no code implementations • 7 Dec 2023 • Shusen Liu, Haichao Miao, Zhimin Li, Matthew Olson, Valerio Pascucci, Peer-Timo Bremer
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.
no code implementations • 6 Dec 2023 • Matthew L. Olson, Shusen Liu, Jayaraman J. Thiagarajan, Bogdan Kustowski, Weng-Keen Wong, Rushil Anirudh
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains.
no code implementations • 25 Oct 2023 • Zhimin Li, Shusen Liu, Kailkhura Bhavya, Timo Bremer, Valerio Pascucci
For a neural network model, the non-linear behavior is often caused by non-linear activation units of a model.
no code implementations • 30 Jun 2023 • Ruben Glatt, Shusen Liu
Emerging foundation models in machine learning are models trained on vast amounts of data that have been shown to generalize well to new tasks.
1 code implementation • CVPR 2023 • Matthew L. Olson, Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Weng-Keen Wong
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.
no code implementations • 30 Oct 2022 • Yuzhe Lu, Shusen Liu, Jayaraman J. Thiagarajan, Wesam Sakla, Rushil Anirudh
We present a fully automated framework for building object detectors on satellite imagery without requiring any human annotation or intervention.
no code implementations • 16 Jun 2022 • Zhimin Li, Shusen Liu, Xin Yu, Kailkhura Bhavya, Jie Cao, Diffenderfer James Daniel, Peer-Timo Bremer, Valerio Pascucci
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.
no code implementations • 25 Jun 2021 • Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna Hiszpanski, Yong Han
Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection.
no code implementations • 16 Jul 2020 • Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin Han
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
no code implementations • 30 Jun 2020 • Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin Han
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
2 code implementations • 5 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.
2 code implementations • 3 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.
1 code implementation • 25 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.
2 code implementations • 19 Jul 2019 • Shusen Liu, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom, Jim Gaffney, Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer
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.
no code implementations • 6 Jul 2019 • Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han
In this work, we propose an introspection technique for deep neural networks that relies on a generative model to instigate salient editing of the input image for model interpretation.
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.
1 code implementation • 2 Jul 2018 • Shusen Liu, Yi-Nan Li, Runyao Duan
We program these two schemes on the \emph{ibmqx4}, a $5$-qubit superconducting quantum processor via IBM cloud, with the help of the $QSI$ modules [S. Liu et al.,~arXiv:1710. 09500, 2017].
Quantum Physics
no code implementations • 19 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.