rtanno21609 / AdaptiveNeuralTreesLinks
Adaptive Neural Trees
☆155Updated 6 years ago
Alternatives and similar repositories for AdaptiveNeuralTrees
Users that are interested in AdaptiveNeuralTrees are comparing it to the libraries listed below
Sorting:
- pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"☆302Updated 7 years ago
- An implementation of the Deep Neural Decision Forests in PyTorch☆165Updated 6 years ago
- Deep Neural Decision Trees☆165Updated 3 years ago
- Neural Architecture Search with Bayesian Optimisation and Optimal Transport☆135Updated 6 years ago
- PyTorch implementation of Neural Processes☆88Updated 6 years ago
- a python implementation of various versions of the information bottleneck, including automated parameter searching☆130Updated 5 years ago
- Gradient based hyperparameter optimization & meta-learning package for TensorFlow☆190Updated 5 years ago
- This is the official implementation for the paper 'AutoEncoder by Forest'☆75Updated 7 years ago
- PyTorch Implementation of "Distilling a Neural Network Into a Soft Decision Tree." Nicholas Frosst, Geoffrey Hinton., 2017.☆105Updated last year
- Code for AAAI 2018 accepted paper: "Beyond Sparsity: Tree Regularization of Deep Models for Interpretability"☆79Updated 7 years ago
- Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"☆68Updated last year
- A Python implementation of Kernel Mean Matching data reweighting algorithm☆33Updated 10 years ago
- Distillation of Neural Network Into a Soft Decision Tree☆65Updated 6 years ago
- Code for experiments regarding importance sampling for training neural networks☆328Updated 4 years ago
- Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …☆112Updated 7 years ago
- Learning error bars for neural network predictions☆72Updated 5 years ago
- Code for the paper Gaussian process behaviour in wide deep networks☆46Updated 7 years ago
- Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832☆253Updated 7 years ago
- ☆125Updated 4 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆132Updated 5 years ago
- Dilated RNNs in pytorch☆211Updated 6 years ago
- A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).☆206Updated 7 years ago
- Implementation of soft parameter sharing for neural networks☆70Updated 5 years ago
- Population Based Training (in PyTorch with sqlite3). Status: Unsupported☆162Updated 7 years ago
- Hypergradient descent☆147Updated last year
- My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"☆139Updated 7 years ago
- Functional ANOVA☆125Updated 9 months ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆101Updated 7 years ago
- Code for the paper Implicit Weight Uncertainty in Neural Networks☆65Updated 6 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆141Updated 9 years ago