11 datasets found

A compact representation that combines a set of coarse shape proxies with learned neural textures to encode both medium and fine scale geometry as well as view-dependent...

Generative adversarial nets (GANs) are a class of deep learning models that consist of two neural networks: a generator and a discriminator.

Diffusion models currently stand as the predominant approach to generative modeling in audio and image domains.

We use the promoter DNA sequence dataset containing 100k promoter sequences with the corresponding transcription initiation signal profiles.

We demonstrate the effectiveness of our method on the discrete generation problem by instantiating SFM on the manifold of categorical distributions whose geometric properties...

We apply SFM to diverse downstream discrete generation tasks across different domains including computer vision, natural language processing, and bioinformatics to demonstrate...

The dataset for the experiments comes from two dimensional turbulent flow simulated using the Lattice Boltzmann Method. The dataset consists of 1500 images of velocity fields,...

We use the preprocessed binarized MNIST dataset from [49] which has a split of 50k/10k/10k.

Word2Vec is a distributed word embedding generator that uses an artificial neural network to learn dense vector representations of words.

Dataset used for population synthesis and generative modeling.

Denoising diffusion models have become ubiquitous for generative modeling. The core idea is to transport the data distribution to a Gaussian by using a diffusion. Approximate...