← Authors

Toan Tran

18 papers · 2017–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (15) πŸƒ Academic Marathon (8) 🌈 Renaissance Researcher (8) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (49) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (7) πŸ† Grand Slam πŸ’Ž Century Club (18) ⚑ Prolific Year (6) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (5) πŸ—ƒοΈ Keyword Collector (73)

Conferences

NIPS (6) ICLR (4) AAAI (2) CVPR (2) ICCV (2) ACL (1) ICML (1)

Top co-authors

Dinh Phung (4) Gustavo Carneiro (3) Anh Tong (3) Anh Tran (3) Ian Reid (3) Trung Le (3) Quyen Tran (2) Atilim Gunes Baydin (2) Thanh Nguyen-Tang (2) Thanh-Toan Do (2)

Research topics

Privacy (1)

Keywords

image generation (3) domain generalization (3) deep learning (3) bayesian inference (2) transfer learning (2) domain adaptation (2) generative model (2) diffusion model (2) data augmentation (2) multi-task learning (1) adversarial learning (1) active learning (1) probabilistic inference (1) variational inference (1) catastrophic forgetting (1) model aggregation (1) unsupervised domain adaptation (1) incremental learning (1) expectation maximization (1) representation learning (1)

Papers

Boosting Multiple Views for pretrained-based Continual Learning ICLR 2025
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning ICLR 2025
CASUAL: Conditional Support Alignment for Domain Adaptation with Label Shift AAAI 2025
Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training ACL 2025
Low-Rank Adaptation in Multilinear Operator Networks for Security-Preserving Incremental Learning CVPR 2025
Supercharged One-step Text-to-Image Diffusion Models with Negative Prompts ICCV 2025
On Inference Stability for Diffusion Models AAAI 2024
Reducing Training Time in Cross-Silo Federated Learning Using Multigraph Topology ICCV 2023
KL Guided Domain Adaptation ICLR 2022
Learning Fractional White Noises in Neural Stochastic Differential Equations NIPS 2022
Distributionally Robust Fair Principal Components via Geodesic Descents ICLR 2022
Stochastic Multiple Target Sampling Gradient Descent NIPS 2022
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources NIPS 2021
Domain Invariant Representation Learning with Domain Density Transformations NIPS 2021
Exploiting Domain-Specific Features to Enhance Domain Generalization NIPS 2021
Bayesian Generative Active Deep Learning ICML 2019
A Theoretically Sound Upper Bound on the Triplet Loss for Improving the Efficiency of Deep Distance Metric Learning CVPR 2019
A Bayesian Data Augmentation Approach for Learning Deep Models NIPS 2017