d909b / ameLinks
🤖🤖 Attentive Mixtures of Experts (AMEs) are neural network models that learn to output both accurate predictions and estimates of feature importance for individual samples.
☆43Updated 2 years ago
Alternatives and similar repositories for ame
Users that are interested in ame are comparing it to the libraries listed below
Sorting:
- Code for "Neural causal learning from unknown interventions"☆104Updated 5 years ago
- An Empirical Study of Invariant Risk Minimization☆27Updated 5 years ago
- ☆32Updated 7 years ago
- Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR☆64Updated 5 years ago
- Code for our ICML '19 paper: Neural Network Attributions: A Causal Perspective.☆51Updated 4 years ago
- ☆65Updated last year
- Repository for code release of paper "Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data" (AISTATS 2020)☆49Updated 5 years ago
- Keras implementation of Deep Wasserstein Embeddings☆48Updated 7 years ago
- Pytorch Implemetation for our NAACL2019 Paper "Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling" http…☆63Updated 5 years ago
- Source code for Naesseth et. al. "Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms" (2017)☆38Updated 8 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆132Updated 5 years ago
- Reproduction work of "Neural Relational Inference for Interacting Systems" in Chainer☆34Updated 6 years ago
- Tools for robustness evaluation in interpretability methods☆10Updated 4 years ago
- a python implementation of various versions of the information bottleneck, including automated parameter searching☆132Updated 5 years ago
- Code for paper EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE☆42Updated 2 years ago
- ☆30Updated 7 years ago
- [ICLR 2020] FSPool: Learning Set Representations with Featurewise Sort Pooling☆41Updated 2 years ago
- Variational Auto-encoder with Non-parametric Bayesian Prior☆43Updated 8 years ago
- SparseMax activation function implementation (ICML 2016) (PyTorch)☆28Updated 8 years ago
- Code for "Generative causal explanations of black-box classifiers"☆35Updated 5 years ago
- Dirichlet-Variational Auto-Encoder by PyTorch☆24Updated 3 months ago
- Uncertainty in Conditional Average Treatment Effect Estimation☆33Updated 4 years ago
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors☆62Updated 5 years ago
- ☆40Updated 7 years ago
- A TensorFlow implementation of the paper 'Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks'☆31Updated last year
- Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation☆69Updated 5 years ago
- Explaining a black-box using Deep Variational Information Bottleneck Approach☆46Updated 3 years ago
- Code for Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding☆24Updated 3 years ago
- Implementation of the Neural Clustering Process algorithm in Pytorch☆32Updated 5 years ago
- Code for "A Meta Transfer Objective For Learning To Disentangle Causal Mechanisms"☆127Updated 6 years ago