thegialeo / Training-Invertible-Neural-Networks-as-Autoencoders
☆17Updated last year
Related projects ⓘ
Alternatives and complementary repositories for Training-Invertible-Neural-Networks-as-Autoencoders
- MintNet: Building Invertible Neural Networks with Masked Convolutions☆39Updated 3 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆30Updated 2 years ago
- Personal implementation of "Variational Inference with Normalizing Flows" by [Rezende, et al., 2015] in PyTorch☆22Updated 4 years ago
- Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation☆69Updated 3 years ago
- Code for the paper "Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification" (2020)☆43Updated 6 months ago
- Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'☆27Updated 3 years ago
- Code for "Variational Autoencoder with Learned Latent Structure"☆32Updated 3 years ago
- ☆45Updated last year
- PyTorch implementation of the OT-Flow approach in arXiv:2006.00104☆49Updated 4 months ago
- PyTorch implementation of Proximal Gradient Algorithms a la Parikh and Boyd (2014). Useful for Auto-Sizing (Murray and Chiang 2015, Murra…☆40Updated 4 years ago
- Official code for the ICLR 2021 paper Neural ODE Processes☆71Updated 2 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 3 years ago
- Implementations of Normalizing Flows in Pytorch/Pyro☆19Updated 4 years ago
- Code for the research paper "HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference".☆21Updated 3 years ago
- PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.☆52Updated 3 years ago
- repo for "Decision explanation and feature importance for invertible networks"☆13Updated 5 years ago
- Contains legacy code and model examples for the paper "BayesFlow: Learning complex stochastic models with invertible neural networks"☆21Updated 3 years ago
- Code for the paper "Analyzing inverse problems with invertible neural networks." (2018)☆84Updated 4 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 4 years ago
- Relative gradient optimization of the Jacobian term in unsupervised deep learning, NeurIPS 2020☆21Updated 3 years ago
- Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"☆33Updated 2 years ago
- Official code for UnICORNN (ICML 2021)☆27Updated 3 years ago
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 4 years ago
- Code for artificial toy data sets used to evaluate (conditional) invertible neural networks and related methods☆11Updated 3 years ago
- ☆28Updated 2 years ago
- Glow and RealNVP with tensorflow 2.0 and tensorflow probability☆31Updated 2 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- ☆23Updated 3 years ago
- Reproducing the paper "Variational Sparse Coding" for the ICLR 2019 Reproducibility Challenge☆60Updated last year
- ☆31Updated 4 years ago