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09/12/2023
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
Diffusion models have revolutionized text-to-image generation with its e...
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05/11/2023
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Deep generative models have recently achieved superior performance in 3D...
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03/06/2023
3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction
Rich data and powerful machine learning models allow us to design drugs ...
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11/20/2022
Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation
Learning new task-specific skills from a few trials is a fundamental cha...
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05/15/2022
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
Deep learning has achieved tremendous success in designing novel chemica...
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05/15/2022
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Deep generative models have achieved tremendous success in designing nov...
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03/28/2022
Equivariant Point Cloud Analysis via Learning Orientations for Message Passing
Equivariance has been a long-standing concern in various fields ranging ...
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03/22/2022
Provable Constrained Stochastic Convex Optimization with XOR-Projected Gradient Descent
Provably solving stochastic convex optimization problems with constraint...
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03/20/2022
A 3D Molecule Generative Model for Structure-Based Drug Design
We study a fundamental problem in structure-based drug design – generati...
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01/28/2022
Directed Weight Neural Networks for Protein Structure Representation Learning
A protein performs biological functions by folding to a particular 3D st...
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12/05/2021
Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
Muilti-modality data are ubiquitous in biology, especially that we have ...
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10/16/2021
Fast Projection onto the Capped Simplex withApplications to Sparse Regression in Bioinformatics
We consider the problem of projecting a vector onto the so-called k-capp...
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06/11/2021
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Graph neural networks (GNNs) have achieved tremendous success on multipl...
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06/10/2021
Neural Higher-order Pattern (Motif) Prediction in Temporal Networks
Dynamic systems that consist of a set of interacting elements can be abs...
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01/01/2021
Quantifying spatial homogeneity of urban road networks via graph neural networks
The spatial homogeneity of an urban road network (URN) measures whether ...
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12/10/2013
