Search Results for author: Shusen Liu

Found 18 papers, 6 papers with code

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

no code implementations7 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.

Decision Making

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

no code implementations6 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.

Instance-wise Linearization of Neural Network for Model Interpretation

no code implementations25 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.

Dimensionality Reduction

Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging

no code implementations30 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.

Image Segmentation Semantic Segmentation +2

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

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.

Attribute Fairness

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

no code implementations30 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.

Object object-detection +1

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

no code implementations16 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.

Network Pruning

Reliable Graph Neural Network Explanations Through Adversarial Training

no code implementations25 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.

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

no code implementations16 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.

Actionable Attribution Maps for Scientific Machine Learning

no code implementations30 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.

BIG-bench Machine Learning

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

2 code implementations5 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.

Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

2 code implementations3 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.

Function Preserving Projection for Scalable Exploration of High-Dimensional Data

1 code implementation25 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.

Dimensionality Reduction Vocal Bursts Intensity Prediction

Generative Counterfactual Introspection for Explainable Deep Learning

no code implementations6 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.

counterfactual

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

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.

Decision Making Natural Language Inference +1

Distinguishing Unitary Gates on the IBM Quantum Processor

1 code implementation2 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

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

no code implementations19 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.

Vocal Bursts Intensity Prediction

Cannot find the paper you are looking for? You can Submit a new open access paper.