kevtimova / deep-sparse
We introduce a way to extend sparse dictionary learning to deep architectures.
☆16Updated 3 years ago
Alternatives and similar repositories for deep-sparse:
Users that are interested in deep-sparse are comparing it to the libraries listed below
- Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"☆28Updated 5 years ago
- Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"☆25Updated 3 years ago
- Learning Generative Models across Incomparable Spaces (ICML 2019)☆27Updated 4 years ago
- NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks☆36Updated last year
- ☆29Updated 3 years ago
- Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation☆69Updated 4 years ago
- Code for "Learning with minibatch Wasserstein: asymptotic and gradient properties".☆13Updated 3 years ago
- Learning Autoencoders with Relational Regularization☆45Updated 4 years ago
- ☆17Updated last year
- Featurized Density Ratio Estimation☆20Updated 3 years ago
- Improving Transformation Invariance in Contrastive Representation Learning☆13Updated 3 years ago
- ☆25Updated last year
- Stochastic algorithms for computing Regularized Optimal Transport☆57Updated 6 years ago
- ICLR 2021 (spotlight): Graph Convolution with Low-rank Learnable Local Filters☆15Updated 4 years ago
- The implementation code for our paper Wasserstein Embedding for Graph Learning (ICLR 2021).☆31Updated 4 years ago
- ☆32Updated 5 years ago
- Reproducing the paper "Variational Sparse Coding" for the ICLR 2019 Reproducibility Challenge☆61Updated last year
- Implementation of the Gromov-Wasserstein distance to the setting of Unbalanced Optimal Transport☆43Updated last year
- ☆35Updated 3 years ago
- Implementation of "Learning Multiscale Convolutional Dictionaries for Image Reconstruction", IEEE Transaction On Computational Imaging, 2…☆29Updated last year
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆30Updated 3 years ago
- Code for `BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery`, Neurips 2021☆24Updated 3 years ago
- Contains the code relative to the paper Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning https://arxiv.org/abs…☆21Updated 4 years ago
- Implementations of orthogonal and semi-orthogonal convolutions in the Fourier domain with applications to adversarial robustness☆43Updated 3 years ago
- ☆30Updated 3 years ago
- Robust Learning with the Hilbert-Schmidt Independence Criterion☆44Updated 4 years ago
- Implementation of the Sliced Wasserstein Autoencoder using PyTorch☆101Updated 6 years ago
- Personal implementation of "Variational Inference with Normalizing Flows" by [Rezende, et al., 2015] in PyTorch☆22Updated 5 years ago
- ☆10Updated 5 years ago
- ☆35Updated 4 years ago