6 papers · 1 filter
D-IMPL: A Diffusion-based Solver for Parameterized BBOs
Yang Hu, Na Li
Diffusion models have demonstrated strong power in generative modeling tasks across multiple domains, exhibiting a remarkable capability of learning complex distributions from samp…
ConvergeFlow: Language Flow with Provable Convergence to Token Embeddings
Na Li, Yuchen Jiao, Changxiao Cai +1
Recent advances in continuous diffusion and flow-based language models (LMs) have achieved performance competitive with discrete LMs. However, existing continuous frameworks still…
Provable diffusion-based posterior sampling for linear inverse problems via DDIM
Yuchen Jiao, Na Li, Changxiao Cai +2
Diffusion-based methods have achieved remarkable empirical success in solving inverse problems. However, many existing posterior samplers either lack rigorous theoretical guarantee…
Spectral Ghost in Representation Learning: from Component Analysis to Self-Supervised Learning
Bo Dai, Na Li, Dale Schuurmans
Self-supervised learning (SSL) has improved empirical performance by unleashing the power of unlabeled data for practical applications. Specifically, SSL extracts the representatio…
Spectral Representation-based Reinforcement Learning
Chenxiao Gao, Haotian Sun, Na Li +2
In real-world applications with large state and action spaces, reinforcement learning (RL) typically employs function approximations to represent core components like the policies,…
DiffKD-DCIS: Predicting Upgrade of Ductal Carcinoma In Situ with Diffusion Augmentation and Knowledge Distillation
Tao Li, Qing Li, Na Li +1
Accurately predicting the upgrade of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) is crucial for surgical planning. However, traditional deep learning methods…