Search Results for author: Shusen Liu

Found 11 papers, 5 papers with code

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.

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

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.

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.

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