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)

Out-of-Distribution Detection Using Neural Rendering Generative Models. - Yujia Huang; Sihui Dai; Tan M. Nguyen; 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)

InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers. - Tan M. Nguyen; Animesh Garg; Richard G. Baraniuk; Anima Anandkumar (CoRR, 2019)

Neural Networks with Recurrent Generative Feedback. - Yujia Huang; James Gornet; Sihui Dai; Zhiding Yu; Tan M. Nguyen; Doris Y. Tsao; Anima Anandkumar (NeurIPS, 2020)

MomentumRNN: Integrating Momentum into Recurrent Neural Networks. - Tan M. Nguyen; Richard G. Baraniuk; Andrea L. Bertozzi; Stanley J. Osher; Bao Wang 001 (NeurIPS, 2020)

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. - Bao Wang 001; Tan M. Nguyen; Andrea L. Bertozzi; Richard G. Baraniuk; Stanley J. Osher (CoRR, 2020)

Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference. - Yue Wang 036; Jianghao Shen; Ting-Kuei Hu; Pengfei Xu 011; Tan M. Nguyen; Richard G. Baraniuk; Zhangyang Wang; Yingyan Lin (IEEE J. Sel. Top. Signal Process., 2020)

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 (APSEC, 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 001 (NeurIPS, 2021)

Heavy Ball Neural Ordinary Differential Equations. - Hedi Xia; Vai Suliafu; Hangjie Ji; Tan M. Nguyen; Andrea L. Bertozzi; Stanley J. Osher; Bao Wang 001 (NeurIPS, 2021)

How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies. - Bao Wang 001; Hedi Xia; Tan M. Nguyen; Stanley J. Osher (CoRR, 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)

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 001 (ICLR, 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 (ICML, 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 001 (MSML, 2022)

Transformer with Fourier Integral Attentions. - Tan M. Nguyen; Minh Pham 003; Tam Nguyen; Khai Nguyen; Stanley J. Osher; 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)

Robustify Transformers with Robust Kernel Density Estimation. - Xing Han; Tongzheng Ren; Tan Minh Nguyen; Khai Nguyen; Joydeep Ghosh; Nhat Ho (CoRR, 2022)

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. - Bao Wang 001; Tan M. Nguyen; Tao Sun 005; Andrea L. Bertozzi; Richard G. Baraniuk; Stanley J. Osher (SIAM J. Imaging Sci., 2022)

DeepGRAND: Deep Graph Neural Diffusion. - Khang Nguyen 002; Hieu Nong; Khuong Nguyen; Tan M. Nguyen; Vinh Nguyen (ACSSC, 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 (ICASSP, 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 (ICLR, 2023)

Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data. - Hien Dang 003; Tho Tran Huu; Stanley J. Osher; Hung Tran-The; Nhat Ho; Tan Minh Nguyen (ICML, 2023)

Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature. - Khang Nguyen 002; Nong Minh Hieu; Vinh Duc Nguyen; Nhat Ho; Stanley J. Osher; Tan Minh Nguyen (ICML, 2023)

p-Laplacian Transformer. - Tuan Nguyen; Tam Nguyen; Vinh Nguyen; Tan M. Nguyen (CoRR, 2023)

Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals. - Tam Nguyen; Tan M. Nguyen; Richard G. Baraniuk (CoRR, 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., 2023)

From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach. - Tuan Nguyen; Hirotada Honda; Takashi Sano 002; Vinh Nguyen; Shugo Nakamura; Tan Minh Nguyen (AISTATS, 2024)

Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders. - Hien Dang 003; Tho Tran Huu; Tan Minh Nguyen; Nhat Ho (ICLR, 2024)

Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Features Model. - Hien Dang 003; Tho Tran Huu; Tan Minh Nguyen; Nhat Ho (ICML, 2024)

PIDformer: Transformer Meets Control Theory. - Tam Minh Nguyen; César A. Uribe; Tan Minh Nguyen; Richard G. Baraniuk (ICML, 2024)

Monomial Matrix Group Equivariant Neural Functional Networks. - Hoang V. Tran; Thieu N. Vo; Tho Huu; An Nguyen The; Tan Minh Nguyen (NeurIPS, 2024)

Revisiting Kernel Attention with Correlated Gaussian Process Representation. - Long Minh Bui; Tho Tran Huu; Duy Dinh; Tan Minh Nguyen; Trong Nghia Hoang (UAI, 2024)

PIDformer: Transformer Meets Control Theory. - Tam Nguyen; César A. Uribe; Tan M. Nguyen; Richard G. Baraniuk (CoRR, 2024)

Tree-Sliced Wasserstein Distance on a System of Lines. - Viet-Hoang Tran; Trang Pham; Tho Tran; Tam Le; Tan M. Nguyen (CoRR, 2024)

Unveiling the Hidden Structure of Self-Attention via Kernel Principal Component Analysis. - Rachel S. Y. Teo; Tan M. Nguyen (CoRR, 2024)

Elliptical Attention. - Stefan K. Nielsen; Laziz U. Abdullaev; Rachel S. Y. Teo; Tan M. Nguyen (CoRR, 2024)

A Primal-Dual Framework for Transformers and Neural Networks. - Tan M. Nguyen; Tam Nguyen; Nhat Ho; Andrea L. Bertozzi; Richard G. Baraniuk; Stanley J. Osher (CoRR, 2024)

Monomial Matrix Group Equivariant Neural Functional Networks. - Hoang V. Tran; Thieu N. Vo; Tho H. Tran; An T. Nguyen; Tan Minh Nguyen (CoRR, 2024)

Demystifying the Token Dynamics of Deep Selective State Space Models. - Thieu N. Vo; Tung D. Pham; Xin T. Tong; Tan Minh Nguyen (CoRR, 2024)

Equivariant Neural Functional Networks for Transformers. - Viet-Hoang Tran; Thieu N. Vo; An Nguyen The; Tho Tran Huu; Minh-Khoi Nguyen-Nhat; Thanh Tran; Duy-Tung Pham; Tan Minh Nguyen (CoRR, 2024)

Equivariant Polynomial Functional Networks. - Thieu N. Vo; Viet-Hoang Tran; Tho Tran Huu; An Nguyen The; Thanh Tran; Minh-Khoi Nguyen-Nhat; Duy-Tung Pham; Tan Minh Nguyen (CoRR, 2024)

A Clifford Algebraic Approach to E(n)-Equivariant High-order Graph Neural Networks. - Hoang-Viet Tran; Thieu N. Vo; Tho Tran Huu; Tan Minh Nguyen (CoRR, 2024)

MomentumSMoE: Integrating Momentum into Sparse Mixture of Experts. - Rachel S. Y. Teo; Tan M. Nguyen (CoRR, 2024)

An Attention-based Framework for Fair Contrastive Learning. - Stefan K. Nielsen; Tan M. Nguyen (CoRR, 2024)

Transformer Meets Twicing: Harnessing Unattended Residual Information. - Laziz U. Abdullaev; Tan Minh Nguyen (ICLR, 2025)

CAMEx: Curvature-aware Merging of Experts. - Viet Dung Nguyen; Minh Nguyen Hoang; Luc Q. Nguyen; Rachel S. Y. Teo; Tan Minh Nguyen; Linh Duy Tran (ICLR, 2025)

Tight Clusters Make Specialized Experts. - Stefan K. Nielsen; Rachel S. Y. Teo; Laziz U. Abdullaev; Tan Minh Nguyen (ICLR, 2025)

MoLEx: Mixture of Layer Experts for Fine-tuning with Sparse Upcycling. - Rachel S. Y. Teo; Tan Minh Nguyen (ICLR, 2025)

Spherical Tree-Sliced Wasserstein Distance. - Hoang V. Tran; Thanh T. Chu; Minh-Khoi Nguyen-Nhat; Huyen Trang Pham; Tam Le; Tan Minh Nguyen (ICLR, 2025)

Distance-Based Tree-Sliced Wasserstein Distance. - Hoang V. Tran; Minh-Khoi Nguyen-Nhat; Huyen Trang Pham; Thanh T. Chu; Tam Le; Tan Minh Nguyen (ICLR, 2025)

Equivariant Neural Functional Networks for Transformers. - Hoang V. Tran; Thieu Vo; An Nguyen The; Tho Tran Huu; Minh-Khoi Nguyen-Nhat; Thanh Tran; Duy-Tung Pham; Tan Minh Nguyen (ICLR, 2025)

Demystifying the Token Dynamics of Deep Selective State Space Models. - Thieu N. Vo; Duy-Tung Pham; Xin T. Tong; Tan Minh Nguyen (ICLR, 2025)

Learning and predicting dynamics of compositional multiphase mixtures using Graph Neural Networks. - Duc Thach Son Vu; Tan M. Nguyen; Weiqing Ren (J. Comput. Phys., 2025)

CAMEx: Curvature-aware Merging of Experts. - Dung V. Nguyen; Minh H. Nguyen; Luc Q. Nguyen; Rachel S. Y. Teo; Tan M. Nguyen; Linh Duy Tran (CoRR, 2025)

Distance-Based Tree-Sliced Wasserstein Distance. - Hoang V. Tran; Khoi N. M. Nguyen; Trang Pham; Thanh T. Chu; Tam Le; Tan M. Nguyen (CoRR, 2025)

MoLEx: Mixture of Layer Experts for Finetuning with Sparse Upcycling. - Rachel S. Y. Teo; Tan M. Nguyen (CoRR, 2025)

Spherical Tree-Sliced Wasserstein Distance. - Viet-Hoang Tran; Thanh T. Chu; Khoi N. M. Nguyen; Trang Pham; Tam Le; Tan M. Nguyen (CoRR, 2025)

Tree-Sliced Wasserstein Distance with Nonlinear Projection. - Thanh Tran; Viet-Hoang Tran; Thanh T. Chu; Trang Pham; Laurent El Ghaoui; Tam Le; Tan M. Nguyen (CoRR, 2025)

Resolving Memorization in Empirical Diffusion Model for Manifold Data in High-Dimensional Spaces. - Yang Lyu; Yuchun Qian; Tan Minh Nguyen; Xin T. Tong (CoRR, 2025)

Revisiting Transformers with Insights from Image Filtering. - Laziz U. Abdullaev; Maksim Tkachenko; Tan M. Nguyen (CoRR, 2025)

The Blessing and Curse of Dimensionality in Safety Alignment. - Rachel S. Y. Teo; Laziz U. Abdullaev; Tan M. Nguyen (CoRR, 2025)