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ORCIDTan M. Nguyen
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- affiliation: University of California, Los Angeles, CA, USA
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Journal Articles
- 2023
[j3]Son Nguyen
, Cuong Tran Manh, Trung Kien Tran, Tan M. Nguyen, Thu-Trang Nguyen, Kien-Tuan Ngo, Hieu Dinh Vo:
ARist: An effective API argument recommendation approach. J. Syst. Softw. 204: 111786 (2023)- 2022
- [j2]Bao Wang, Tan M. Nguyen, Tao Sun, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. SIAM J. Imaging Sci. 15(2): 738-761 (2022) - 2020
- [j1]Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan M. Nguyen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin:
Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference. IEEE J. Sel. Top. Signal Process. 14(4): 623-633 (2020)
Conference and Workshop Papers
- 2024
- [c18]Tuan Nguyen, Hirotada Honda, Takashi Sano, Vinh Nguyen, Shugo Nakamura, Tan Minh Nguyen:
From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach. AISTATS 2024: 2710-2718 - [c17]Hien Dang, Tho Tran Huu, Tan Minh Nguyen, Nhat Ho:
Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders. ICLR 2024 - [c16]Hien Dang, Tho Tran Huu, Tan Minh Nguyen, Nhat Ho:
Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Features Model. ICML 2024 - [c15]Tam Minh Nguyen, César A. Uribe, Tan Minh Nguyen, Richard G. Baraniuk:
PIDformer: Transformer Meets Control Theory. ICML 2024 - 2023
- [c14]Khang Nguyen, Hieu Nong, Khuong Nguyen, Tan M. Nguyen, Vinh Nguyen:
DeepGRAND: Deep Graph Neural Diffusion. ACSSC 2023: 1044-1048 - [c13]Tan M. Nguyen, Tam Nguyen, Long Bui, Hai Do, Duy Khuong Nguyen, Dung D. Le, Hung Tran-The, Nhat Ho, Stanley J. Osher, Richard G. Baraniuk:
A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys. ICASSP 2023: 1-5 - [c12]Tan Minh Nguyen, Tam Minh Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
A Primal-Dual Framework for Transformers and Neural Networks. ICLR 2023 - [c11]Khai Nguyen, Tongzheng Ren, Huy Nguyen, Litu Rout, Tan Minh Nguyen, Nhat Ho:
Hierarchical Sliced Wasserstein Distance. ICLR 2023 - [c10]Hien Dang, Tho Tran Huu, Stanley J. Osher, Hung Tran-The, Nhat Ho, Tan Minh Nguyen:
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data. ICML 2023: 6873-6947 - [c9]Khang Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, Nhat Ho, Stanley J. Osher, Tan Minh Nguyen:
Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature. ICML 2023: 25956-25979 - 2022
- [c8]Matthew Thorpe, Tan Minh Nguyen, Hedi Xia, Thomas Strohmer, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
GRAND++: Graph Neural Diffusion with A Source Term. ICLR 2022 - [c7]Tam Minh Nguyen, Tan Minh Nguyen, Dung D. D. Le, Duy Khuong Nguyen, Viet-Anh Tran, Richard G. Baraniuk, Nhat Ho, Stanley J. Osher:
Improving Transformers with Probabilistic Attention Keys. ICML 2022: 16595-16621 - [c6]Tan Minh Nguyen, Richard G. Baraniuk, Robert M. Kirby, Stanley J. Osher, Bao Wang:
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization. MSML 2022: 189-204 - 2021
- [c5]Tran Manh Cuong, Trung Kien Tran, Tan M. Nguyen, Thu-Trang Nguyen, Son Nguyen, Hieu Dinh Vo:
API parameter recommendation based on language model and program analysis. APSEC 2021: 492-496 - [c4]Hedi Xia, Vai Suliafu, Hangjie Ji, Tan M. Nguyen, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
Heavy Ball Neural Ordinary Differential Equations. NeurIPS 2021: 18646-18659 - [c3]Tan M. Nguyen, Vai Suliafu, Stanley J. Osher, Long Chen, Bao Wang:
FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention. NeurIPS 2021: 29449-29463 - 2020
- [c2]Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. NeurIPS 2020 - [c1]Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
MomentumRNN: Integrating Momentum into Recurrent Neural Networks. NeurIPS 2020
Informal and Other Publications
- 2024
- [i27]Tam Nguyen, César A. Uribe, Tan M. Nguyen, Richard G. Baraniuk:
PIDformer: Transformer Meets Control Theory. CoRR abs/2402.15989 (2024) - [i26]Viet-Hoang Tran, Trang Pham, Tho Tran, Tam Le, Tan M. Nguyen:
Tree-Sliced Wasserstein Distance on a System of Lines. CoRR abs/2406.13725 (2024) - [i25]Rachel S. Y. Teo, Tan M. Nguyen:
Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis. CoRR abs/2406.13762 (2024) - [i24]Stefan K. Nielsen, Laziz U. Abdullaev, Rachel S. Y. Teo, Tan M. Nguyen:
Elliptical Attention. CoRR abs/2406.13770 (2024) - [i23]Tan M. Nguyen, Tam Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
A Primal-Dual Framework for Transformers and Neural Networks. CoRR abs/2406.13781 (2024) - [i22]Hoang V. Tran, Thieu N. Vo, Tho H. Tran, An T. Nguyen, Tan Minh Nguyen:
Monomial Matrix Group Equivariant Neural Functional Networks. CoRR abs/2409.11697 (2024) - 2023
- [i21]Hien Dang, Tho Tran, Tan M. Nguyen, Stanley J. Osher, Hung Tran-The, Nhat Ho:
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data. CoRR abs/2301.00437 (2023) - [i20]Son Nguyen, Cuong Tran Manh, Trung Kien Tran, Tan M. Nguyen, Thu-Trang Nguyen, Kien-Tuan Ngo, Hieu Dinh Vo:
ARIST: An Effective API Argument Recommendation Approach. CoRR abs/2306.06620 (2023) - [i19]Tuan Nguyen, Tam Nguyen, Vinh Nguyen, Tan M. Nguyen:
p-Laplacian Transformer. CoRR abs/2311.03235 (2023) - [i18]Tuan Nguyen, Tan M. Nguyen, Hirotada Honda, Takashi Sano, Vinh Nguyen, Shugo Nakamura:
From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach. CoRR abs/2311.03260 (2023) - [i17]Tam Nguyen, Tan M. Nguyen, Richard G. Baraniuk:
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals. CoRR abs/2312.00751 (2023) - 2022
- [i16]Tan M. Nguyen, Minh Pham, Tam Nguyen, Khai Nguyen, Stanley J. Osher, Nhat Ho:
Transformer with Fourier Integral Attentions. CoRR abs/2206.00206 (2022) - [i15]Tan M. Nguyen, Richard G. Baraniuk, Robert M. Kirby, Stanley J. Osher, Bao Wang:
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization. CoRR abs/2208.00579 (2022) - [i14]Anh Do, Duy Dinh, Tan M. Nguyen, Khuong Nguyen, Stanley J. Osher, Nhat Ho:
Improving Generative Flow Networks with Path Regularization. CoRR abs/2209.15092 (2022) - [i13]Xing Han, Tongzheng Ren, Tan Minh Nguyen, Khai Nguyen, Joydeep Ghosh, Nhat Ho:
Robustify Transformers with Robust Kernel Density Estimation. CoRR abs/2210.05794 (2022) - 2021
- [i12]Tan M. Nguyen, Vai Suliafu, Stanley J. Osher, Long Chen, Bao Wang:
FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention. CoRR abs/2108.02347 (2021) - [i11]Hedi Xia, Vai Suliafu, Hangjie Ji, Tan M. Nguyen, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
Heavy Ball Neural Ordinary Differential Equations. CoRR abs/2110.04840 (2021) - [i10]Bao Wang, Hedi Xia, Tan M. Nguyen, Stanley J. Osher:
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies. CoRR abs/2110.07034 (2021) - [i9]Tam Nguyen, Tan M. Nguyen, Dung Le, Khuong Nguyen, Anh Tran, Richard G. Baraniuk, Nhat Ho, Stanley J. Osher:
Transformer with a Mixture of Gaussian Keys. CoRR abs/2110.08678 (2021) - 2020
- [i8]Bao Wang, Tan M. Nguyen, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. CoRR abs/2002.10583 (2020) - [i7]Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
MomentumRNN: Integrating Momentum into Recurrent Neural Networks. CoRR abs/2006.06919 (2020) - [i6]Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar:
Neural Networks with Recurrent Generative Feedback. CoRR abs/2007.09200 (2020) - 2019
- [i5]Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan M. Nguyen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin:
Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference. CoRR abs/1907.04523 (2019) - [i4]Yujia Huang, Sihui Dai, Tan M. Nguyen, Richard G. Baraniuk, Anima Anandkumar:
Out-of-Distribution Detection Using Neural Rendering Generative Models. CoRR abs/1907.04572 (2019) - [i3]Tan M. Nguyen, Nan Ye, Peter L. Bartlett:
Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees. CoRR abs/1910.03742 (2019) - [i2]Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar:
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers. CoRR abs/1912.03978 (2019) - 2018
- [i1]Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk:
Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. CoRR abs/1811.02657 (2018)
Coauthor Index
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