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Found 27 papers, 22 papers with code
Cross-Modal Contextualized Diffusion Models for Text-Guided Visual Generation and Editing
To address this issue, we propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample into forward and reverse processes.
RealCompo: Dynamic Equilibrium between Realism and Compositionality Improves Text-to-Image Diffusion Models
In this paper, we propose a new training-free and transferred-friendly text-to-image generation framework, namely RealCompo, which aims to leverage the advantages of text-to-image and layout-to-image models to enhance both realism and compositionality of the generated images.
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs
In this paper, we propose a brand new training-free text-to-image generation/editing framework, namely Recaption, Plan and Generate (RPG), harnessing the powerful chain-of-thought reasoning ability of multimodal LLMs to enhance the compositionality of text-to-image diffusion models.
Equivariant Graph Neural Operator for Modeling 3D Dynamics
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 particle mechanics.
Equivariant Flow Matching with Hybrid Probability Transport
The generation of 3D molecules requires simultaneously deciding the categorical features~(atom types) and continuous features~(atom coordinates).
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Our key innovation is to develop a multi-stage diffusion process.
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
MADiff: Offline Multi-agent Learning with Diffusion Models
To the best of our knowledge, MADiff is the first diffusion-based multi-agent offline RL framework, which behaves as both a decentralized policy and a centralized controller.
Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D
Generating desirable molecular structures in 3D is a fundamental problem for drug discovery.
Geometric Latent Diffusion Models for 3D Molecule Generation
Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries and advancing foundational science problems such as molecule design.
MUDiff: Unified Diffusion for Complete Molecule Generation
Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability
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 Networks (GNNs) over Neural Networks on node classification tasks.
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Current strategies use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route.
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation
GeoDiff treats each atom as a particle and learns to directly reverse the diffusion process (i. e., transforming from a noise distribution to stable conformations) as a Markov chain.
Generative Coarse-Graining of Molecular Conformations
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation.
Predicting Molecular Conformation via Dynamic Graph Score Matching
However, these non-bonded atoms may be proximal to each other in 3D space, and modeling their interactions is of crucial importance to accurately determine molecular conformations, especially for large molecules and multi-molecular complexes.
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program.
Learning Gradient Fields for Molecular Conformation Generation
We study a fundamental problem in computational chemistry known as molecular conformation generation, trying to predict stable 3D structures from 2D molecular graphs.
Learning Neural Generative Dynamics for Molecular Conformation Generation
Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
Towards Generalized Implementation of Wasserstein Distance in GANs
Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models.
Reciprocal Supervised Learning Improves Neural Machine Translation
Despite the recent success on image classification, self-training has only achieved limited gains on structured prediction tasks such as neural machine translation (NMT).
Energy-Based Imitation Learning
We tackle a common scenario in imitation learning (IL), where agents try to recover the optimal policy from expert demonstrations without further access to the expert or environment reward signals.
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator
Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data.
Ranked #7 on
Image Generation
on STL-10
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip
In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit-Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme.
A Graph to Graphs Framework for Retrosynthesis Prediction
A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a. k. a.
Ranked #15 on
Single-step retrosynthesis
on USPTO-50k
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention.
Ranked #1 on
Molecular Graph Generation
on MOSES
