Dnabert-s: Learning species-aware dna embedding with genome foundation models Z Zhou*, W Wu*, H Ho, J Wang, L Shi, RV Davuluri, Z Wang, H Liu arXiv preprint arXiv:2402.08777 2, 2024 | 23 | 2024 |
On statistical rates and provably efficient criteria of latent diffusion transformers (dits) JYC Hu*, W Wu*, Z Song, H Liu Advances in Neural Information Processing Systems 38 (NeurIPS) 2024, 2024 | 21 | 2024 |
On statistical rates of conditional diffusion transformers: Approximation, estimation and minimax optimality JYC Hu*, W Wu*, YC Lee*, YC Huang*, M Chen, H Liu International Conference on Learning Representations (ICLR) 2025, 2024 | 6 | 2024 |
Instance-aware model ensemble with distillation for unsupervised domain adaptation W Wu, J Fan, T Chen, H Ye, B Zhang, B Li arXiv preprint arXiv:2211.08106, 2022 | 3 | 2022 |
DNABERT-S: Pioneering species differentiation with species-aware DNA embeddings Z Zhou, W Wu, H Ho, J Wang, L Shi, RV Davuluri, Z Wang, H Liu ArXiv, arXiv: 2402.08777 v3, 2024 | 1 | 2024 |
GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies Z Zhou, R Riley, S Kautsar, W Wu, R Egan, S Hofmeyr, ... bioRxiv, 2025.01. 30.635558, 2025 | | 2025 |
Transformers are Deep Optimizers: Provable In-Context Learning for Deep Model Training W Wu*, M Su*, JYC Hu*, Z Song, H Liu arXiv preprint arXiv:2411.16549, 2024 | | 2024 |