94 citations · 108 across the 9 of their papers we have counts for
14 papers
Integration-free Learning of Flow Maps
Saroj Sahoo, Matthew Berger
We present a method for learning neural representations of flow maps from time-varying vector field data. The flow map is pervasive within the area of flow visualization, as it is…
STFT-LDA: An Algorithm to Facilitate the Visual Analysis of Building Seismic Responses
Zhenge Zhao, Danilo Motta, Matthew Berger +5
Civil engineers use numerical simulations of a building's responses to seismic forces to understand the nature of building failures, the limitations of building codes, and how to d…
Compressive Neural Representations of Volumetric Scalar Fields
Yuzhe Lu, Kairong Jiang, Joshua A. Levine +1
We present an approach for compressing volumetric scalar fields using implicit neural representations. Our approach represents a scalar field as a learned function, wherein a neura…
Visually Analyzing and Steering Zero Shot Learning
Saroj Sahoo, Matthew Berger
We propose a visual analytics system to help a user analyze and steer zero-shot learning models. Zero-shot learning has emerged as a viable scenario for categorizing data that cons…
Visually Analyzing Contextualized Embeddings
Matthew Berger
In this paper we introduce a method for visually analyzing contextualized embeddings produced by deep neural network-based language models. Our approach is inspired by linguistic p…
Attention Flows: Analyzing and Comparing Attention Mechanisms in Language Models
Joseph F DeRose, Jiayao Wang, Matthew Berger
Advances in language modeling have led to the development of deep attention-based models that are performant across a wide variety of natural language processing (NLP) problems. Th…
PRAGMA: Interactively Constructing Functional Brain Parcellations
Roza G. Bayrak, Nhung Hoang, Colin B. Hansen +2
A prominent goal of neuroimaging studies is mapping the human brain, in order to identify and delineate functionally-meaningful regions and elucidate their roles in cognitive behav…
Visualization of Unsteady Flow Using Heat Kernel Signatures
Kairong Jiang, Matthew Berger, Joshua A. Levine
We introduce a new technique to visualize complex flowing phenomena by using concepts from shape analysis. Our approach uses techniques that examine the intrinsic geometry of manif…
NeuralCubes: Deep Representations for Visual Data Exploration
Zhe Wang, Dylan Cashman, Mingwei Li +5
Visual exploration of large multidimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summ…
Topological Eulerian Synthesis of Slow Motion Periodic Videos
Christopher Tralie, Matthew Berger
We consider the problem of taking a video that is comprised of multiple periods of repetitive motion, and reordering the frames of the video into a single period, producing a detai…
A Generative Model for Volume Rendering
Matthew Berger, Jixian Li, Joshua A. Levine
We present a technique to synthesize and analyze volume-rendered images using generative models. We use the Generative Adversarial Network (GAN) framework to compute a model from a…
Visualizing Time-Varying Particle Flows with Diffusion Geometry
Matthew Berger, Joshua A. Levine
The tasks of identifying separation structures and clusters in flow data are fundamental to flow visualization. Significant work has been devoted to these tasks in flow represented…
Active Perceptual Similarity Modeling with Auxiliary Information
Eric Heim, Matthew Berger, Lee Seversky +1
Learning a model of perceptual similarity from a collection of objects is a fundamental task in machine learning underlying numerous applications. A common way to learn such a mode…
Efficient Online Relative Comparison Kernel Learning
Eric Heim, Matthew Berger, Lee M. Seversky +1
Learning a kernel matrix from relative comparison human feedback is an important problem with applications in collaborative filtering, object retrieval, and search. For learning a…