Shusen Liu

According to our database, Shusen Liu authored at least 44 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Visual Comparison of Silent Error Propagation.

Zhimin Li, Harshitha Menon, Kathryn M. Mohror, Shusen Liu, Luanzheng Guo, Peer-Timo Bremer, Valerio Pascucci

IEEE Trans. Vis. Comput. Graph., July, 2024

Visualization Literacy of Multimodal Large Language Models: A Comparative Study.

Zhimin Li, Haichao Miao, Valerio Pascucci, Shusen Liu

CoRR, 2024

Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization.

Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt, Shusen Liu

CoRR, 2024

Text-based Transfer Function Design for Semantic Volume Rendering.

Sangwon Jeong, Jixian Li, Christopher Johnson, Shusen Liu, Matthew Berger

CoRR, 2024

AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making.

Shusen Liu, Haichao Miao, Zhimin Li, Matthew L. Olson, Valerio Pascucci, Peer-Timo Bremer

Comput. Graph. Forum, 2024

CAN: Concept-Aligned Neurons for Visual Comparison of Deep Neural Network Models.

Mingwei Li, Sangwon Jeong, Shusen Liu, Matthew Berger

Comput. Graph. Forum, 2024

Text-based transfer function design for semantic volume rendering.

Sangwon Jeong, Jixian Li, Chris R. Johnson, Shusen Liu, Matthew Berger

Proceedings of the IEEE Visualization and Visual Analytics, 2024

The Visualization JUDGE: Can Multimodal Foundation Models Guide Visualization Design Through Visual Perception?

Matthew Berger, Shusen Liu

Proceedings of the IEEE Evaluation and Beyond, 2024

RankMean: Module-Level Importance Score for Merging Fine-tuned LLM Models.

Gabriel Perin, Xuxi Chen, Shusen Liu, Bhavya Kailkhura, Zhangyang Wang, Brian Gallagher

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data.

Matthew L. Olson, Shusen Liu, Jayaraman J. Thiagarajan, Bogdan Kustowski, Weng-Keen Wong, Rushil Anirudh

CoRR, 2023

Instance-wise Linearization of Neural Network for Model Interpretation.

Zhimin Li, Shusen Liu, Bhavya Kailkhura, Timo Bremer, Valerio Pascucci

CoRR, 2023

Concept Lens: Visually Analyzing the Consistency of Semantic Manipulation in GANs.

Sangwon Jeong, Mingwei Li, Matthew Berger, Shusen Liu

Proceedings of the 2023 IEEE Visualization and Visual Analytics (VIS), 2023

Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences Between Pretrained Generative Models.

Matthew L. Olson, Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Weng-Keen Wong

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

On-the-fly Object Detection using StyleGAN with CLIP Guidance.

Yuzhe Lu, Shusen Liu, Jayaraman J. Thiagarajan, Wesam Sakla, Rushil Anirudh

CoRR, 2022

"Understanding Robustness Lottery": A Comparative Visual Analysis of Neural Network Pruning Approaches.

Zhimin Li, Shusen Liu, Xin Yu, Bhavya Kailkhura, Jie Cao, James Daniel Diffenderfer, Peer-Timo Bremer, Valerio Pascucci

CoRR, 2022

Interactively Assessing Disentanglement in GANs.

Sangwon Jeong, Shusen Liu, Matthew Berger

Comput. Graph. Forum, 2022

Sparsity Improves Unsupervised Attribute Discovery in Stylegan.

Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

SpotSDC: Revealing the Silent Data Corruption Propagation in High-Performance Computing Systems.

Zhimin Li, Harshitha Menon, Dan Maljovec, Yarden Livnat, Shusen Liu, Kathryn M. Mohror, Peer-Timo Bremer, Valerio Pascucci

IEEE Trans. Vis. Comput. Graph., 2021

Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations.

Harsh Bhatia, Timothy S. Carpenter, Helgi I. Ingólfsson, Gautham Dharuman, Piyush Karande, Shusen Liu, Tomas Oppelstrup, Chris Neale, Felice C. Lightstone, Brian Van Essen, James N. Glosli, Peer-Timo Bremer

Nat. Mach. Intell., 2021

Reliable Graph Neural Network Explanations Through Adversarial Training.

Donald Loveland, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, Yong Han

CoRR, 2021

2020

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications.

Shusen Liu, Jim Gaffney, J. Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom

IEEE Trans. Vis. Comput. Graph., 2020

Uncovering interpretable relationships in high-dimensional scientific data through function preserving projections.

Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer

Mach. Learn. Sci. Technol., 2020

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge.

Anna M. Hiszpanski, Brian Gallagher, Karthik Chellappan, Peggy Li, Shusen Liu, Hyojin Kim, Jinkyu Han, Bhavya Kailkhura, David J. Buttler, Thomas Yong-Jin Han

J. Chem. Inf. Model., 2020

Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design.

Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han

CoRR, 2020

Actionable Attribution Maps for Scientific Machine Learning.

Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, Thomas Yong-Jin Han

CoRR, 2020

2019

NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models.

Shusen Liu, Zhimin Li, Tao Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer

IEEE Trans. Vis. Comput. Graph., 2019

Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion.

Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Brian K. Spears

CoRR, 2019

Function Preserving Projection for Scalable Exploration of High-Dimensional Data.

Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan, Peer-Timo Bremer

CoRR, 2019

Generative Counterfactual Introspection for Explainable Deep Learning.

Shusen Liu, Bhavya Kailkhura, Donald Loveland, Yong Han

Proceedings of the 2019 IEEE Global Conference on Signal and Information Processing, 2019

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets.

Sam Ade Jacobs, Jim Gaffney, Tom Benson, Peter B. Robinson, J. Luc Peterson, Brian K. Spears, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer

Proceedings of the 2019 IEEE International Conference on Cluster Computing, 2019

2018

Visual Exploration of Semantic Relationships in Neural Word Embeddings.

Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Vivek Srikumar, Bei Wang, Yarden Livnat, Valerio Pascucci

IEEE Trans. Vis. Comput. Graph., 2018

Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections.

Jayaraman J. Thiagarajan, Shusen Liu, Karthikeyan Natesan Ramamurthy, Peer-Timo Bremer

Comput. Graph. Forum, 2018

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

Shusen Liu, Tao Li, Zhimin Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018

2017

Visual Exploration of High-Dimensional Spaces Through Identification, Summarization, and Interpretation of Two-Dimensional Projections.

Shusen Liu

PhD thesis, 2017

Visualizing High-Dimensional Data: Advances in the Past Decade.

Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer, Valerio Pascucci

IEEE Trans. Vis. Comput. Graph., 2017

2016

Analyzing simulation-based PRA data through traditional and topological clustering: A BWR station blackout case study.

Dan Maljovec, Shusen Liu, Bei Wang, Diego Mandelli, Peer-Timo Bremer, Valerio Pascucci, Curtis L. Smith

Reliab. Eng. Syst. Saf., 2016

The Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data.

Shusen Liu, Peer-Timo Bremer, J. J. Jayaraman, Bei Wang, Brian Summa, Valerio Pascucci

Comput. Graph. Forum, 2016

Embedded domain-specific language and runtime system for progressive spatiotemporal data analysis and visualization.

Cameron Christensen, Ji-Woo Lee, Shusen Liu, Peer-Timo Bremer, Giorgio Scorzelli, Valerio Pascucci

Proceedings of the 6th IEEE Symposium on Large Data Analysis and Visualization, 2016

2015

Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections.

Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci

Comput. Graph. Forum, 2015

2014

Distortion-Guided Structure-Driven Interactive Exploration of High-Dimensional Data.

Shusen Liu, Bei Wang, Peer-Timo Bremer, Valerio Pascucci

Comput. Graph. Forum, 2014

Multivariate volume visualization through dynamic projections.

Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci

Proceedings of the 4th IEEE Symposium on Large Data Analysis and Visualization, 2014

2012

Gaussian mixture model based volume visualization.

Shusen Liu, Joshua A. Levine, Peer-Timo Bremer, Valerio Pascucci

Proceedings of the IEEE Symposium on Large Data Analysis and Visualization, 2012

2011

Feature-Based Statistical Analysis of Combustion Simulation Data.

Janine Bennett, Vaidyanathan Krishnamoorthy, Shusen Liu, Ray W. Grout, Evatt R. Hawkes, Jacqueline H. Chen, Jason Shepherd, Valerio Pascucci, Peer-Timo Bremer

IEEE Trans. Vis. Comput. Graph., 2011

Evaluating graph coloring on GPUs.

Andre Vincent Pascal Grosset, Peihong Zhu, Shusen Liu, Suresh Venkatasubramanian, Mary W. Hall

Proceedings of the 16th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2011