Tan M. Nguyen

According to our database, Tan M. Nguyen authored at least 31 papers between 2018 and 2024.

Collaborative distances:

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

2024

PIDformer: Transformer Meets Control Theory.

Tam Nguyen, César A. Uribe, Tan M. Nguyen, Richard G. Baraniuk

CoRR, 2024

2023

ARist: An effective API argument recommendation approach.

Son Nguyen, Cuong Tran Manh, Trung Kien Tran, Tan M. Nguyen, Thu-Trang Nguyen, Kien-Tuan Ngo, Hieu Dinh Vo

J. Syst. Softw., October, 2023

Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals.

Tam Nguyen, Tan M. Nguyen, Richard G. Baraniuk

CoRR, 2023

From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach.

Tuan Nguyen, Tan M. Nguyen, Hirotada Honda, Takashi Sano, Vinh Nguyen, Shugo Nakamura

CoRR, 2023

p-Laplacian Transformer.

Tuan Nguyen, Tam Nguyen, Vinh Nguyen, Tan M. Nguyen

CoRR, 2023

Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature.

Khang Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, Nhat Ho, Stanley J. Osher, Tan Minh Nguyen

Proceedings of the International Conference on Machine Learning, 2023

Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data.

Hien Dang, Tho Tran Huu, Stanley J. Osher, Hung Tran-The, Nhat Ho, Tan Minh Nguyen

Proceedings of the International Conference on Machine Learning, 2023

Hierarchical Sliced Wasserstein Distance.

Khai Nguyen, Tongzheng Ren, Huy Nguyen, Litu Rout, Tan Minh Nguyen, Nhat Ho

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Primal-Dual Framework for Transformers and Neural Networks.

Tan Minh Nguyen, Tam Minh Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys.

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

Proceedings of the IEEE International Conference on Acoustics, 2023

DeepGRAND: Deep Graph Neural Diffusion.

Khang Nguyen, Hieu Nong, Khuong Nguyen, Tan M. Nguyen, Vinh Nguyen

Proceedings of the 57th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2023, Pacific Grove, CA, USA, October 29, 2023

2022

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent.

Bao Wang, Tan M. Nguyen, Tao Sun, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher

SIAM J. Imaging Sci., 2022

Robustify Transformers with Robust Kernel Density Estimation.

Xing Han, Tongzheng Ren, Tan Minh Nguyen, Khai Nguyen, Joydeep Ghosh, Nhat Ho

CoRR, 2022

Improving Generative Flow Networks with Path Regularization.

Anh Do, Duy Dinh, Tan M. Nguyen, Khuong Nguyen, Stanley J. Osher, Nhat Ho

CoRR, 2022

Transformer with Fourier Integral Attentions.

Tan M. Nguyen, Minh Pham, Tam Nguyen, Khai Nguyen, Stanley J. Osher, Nhat Ho

CoRR, 2022

Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization.

Tan Minh Nguyen, Richard G. Baraniuk, Robert M. Kirby, Stanley J. Osher, Bao Wang

Proceedings of the Mathematical and Scientific Machine Learning, 2022

Improving Transformers with Probabilistic Attention Keys.

Tam Minh Nguyen, Tan Minh Nguyen, Dung D. D. Le, Duy Khuong Nguyen, Viet-Anh Tran, Richard G. Baraniuk, Nhat Ho, Stanley J. Osher

Proceedings of the International Conference on Machine Learning, 2022

GRAND++: Graph Neural Diffusion with A Source Term.

Matthew Thorpe, Tan Minh Nguyen, Hedi Xia, Thomas Strohmer, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Transformer with a Mixture of Gaussian Keys.

Tam Nguyen, Tan M. Nguyen, Dung Le, Khuong Nguyen, Anh Tran, Richard G. Baraniuk, Nhat Ho, Stanley J. Osher

CoRR, 2021

How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies.

Bao Wang, Hedi Xia, Tan M. Nguyen, Stanley J. Osher

CoRR, 2021

Heavy Ball Neural Ordinary Differential Equations.

Hedi Xia, Vai Suliafu, Hangjie Ji, Tan M. Nguyen, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

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

FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention.

Tan M. Nguyen, Vai Suliafu, Stanley J. Osher, Long Chen, Bao Wang

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

API parameter recommendation based on language model and program analysis.

Tran Manh Cuong, Trung Kien Tran, Tan M. Nguyen, Thu-Trang Nguyen, Son Nguyen, Hieu Dinh Vo

Proceedings of the 28th Asia-Pacific Software Engineering Conference, 2021

2020

Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference.

Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan M. Nguyen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin

IEEE J. Sel. Top. Signal Process., 2020

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent.

Bao Wang, Tan M. Nguyen, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher

CoRR, 2020

MomentumRNN: Integrating Momentum into Recurrent Neural Networks.

Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Neural Networks with Recurrent Generative Feedback.

Yujia Huang, James Gornet, Sihui Dai, Zhiding Yu, Tan M. Nguyen, Doris Y. Tsao, Anima Anandkumar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers.

Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar

CoRR, 2019

Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees.

Tan M. Nguyen, Nan Ye, Peter L. Bartlett

CoRR, 2019

Out-of-Distribution Detection Using Neural Rendering Generative Models.

Yujia Huang, Sihui Dai, Tan M. Nguyen, Richard G. Baraniuk, Anima Anandkumar

CoRR, 2019

2018

Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning.

Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk

CoRR, 2018