32 citations · 53 across the 27 of their papers we have counts for
10 papers · 1 filter
Unsupervised Latent Space Alignment with Hyperspherical Geodesic Matching
Cameron Ryan, Vivek Sivaraman Narayanaswamy, Kowshik Thopalli +1
Independently trained neural networks tend to encode the same data with similar latent geometries. These latent geometries are not directly compatible, yet they can be nearly the s…
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization
Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt +1
In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks. We pr…
Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data
Matthew L. Olson, Shusen Liu, Jayaraman J. Thiagarajan +3
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated supe…
Instance-wise Linearization of Neural Network for Model Interpretation
Zhimin Li, Shusen Liu, Kailkhura Bhavya +3
Neural network have achieved remarkable successes in many scientific fields. However, the interpretability of the neural network model is still a major bottlenecks to deploy such t…
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
Matthew L. Olson, Shusen Liu, Rushil Anirudh +3
Generative Adversarial Networks (GANs) are notoriously difficult to train especially for complex distributions and with limited data. This has driven the need for tools to audit tr…
Reliable Graph Neural Network Explanations Through Adversarial Training
Donald Loveland, Shusen Liu, Bhavya Kailkhura +2
Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection. While this has been deemed successful, many post-hoc explanation methods have…
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design
Shusen Liu, Bhavya Kailkhura, Jize Zhang +4
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges. However, despite the effectiveness in buildin…
Function Preserving Projection for Scalable Exploration of High-Dimensional Data
Shusen Liu, Rushil Anirudh, Jayaraman J. Thiagarajan +1
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data. Conventional dimensio…
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
Shusen Liu, Di Wang, Dan Maljovec +13
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. First…
Generative Counterfactual Introspection for Explainable Deep Learning
Shusen Liu, Bhavya Kailkhura, Donald Loveland +1
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 interpretat…