
Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
Neural networks have become very popular in surrogate modeling because o...
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12/05/2019
Merlin: Enabling Machine Learning-Ready HPC Ensembles
With the growing complexity of computational and experimental facilities...
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10/05/2019
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Training deep neural networks on large scientific data is a challenging ...
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10/03/2019
Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
There is significant interest in using modern neural networks for scient...
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09/25/2019
Function Preserving Projection for Scalable Exploration of High-Dimensional Data
We present function preserving projections (FPP), a scalable linear proj...
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09/09/2019
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
With rapid adoption of deep learning in high-regret applications, the qu...
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07/19/2019
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
With the rapid adoption of machine learning techniques for large-scale a...
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06/06/2019
A Look at the Effect of Sample Design on Generalization through the Lens of Spectral Analysis
This paper provides a general framework to study the effect of sampling ...
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10/31/2018
Unsupervised Dimension Selection using a Blue Noise Spectrum
Unsupervised dimension selection is an important problem that seeks to r...
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10/31/2018
Understanding Deep Neural Networks through Input Uncertainties
Techniques for understanding the functioning of complex machine learning...
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09/05/2018
Controlled Random Search Improves Sample Mining and Hyper-Parameter Optimization
A common challenge in machine learning and related fields is the need to...
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12/19/2017
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
Two-dimensional embeddings remain the dominant approach to visualize hig...
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12/16/2017
