Deep probabilistic analysis of single-cell and spatial omics data
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Updated
May 30, 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
Neural Relation Understanding: neural cardinality estimators for tabular data
Geometric Latent Diffusion Models for 3D Molecule Generation
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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