Categories: FAANG

Generating Molecular Conformers with Manifold Diffusion Fields

This paper was accepted at Generative AI and Biology workshop at NeurIPS 2023.
In this paper we tackle the problem of generating a molecule conformation in 3D space given its 2D structure. We approach this problem through the lens of a diffusion model for functions in Riemannian Manifolds. Our approach is simple and scalable, and obtains results that are on par with state-of-the-art while making no assumptions about the explicit structure of molecules.

HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion

Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (e.g., xyz) to signals (e.g., signed distances), have shown remarkable promise as a high-fidelity and compact representation. However, the lack of a regular and explicit grid structure also makes it challenging to apply generative modeling directly…

September 26, 2023

In "FAANG"

TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation

Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations. Consequently, techniques such as binary time-distillation (BTD) have been proposed to reduce the number of network calls for a fixed architecture. In this paper, we introduce TRAnsitive Closure Time-distillation (TRACT), a new…

April 4, 2023

In "FAANG"

Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction

3D-aware image synthesis encompasses a variety of tasks, such as scene generation and novel view synthesis from images. Despite numerous task-specific methods, developing a comprehensive model remains challenging. In this paper, we present SSDNeRF, a unified approach that employs an expressive diffusion model to learn a generalizable prior of neural…

September 27, 2023

In "FAANG"

AI Generated Robotic Content