A Collection of Variational Autoencoders (VAE) in PyTorch.
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Updated
May 6, 2024 - Python
A Collection of Variational Autoencoders (VAE) in PyTorch.
collections of examples of gumbel softmax tricks in optimization & deep learning
Python library for the differentiable hypergeometric distribution
Jittor reimplementation of DiverseSampling (MM22)
Official project of DiverseSampling (ACMMM2022 Paper)
Code acompanying the paper Developmentally motivated emergence of compositional communication via template transfer
Code for "Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection", ECCV 2022
Codes for "Deep Joint Source-Channel Coding for Wireless Image Transmission with Adaptive Rate Control", ICASSP 2022
Implementation of the Gumbel-Sigmoid distribution in PyTorch.
Pytorch implementation of stochastically quantized variational autoencoder (SQ-VAE)
Source code for the NAACL 2019 paper "SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression"
De novo Drug Design via Binary Representations of SMILES for avoiding the Posterior Collapse Problem (BIBM 2021)
Code for TACL 2022 paper on Data-to-text Generation with Variational Sequential Planning
Implementation of NeurIPS 19 paper: Paraphrase Generation with Latent Bag of Words
A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation
GAN-Based Text Generation
Black-box spike and slab variational inference, example with linear models
Pytorch implementation of JointVAE, a framework for disentangling continuous and discrete factors of variation 🌟
Keras, Tensorflow eager execution implementation of Categorical Variational Autoencoder
The implementation of Gumbel softmax reparametrization trick for discrete VAE
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