Jie Bu

According to our database, Jie Bu authored at least 15 papers between 2019 and 2023.

Collaborative distances:

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

2023

Beyond Discriminative Regions: Saliency Maps as Alternatives to CAMs for Weakly Supervised Semantic Segmentation.

M. Maruf, Arka Daw, Amartya Dutta, Jie Bu, Anuj Karpatne

CoRR, 2023

Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation.

Jie Bu, Kazi Sajeed Mehrab, Anuj Karpatne

CoRR, 2023

Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling.

Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne

Proceedings of the International Conference on Machine Learning, 2023

2022

<i>CoPhy</i>-PGNN: Learning Physics-guided Neural Networks with Competing Loss Functions for Solving Eigenvalue Problems.

Mohannad Elhamod, Jie Bu, Christopher Singh, Matthew Redell, Abantika Ghosh, Viktor Podolskiy, Wei-Cheng Lee, Anuj Karpatne

ACM Trans. Intell. Syst. Technol., 2022

Robust multi-view subspace clustering based on consensus representation and orthogonal diversity.

Nan Zhao, Jie Bu

Neural Networks, 2022

Rethinking the Importance of Sampling in Physics-informed Neural Networks.

Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne

CoRR, 2022

2021

Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs.

Jie Bu, Anuj Karpatne

Proceedings of the 2021 SIAM International Conference on Data Mining, 2021

Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM).

Jie Bu, Arka Daw, M. Maruf, Anuj Karpatne

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

PhyFlow: Physics-Guided Deep Learning for Generating Interpretable 3D Flow Fields.

Nikhil Muralidhar, Jie Bu, Ze Cao, Neil Raj, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne

Proceedings of the IEEE International Conference on Data Mining, 2021

Learning Physics-guided Neural Networks with Competing Physics Loss: A Summary of Results in Solving Eigenvalue Problems.

Mohannad Elhamod, Jie Bu, Christopher Singh, Matthew Redell, Abantika Ghosh, Viktor Podolskiy, Wei-Cheng Lee, Anuj Karpatne

Proceedings of the AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, USA, March 22nd - to, 2021

2020

Beyond Observed Connections : Link Injection.

Jie Bu, M. Maruf, Arka Daw

CoRR, 2020

Learning Neural Networks with Competing Physics Objectives: An Application in Quantum Mechanics.

Jie Bu, Mohannad Elhamod, Christopher Singh, Matthew Redell, Wei-Cheng Lee, Anuj Karpatne

CoRR, 2020

Physics-Guided Deep Learning for Drag Force Prediction in Dense Fluid-Particulate Systems.

Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne

Big Data, 2020

PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly.

Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

2019

Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids.

Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne

CoRR, 2019