DeLTA Lab - Publication List

The School of Computing Science, Simon Fraser University

* denotes the author to be a DeLTA student or Dr. Chen's mentee. # denotes equal contributions.

Journal Paper

W. Chen#, X. Gong#, J. Wu#, Y. Wei, H. Shi, Z. Yan, Y. Yang, Z. Wang
“Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics”
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023. [Paper] [Code]

(α-β) T. Chen, X. Chen, W. Chen, H. Heaton, J. Liu, Z. Wang, W. Yin
“Learning to Optimize: A Primer and A Benchmark”
Journal of Machine Learning Research (JMLR), 2022. [Paper] [Code]

C. Li, W. Chen, Y. Gu, T. Chen, Y. Fu, Z. Wang, Y. Lin
“DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference”
ACM Transactions on Design Automation of Embedded Systems (TODAES), 2021. [Paper]

Conference Paper & Workshop Presentation

W. Chen#, J. Song#*, P. Ren, S. Subramanian, D. Morozov, MW. Mahoney
“Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning”
NeurIPS 2024. [Paper]

J. Wu*, W. Huang, M. Bai, X. Hu, Y. Duan, W. Chen
“Training-free Design of Augmentations with Data-centric Principles”
ICML 2024 Workshop AI4Science, 2024. [Paper]

J. Xu*, W. Chen, Y. Zhao, Y. Wei
“Transferable and Principled Efficiency for Open-Vocabulary Segmentation”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024. [Paper] [Code]

W. Chen, J. Wu, Z. Wang, B. Hanin
“Principled Architecture-aware Scaling of Hyperparameters”
International Conference on Learning Representations (ICLR), 2024. [Paper] [Code]

S. Shen, L. Hou, Y. Zhou, N. Du, S. Longpre, J. Wei, H. Chung, B. Zoph, W. Fedus, X. Chen, T. Vu, Y. Wu, W. Chen, A. Webson, Y. Li, V. Zhao, H. Yu, K. Keutzer, T. Darrell, D. Zhou
“Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for Large Language Models”
International Conference on Learning Representations (ICLR), 2024. [Paper]

W. Chen#, W. Huang#, and Z. Wang
“No Free Lunch in Neural Architectures? A Joint Analysis of Expressivity, Convergence, and Generalization”
International Conference on Automated Machine Learning (AutoML-Conf), 2023. [Paper] [Code]

W. Chen, Y. Zhou, N. Du, Y. Huang, J. Laudon, Z. Chen, C. Cu
“Lifelong Language Pretraining with Distribution-Specialized Experts”
International Conference on Machine Learning (ICML), 2023. [Paper]

Y. Zhang, A. Kamath, Q. Wu, Z. Fan, W. Chen, Z. Wang, S. Chang, S. Liu, C. Hao
“Data-Model-Circuit Tri-design for Ultra-light Video Intelligence on Edge Devices”
IEEE Asia and South Pacific Design Automation Conference (ASP-DAC), 2022 [Paper]

W. Chen#, W. Huang#, X. Gong, B. Hanin, Z. Wang
“Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis”
Advances in Neural Information Processing Systems (NeurIPS), 2022. [Paper] [Code]

W. Chen, X. Du, F. Yang, L. Beyer, X. Zhai, T. Lin, H. Chen, J. Li, X. Song, Z. Wang, D. Zhou
“A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation”
European Conference on Computer Vision (ECCV), 2022. [Paper]

X. Gong, W. Chen, T. Chen, Z. Wang
Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022. [Paper] [Code]

M. Lu, X. Luo, T. Chen, W. Chen, D. Liu, Z. Wang
“Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, And No Retraining”
International Conference on Learning Representations (ICLR), 2022. (Spotlight) [Paper] [Code]

W. Chen, W. Huang, X. Du, X. Song, Z. Wang, D. Zhou
“Auto-Scaling Vision Transformers without Training”
International Conference on Learning Representations (ICLR), 2022. [Paper] [Code]

W. Chen, X. Gong, Z. Wang
“Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective”
International Conference on Learning Representations (ICLR), 2021. [Paper] [Code] [NSF News] [UT ECE News] [Project Webpage]

W. Chen, Z. Yu, S. Mello, S. Liu, J. Alvarez, Z. Wang, A. Anandkumar
“Contrastive Syn-to-Real Generalization”
International Conference on Learning Representations (ICLR), 2021. [Paper] [Code]

W. Chen, Z. Yu, Z. Wang, A. Anandkumar
“Automated Synthetic-to-Real Generalization”
International Conference on Machine Learning (ICML), 2020. [Paper] [Code]

X. Chen#, W. Chen#, T. Chen, Y. Yuan, C. Gong, K. Chen, Z. Wang
“Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training”
International Conference on Machine Learning (ICML), 2020. [Paper] [Code]

Y. Fu, W. Chen, H. Wang, H. Li, Y. Lin, Z. Wang
“AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks”
International Conference on Machine Learning (ICML), 2020. [Paper] [Code]

W. Chen, X. Gong, X. Liu, Q. Zhang, Y. Li, Z. Wang
“FasterSeg: Searching for Faster Real-time Semantic Segmentation”
International Conference on Learning Representations (ICLR), 2020. [Paper] [Code]

Y. Yuan, W. Chen, Y. Yang, Z. Wang
“In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification With Fast Approximated Triplet Loss and Label Distillation”
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020. [Paper] [Code]

Y. Yuan, W. Chen, T. Chen, Y. Yang, Z. Ren, Z. Wang, G. Hua
“Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification”
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020. [Paper] [Code]

T. Chen, S. Ding, J. Xie, Y. Yuan, W. Chen, Y. Yang, Z. Ren, Z. Wang
“ABD-Net: Attentive but Diverse Person Re-Identification”
IEEE International Conference on Computer Vision (ICCV), 2019. [Paper] [Code]

W. Chen#, Z. Jiang#, Z. Wang, K. Cui, X. Qian
“Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-high Resolution Images”
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (Oral) [Paper] [Code]

Preprint

T. Wu, W. Chen, Z. Zhang
“Training-free Design of Deep Networks as Ensembles of Clinical Experts”
medRxiv, 2024.03. 17.24304438 [Paper]