Alternatives and similar repositories for csc2541-ml-project

Users that are interested in csc2541-ml-project are comparing it to the libraries listed below

• yuqirose / tensor_train_RNN
  Clean repo for tensor-train RNN implemented in TensorFlow
  ☆69Updated 6 years ago
• ngorbach / Variational_Gradient_Matching_for_Dynamical_Systems
  Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"
  ☆26Updated 6 years ago
• ivannz / mlss2019-bayesian-deep-learning
  MLSS2019 Tutorial on Bayesian Deep Learning
  ☆93Updated 5 years ago
• anassinator / bnn
  Bayesian Neural Network in PyTorch
  ☆91Updated last year
• strongio / quantile-regression-tensorflow
  Tensorflow implementation of deep quantile regression
  ☆76Updated 3 years ago
• zalandoresearch / probrnn
  State space modeling with recurrent neural networks
  ☆45Updated 7 years ago
• manuwhs / BayesianRNN
  Reproducing the results of the paper "Bayesian Recurrent Neural Networks" by Fortunato et al.
  ☆40Updated 7 years ago
• abrvkh / WaveNet_simple
  A simple implementation of the WaveNet model for time series forecasting
  ☆26Updated 7 years ago
• bperezorozco / ordinal_tsf
  ☆29Updated 5 years ago
• msmbuilder / vde
  Variational Autoencoder for Dimensionality Reduction of Time-Series
  ☆189Updated 3 years ago
• wassname / attentive-neural-processes
  implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)
  ☆97Updated 6 months ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆37Updated 8 years ago
• maka89 / Deep-Kernel-GP
  Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
  ☆94Updated 7 years ago
• TeaPearce / Deep_Learning_Prediction_Intervals
  ICML paper 'High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach'
  ☆91Updated 5 years ago
• zhulingchen / tfp-tutorial
  TensorFlow Probability Tutorial
  ☆37Updated 5 years ago
• facebookresearch / SingleModelUncertainty
  Learning error bars for neural network predictions
  ☆71Updated 5 years ago
• kumar-shridhar / Master-Thesis-BayesianCNN
  Master Thesis on Bayesian Convolutional Neural Network using Variational Inference
  ☆264Updated 6 years ago
• cambridge-mlg / sghmc_dgp
  ☆29Updated 6 years ago
• spallas / time-attention
  Implementation of RNN for Time Series prediction from the paper https://arxiv.org/abs/1704.02971
  ☆59Updated 2 years ago
• NervanaSystems / aidc-2018-timeseries
  Deep learning for time-series data
  ☆49Updated 2 years ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆236Updated 4 years ago
• amirstar / Deep-Forecast
  The code of the paper 'Deep Forecast : Deep Learning-based Spatio-Temporal Forecasting", ICML Time Series Workshop 2017.
  ☆119Updated 5 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆83Updated last year
• ratschlab / SOM-VAE
  TensorFlow implementation of the SOM-VAE model as described in https://arxiv.org/abs/1806.02199
  ☆197Updated 2 years ago
• sachinruk / KerasQuantileModel
  Quantile Regression using Deep Learning. An alternative to Bayesian models to get uncertainty.
  ☆77Updated 8 years ago
• FelixOpolka / Deep-Gaussian-Process
  🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0
  ☆26Updated last year
• markvdw / GPflow-inter-domain
  Gaussian processes in TensorFlow with modifications to allow inter-domain inducing variables
  ☆13Updated 8 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆141Updated 9 years ago
• Rachnog / disentanglment
  Experiments with beta-VAE to learn disentangled representations from the data
  ☆65Updated 6 years ago
• idelbrid / Randomly-Projected-Additive-GPs
  Code for Randomly Projected Additive Gaussian Processes
  ☆25Updated 5 years ago