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publications
Compass: Spatio temporal sentiment analysis of US election what twitter says!
Published in SIG-KDD, 2017
This paper is about number 1. The number 2 is left for future work.
Recommended citation: Paul, D., Li, F., Teja, M.K., Yu, X. and Frost, R., 2017, August. " Compass: Spatio temporal sentiment analysis of US election what twitter says!." In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1585-1594). (KDD 2017) http://yuxwind.github.io/files/compass-kdd.pdf
Learning strict identity mappings in deep residual networks
Published in CVPR, 2018
We hypothesize that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping.
Recommended citation: Yu, X., Yu, Z. and Ramalingam, S., 2018. Learning strict identity mappings in deep residual networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4432-4440) (CVPR 2018) http://yuxwind.github.io/files/pruning-cvpr2018.pdf
Vlase: Vehicle localization by aggregating semantic edges
Published in IROS, 2018
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Yu, X., Chaturvedi, S., Feng, C., Taguchi, Y., Lee, T.Y., Fernandes, C. and Ramalingam, S., 2018, October. Vlase: Vehicle localization by aggregating semantic edges. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3196-3203). IEEE. (IROS 2018) http://academicpages.github.io/files/paper3.pdf
Mapping of Sparse 3D Data using Alternating Projection
Published in ACCV, 2020
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Ranade, S.*, ,Yu, X.*, Kakkar, S., Miraldo, P. and Ramalingam, S., 2020. Mapping of Sparse 3D Data using Alternating Projection. In Proceedings of the Asian Conference on Computer Vision.(ACCV 2020 Oral) http://yuxwind.github.io/files/3D-accv.pdf
Scaling Up Exact Neural Network Compression by ReLU Stability
Published in NeurIPS, 2021
Recommended citation: Serra, T., Yu, X., Kumar, A. and Ramalingam, S., 2021. Scaling Up Exact Neural Network Compression by ReLU Stability. arXiv preprint arXiv:2102.07804. (NeurIPS 2021) http://yuxwind.github.io/files/pruning-nips2021.pdf
Joint 3D Human Shape Recovery and Pose Estimation from A Single Image with Bilayer-Graph
Published in 3DV, 2021
Recommended citation: Yu X., & Baar J, Chen S. Joint 3D Human Shape Recovery and Pose Estimation from A Single Image with Bilayer-Graph. in International Conference on 3D Vision (3DV 2021) http://yuxwind.github.io/files/2021_3dv.pdf
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Published in ICML, 2022
Recommended citation: Yu X.*, Serra T.*, Ramalingam S., Zhe S. The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks. In International Conference on Machine Learning (ICML 2022). http://yuxwind.github.io/files/2022_icml.pdf
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Published in NeurIPS, 2022
Recommended citation: Good A.*, Lin J.*, Yu X.*, Sieg H., Ferguson M., Zhe S., Wieczore J., & Serra T. Recall Ditortion in Neural Network Pruning and the Undecayed Pruning Algorithm. Advances in Neural Information Processing Systems (NeurIPS 2022) http://yuxwind.github.io/files/2022_neurips.pdf
Batch Multi-Fidelity Active Learning with Budget Constraints
Published in NeurIPS, 2022
Recommended citation: Li, S.*, Phillips, J.*, Yu, X., Kirby, R., & Zhe, S. Batch Multi-Fidelity Active Learning with Budget Constraints. Advances in Neural Information Processing Systems (NeurIPS 2022). http://yuxwind.github.io/files/2022_neurips_active.pdf
Getting away with more network pruning: From sparsity to geometry and linear regions
Published in CPAIOR, 2023
Recommended citation: Cai J., Nguyen K., Shrestha N., Good A., Tu R., Yu X., & Serra T. Getting away with more network pruning: From sparsity to geometry and linear regions. International Confer- ence on Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2023) http://yuxwind.github.io/files/2023_cpaior.pdf
Streaming Factor Trajectory Learning for Temporal Tensor Decomposition
Published in NeurIPS, 2023
Recommended citation: Fang, S., Yu, X., Li, S., Wang, Z., Kirby R., & Zhe, S. Streaming Factor Trajectory Learning for Temporal Tensor Decomposition. Advances in Neural Information Processing Systems (NeurIPS 2023) http://yuxwind.github.io/files/2023_neurips
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching Assistant for CS6320 Computer Vision, Spring 2018
Graduate Course, University of Utah, School of Computing, 2018
This class is taught by Prof. Srikumar Ramalingam, which provides the introduction to fundamental concepts in computer vision. Topics in this class include camera pose estimation, 3D reconstruction, feature detectors and descriptors, object recognition using vocabulary tree, segmentation, stereo matching, graph cuts, belief propagation, and a brief introduction to deep neural networks. In the assignments, the students will be expected to implement basic computer vision tasks such as segmentation, stereo reconstruction, image matching using vocabulary tree, and small computer vision applications using deep neural networks.
Teaching Assistant for CS6320 Computer Vision, Spring 2019
Graduate Course, University of Utah, School of Computing, 2019
This class is taught by Prof. Srikumar Ramalingam, which provides the introduction to fundamental concepts in computer vision. Topics in this class include camera pose estimation, 3D reconstruction, feature detectors and descriptors, object recognition using vocabulary tree, segmentation, stereo matching, graph cuts, belief propagation, and a brief introduction to deep neural networks. In the assignments, the students will be expected to implement basic computer vision tasks such as segmentation, stereo reconstruction, image matching using vocabulary tree, and small computer vision applications using deep neural networks.
Teaching Assistant for CS6320 Computer Vision, Spring 2020
Graduate Course, University of Utah, School of Computing, 2019
This course is taught by Prof. Tucker Hermans
Teaching Assistant for CS6190 Probability Modeling, Spring 2023
Graduate Course, University of Utah, School of Computing, 2023
This course is taught by Prof. Shandian Zhe