210 citations · 727 across the 35 of their papers we have counts for
35 papers
Lyapunov Guidance: A Unified Framework for Stabilizing Generative Flows
Jingdong Zhang, Xinze Li, Yize Jiang +3
Flow matching has emerged as an effective framework for learning complex data distributions, but adapting pretrained flow models to new tasks often requires computationally expensi…
Unified Energy for Invariant and Independent Decoding in Diffusion Language Models
Yuchen Yan, Minkai Xu, Zaiquan Yang +1
Diffusion Language Models (DLMs) enable parallel text generation by iteratively denoising a full sequence, offering attractive flexibility compared to auto-regressive (AR) decoding…
Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
Juntong Shi, Brian L. Trippe, Jure Leskovec +2
Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation quality compared to autoregressi…
Inference-Time Scaling of Diffusion Language Models via Trajectory Refinement
Meihua Dang, Jiaqi Han, Minkai Xu +3
Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-scale training. However, inferenc…
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges
Sitao Luan, Chenqing Hua, Qincheng Lu +11
Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to be the main reason for the superiority of…
Consistency Flow Matching: Defining Straight Flows with Velocity Consistency
Ling Yang, Zixiang Zhang, Zhilong Zhang +6
Flow matching (FM) is a general framework for defining probability paths via Ordinary Differential Equations (ODEs) to transform between noise and data samples. Recent approaches a…
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization
Siyi Gu, Minkai Xu, Alexander Powers +6
Generating ligand molecules for specific protein targets, known as structure-based drug design, is a fundamental problem in therapeutics development and biological discovery. Recen…
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Ling Yang, Zhaochen Yu, Tianjun Zhang +5
We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs).…
Contextualized Diffusion Models for Text-Guided Image and Video Generation
Ling Yang, Zhilong Zhang, Zhaochen Yu +4
Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. Nevertheless, prevailing text-guided visual diffusion m…
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models
Xinchen Zhang, Ling Yang, Yaqi Cai +8
Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositio…
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
Ling Yang, Zhaochen Yu, Chenlin Meng +3
Diffusion models have exhibit exceptional performance in text-to-image generation and editing. However, existing methods often face challenges when handling complex text prompts th…
Equivariant Graph Neural Operator for Modeling 3D Dynamics
Minkai Xu, Jiaqi Han, Aaron Lou +6
Modeling the complex three-dimensional (3D) dynamics of relational systems is an important problem in the natural sciences, with applications ranging from molecular simulations to…
Equivariant Flow Matching with Hybrid Probability Transport
Yuxuan Song, Jingjing Gong, Minkai Xu +5
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates). Deep generative models, especially…
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Yiming Wang, Yuxuan Song, Yiqun Wang +4
Retrosynthesis poses a key challenge in biopharmaceuticals, aiding chemists in finding appropriate reactant molecules for given product molecules. With reactants and products repre…
Scaling Riemannian Diffusion Models
Aaron Lou, Minkai Xu, Stefano Ermon
Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds. Unfortunately, the additional geometric com…
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
Ling Yang, Ye Tian, Minkai Xu +7
GNN-to-MLP distillation aims to utilize knowledge distillation (KD) to learn computationally-efficient multi-layer perceptron (student MLP) on graph data by mimicking the output re…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
Bo Qiang, Yuxuan Song, Minkai Xu +5
Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate m…
Geometric Latent Diffusion Models for 3D Molecule Generation
Minkai Xu, Alexander Powers, Ron Dror +2
Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries and advancing foundational science problems such as mol…
MADiff: Offline Multi-agent Learning with Diffusion Models
Zhengbang Zhu, Minghuan Liu, Liyuan Mao +5
Offline reinforcement learning (RL) aims to learn policies from pre-existing datasets without further interactions, making it a challenging task. Q-learning algorithms struggle wit…
MUDiff: Unified Diffusion for Complete Molecule Generation
Chenqing Hua, Sitao Luan, Minkai Xu +4
Molecule generation is a very important practical problem, with uses in drug discovery and material design, and AI methods promise to provide useful solutions. However, existing me…
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability
Sitao Luan, Chenqing Hua, Minkai Xu +6
Homophily principle, i.e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Netwo…
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Songtao Liu, Zhengkai Tu, Minkai Xu +6
Retrosynthetic planning aims to devise a complete multi-step synthetic route from starting materials to a target molecule. Current strategies use a decoupled approach of single-ste…
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
Minkai Xu, Lantao Yu, Yang Song +3
Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machi…