Search Results for author:
Found 32 papers, 10 papers with code
AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
With recent advances in multi-modal foundation models, the previously text-only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented opportunities for various applications in visualization.
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
To this end, we introduce Cross-GAN Auditing (xGA) that, given an established "reference" GAN and a newly proposed "client" GAN, jointly identifies intelligible attributes that are either common across both GANs, novel to the client GAN, or missing from the client GAN.
Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning
Improved understanding of the relation between the behavior of RAS and RAF proteins and the local lipid environment in the cell membrane is critical for getting insights into the mechanisms underlying cancer formation.
Single Model Uncertainty Estimation via Stochastic Data Centering
We are interested in estimating the uncertainties of deep neural networks, which play an important role in many scientific and engineering problems.
Emerging Patterns in the Continuum Representation of Protein-Lipid Fingerprints
Capturing intricate biological phenomena often requires multiscale modeling where coarse and inexpensive models are developed using limited components of expensive and high-fidelity models.
Models Out of Line: A Fourier Lens on Distribution Shift Robustness
However, there still is no clear understanding of the conditions on OOD data and model properties that are required to observe effective robustness.
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches
We decomposed and evaluated a set of critical geometric concepts from the common adopted classification loss, and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation.
Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion
In this paper, we develop a Wasserstein autoencoder (WAE) with a hyperspherical prior for multimodal data in the application of inertial confinement fusion.
A Biology-Informed Similarity Metric for Simulated Patches of Human Cell Membrane
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.
Suppressing simulation bias using multi-modal data
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.
Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations
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.
Machine Learning-Powered Mitigation Policy Optimization in Epidemiological Models
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
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
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.
Scalable Comparative Visualization of Ensembles of Call Graphs
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
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.
Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
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
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
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
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
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data.
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
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.
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 applications in science and engineering comes the convergence of two grand challenges in visualization.
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 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
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.
Unsupervised Dimension Selection using a Blue Noise Spectrum
Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics.
Understanding Deep Neural Networks through Input Uncertainties
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.
Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization
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.
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
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
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.
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
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
Tuning the models and parameters of common segmentation approaches is challenging especially in the presence of noise and artifacts.
