• Stars
    star
    11
  • Rank 1,694,829 (Top 34 %)
  • Language
    Jupyter Notebook
  • License
    MIT License
  • Created almost 9 years ago
  • Updated almost 9 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Implementation of gaussian processes and bayesian optimization in tensorflow

More Repositories

1

vae-clustering

Unsupervised clustering with (Gaussian mixture) VAEs
Jupyter Notebook
287
star
2

dirt-t

A DIRT-T Approach to Unsupervised Domain Adaptation (ICLR 2018)
Python
174
star
3

nn-bayesian-optimization

We use a modified neural network instead of Gaussian process for Bayesian optimization.
Python
105
star
4

cvae

Conditional variational autoencoder implementation in Torch
Jupyter Notebook
102
star
5

tensorsketch

A lightweight library for tensorflow 2.0
Python
66
star
6

vae-experiments

Code for some of the experiments I did with variational autoencoders on multi-modality and atari video prediction. Atari video prediction is work-in-progress.
Lua
62
star
7

micro-projects

A collection of small code snippets for learning how to code
Jupyter Notebook
58
star
8

tensorbayes

Deep variational inference in tensorflow
Python
56
star
9

began

Boundary equilibrium GAN implementation in Tensorflow
Python
15
star
10

kaos

Deep variational inference library for Keras
Python
15
star
11

fast-style-transfer

Fast style transfer in TensorFlow
Python
14
star
12

one-bit-vae

A silly and weirdly useful experiment where I attempt to encode one bit of information with a VAE
Jupyter Notebook
11
star
13

variational-autoencoder

Basic implementation of variational autoencoders in Torch
Jupyter Notebook
9
star
14

acgan-biased

Experiments verifying that AC-GAN downsamples points near decision boundary (NIPS BDL 2017)
Python
9
star
15

deep-generative-models

Deep generative models in Tensorflow
Python
6
star
16

ConvFeFe

The best neural network
Python
4
star
17

bcde

Bottleneck Conditional Density Estimation (ICML 2017)
Python
4
star
18

vda-hax

Simple tricks to improve visual domain adaptation for MNIST -> SVHN
Python
3
star