3 citations · 5 across the 9 of their papers we have counts for
9 papers
DUET: A Diversity-Quality Duet of Distillation Experts for Two-Step Video Generation
Zian Li, Litong Gong, Borui Liao +6
Diffusion models have enabled high-quality video generation in recent years, but the high cost of iterative sampling hinders their practical deployment. Few-step distillation allev…
Toward Better Geometric Representations for Molecule Generative Models
Shaoheng Yan, Zian Li, Cai Zhou +3
Geometric representation-conditioned molecule generation provides an effective paradigm that decouples molecule representation modeling from structure generation. By decoupling mol…
FlashMol: High-Quality Molecule Generation in as Few as Four Steps
Xinyuan Wei, Zian Li, Shaoheng Yan +2
Generating chemically valid 3D molecular conformations is critical for computational drug discovery. Classical diffusion-based models like GeoLDM perform well but require hundreds…
Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation
Cai Zhou, Zijie Chen, Zian Li +7
Many generative tasks in chemistry and science involve distributions invariant to group symmetries (e.g., permutation and rotation). A common strategy enforces invariance and equiv…
CanvasMAR: Improving Masked Autoregressive Video Prediction With Canvas
Zian Li, Muhan Zhang
Masked autoregressive models (MAR) have emerged as a powerful paradigm for image and video generation, combining the flexibility of masked modeling with the expressiveness of conti…
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining
Shaoheng Yan, Zian Li, Muhan Zhang
The pretraining-finetuning paradigm has powered major advances in domains such as natural language processing and computer vision, with representative examples including masked lan…
Geometric Representation Condition Improves Equivariant Molecule Generation
Zian Li, Cai Zhou, Xiyuan Wang +2
Recent advances in molecular generative models have demonstrated great promise for accelerating scientific discovery, particularly in drug design. However, these models often strug…
On the Completeness of Invariant Geometric Deep Learning Models
Zian Li, Xiyuan Wang, Shijia Kang +1
Invariant models, one important class of geometric deep learning models, are capable of generating meaningful geometric representations by leveraging informative geometric features…
Is Distance Matrix Enough for Geometric Deep Learning?
Zian Li, Xiyuan Wang, Yinan Huang +1
Graph Neural Networks (GNNs) are often used for tasks involving the 3D geometry of a given graph, such as molecular dynamics simulation. While incorporating Euclidean distance into…