JoKerDii / bspline-PyTorch-blocks
A customized PyTorch layer and a customized PyTorch activation function using B-spline transformation
☆25Updated 4 years ago
Alternatives and similar repositories for bspline-PyTorch-blocks:
Users that are interested in bspline-PyTorch-blocks are comparing it to the libraries listed below
- Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with…☆55Updated 2 years ago
- High-order spline interpolation in PyTorch☆67Updated 6 months ago
- L1-regularized least squares with PyTorch☆65Updated 2 years ago
- Interpolating natural cubic splines. Includes batching, GPU support, support for missing values, evaluating derivatives of the spline, an…☆245Updated 2 years ago
- ☆26Updated last month
- Implementation of "Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains" by Tancik et al.☆90Updated 2 years ago
- E3xSO3 convolution implementation presented at MIDL 2023 https://openreview.net/pdf?id=lri_iAbpn_r☆13Updated last year
- A fully invertible U-Net for memory efficiency in Pytorch.☆122Updated 2 years ago
- Modular and intuitive Hypernetworks in Pytorch☆35Updated last year
- This is official Pytorch implementation of "Uncertainty quantification in medical image segmentation with Normalizing Flows", Raghavendra…☆30Updated 2 years ago
- Adaptive, interpretable wavelets across domains (NeurIPS 2021)☆77Updated 3 years ago
- The source code for the Layer Ensembles paper published in MICCAI 2022 (Singapore).☆26Updated 2 years ago
- Official Code for "Invert to Learn to Invert" that allows training of invertible networks without storing activations☆36Updated 4 years ago
- Learning Activation Functions in Deep (Spline) Neural Networks☆28Updated last year
- Experiments for Neural Flows paper☆94Updated 3 years ago
- A python/pytorch package for invertible neural networks☆64Updated last year
- Carpet: Neural Net based solver for the 1d-TV problem☆12Updated 4 years ago
- Multi-view-AE: An extensive collection of multi-modal autoencoders implemented in a modular, scikit-learn style framework.☆50Updated 7 months ago
- A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.☆50Updated 5 years ago
- The Wasserstein Distance and Optimal Transport Map of Gaussian Processes☆52Updated 4 years ago
- Code for the paper "Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification" (2020)☆43Updated 10 months ago
- Anderson accelerated Douglas-Rachford splitting☆29Updated 4 years ago
- Code for Diff-SCM paper☆94Updated last year
- 3D VQ-VAE-2 for high-resolution CT scan synthesis☆45Updated 3 years ago
- Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains☆46Updated 4 years ago
- ☆32Updated 2 years ago
- Code for random Fourier features based on Rahimi and Recht's 2007 paper.☆52Updated 4 years ago
- Algorithms for computations on random manifolds made easier☆88Updated last year
- ☆45Updated last year
- Official code for the ICLR 2021 paper Neural ODE Processes☆71Updated 2 years ago