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DiffDock: Diffusion steps, twists, and turns for molecular docking
DiffDock: Diffusion steps, twists, and turns for molecular docking. -
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular D...
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution. -
DPM-Solver++
The dataset used in the paper is DPM-Solver++ -
Exact Diffusion Inversion via Bi-directional Integration Approximation
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a pre-trained model to generate images. -
Synthetic Data from Diffusion Models Improves ImageNet Classification
Large-scale text-to-image diffusion models can be fine-tuned to produce class-conditional models with SOTA FID and Inception Score on ImageNet. -
SIN3DM: LEARNING A DIFFUSION MODEL FROM A SINGLE 3D TEXTURED SHAPE
Synthesizing novel 3D models that resemble the input example has long been pursued by graphics artists and machine learning researchers. In this paper, we present Sin3DM, a... -
Mix-of-Show: Decentralized low-rank adaptation for multi-concept customizatio...
Mix-of-Show: Decentralized low-rank adaptation for multi-concept customization of diffusion models. -
BiRoDiff: Diffusion policies for bipedal robot locomotion on unseen terrains
Offline dataset for training a diffusion policy controller for bipedal robot locomotion on multiple terrains. -
Motion Planning Diffusion Model
The dataset used in the paper is a set of images and videos of objects moving in different environments, used for training and testing the Motion Planning Diffusion model. -
Projected Generative Diffusion Models
The dataset used in the paper is a large dataset of images, videos, and other data used for training and testing the Projected Generative Diffusion Models (PGDM) and other... -
Diffusion Random Feature Model
Diffusion probabilistic models have been successfully used to generate data from noise. However, most diffusion models are computationally expensive and difficult to interpret... -
eDiff-I: Text-to-image diffusion models with an ensemble of expert denoisers
Text-to-image diffusion models with an ensemble of expert denoisers. -
Diffusion Boosted Trees
Combining the merits of both denoising diffusion probabilistic models and gradient boosting, the diffusion boosting paradigm is introduced for tackling supervised learning... -
EIGENFOLD: Generative Protein Structure Prediction with Diffusion Models
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational... -
LAION2B: An open large-scale dataset for training next generation image-text ...
The LAION2B dataset is a massive 'in the wild' dataset used for training foundation diffusion models. -
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task R...
Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models... -
Object Saliency Noise for Conditional Image Generation with Diffusion Models
Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation. -
3DShape2VecSet
3DShape2VecSet: A 3D shape representation for neural fields and generative diffusion models -
Boosting the performance of anomalous diffusion classifiers with the proper c...
Understanding and identifying different types of single molecules' diffusion that occur in a broad range of systems (including living matter) is extremely important, as it can...
