Deep probabilistic analysis of single-cell and spatial omics data
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Updated
Jun 3, 2024 - Python
Deep probabilistic analysis of single-cell and spatial omics data
Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019
Geometric Latent Diffusion Models for 3D Molecule Generation
Neural Relation Understanding: neural cardinality estimators for tabular data
State-of-the-art neural cardinality estimators for join queries
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling @ INTERSPEECH 2021
Pytorch implementation of stochastically quantized variational autoencoder (SQ-VAE)
Papers and resources on Controllable Generation using Diffusion Models, including ControlNet, DreamBooth, T2I-Adapter, IP-Adapter.
Unofficial Pytorch Implementation of WaveGrad2
Roundtrip: density estimation with deep generative neural networks
Kernel Change-point Detection with Auxiliary Deep Generative Models (ICLR 2019 paper)
Implementation of NeurIPS 19 paper: Paraphrase Generation with Latent Bag of Words
A minimal pytorch implementation of VAE, IWAE, MIWAE
An implementation of Restricted Boltzmann Machine in Pytorch
PyTorch implementation of DiffRoll, a diffusion-based generative automatic music transcription (AMT) model
Implementation of NeurIPS 20 paper: Latent Template Induction with Gumbel-CRFs
A General Causal Inference Framework by Encoding Generative Modeling
A pytorch implementation for FACE: A Normalizing Flow based Cardinality Estimator
𝒫robabilistic modeling of RNA velocity ⬱
PyTorch implementation of the paper "NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity." (NeurIPS 2020)
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