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Search results for deep learning gaussian processes
deep-learning
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gaussian-processes
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26 search results found
D2l En
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21,912
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Gpflow
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1,802
Gaussian processes in TensorFlow
Master
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554
A machine learning course using Python, Jupyter Notebooks, and OpenML
Good Papers
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231
I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers
Keras Gp
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219
Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.
Understandingbdl
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174
Deep Kernel Transfer
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142
Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
Survival Analysis Using Deep Learning
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138
This repository contains morden baysian statistics and deep learning based research articles , software for survival analysis
Aboleth
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125
A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation
Pycrop Yield Prediction
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80
A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction
Deepgp
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78
Deep Gaussian Processes in matlab
Hyper Engine
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69
Python library for Bayesian hyper-parameters optimization
Deep Bayesian Nonparametrics Papers
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67
The collection of papers about combining deep learning and Bayesian nonparametrics
Mnist Challenge
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64
My solution to TUM's Machine Learning MNIST challenge 2016-2017 [winner]
Deepcgp
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57
Deep convolutional gaussian processes.
Jlearn
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55
Machine Learning Library, written in J
Dual
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54
Code for paper "Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning"
Fbnn
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53
Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
Deep Kernel Gp
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46
Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
Convnets As Gps
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36
Code for "Deep Convolutional Networks as shallow Gaussian Processes"
Flu Sequence Predictor
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34
An experimental deep learning & genotype network-based system for predicting new influenza protein sequences.
Deepgp_approxep
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28
see https://github.com/thangbui/geepee for a faster implementation
Bruno
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26
a deep recurrent model for exchangeable data
Sghmc_dgp
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16
Spngp
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15
🔆 A Python implementation of a sum-product network with gaussian processes leafs model (SPNGP, arXiv:1809.04400) 📃
Ml_course
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15
"Learning Machine Learning" Course, Bogotá, Colombia 2019 #LML2019
Machine Learning Summer Schools
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14
Curated materials for different machine learning related summer schools
Dvg
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14
Diverse Video Generation using a Gaussian Process Trigger
Dgplib
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12
Library for Deep Gaussian Processes based on GPflow
Deep Gaussian Process
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12
🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0
Engineer
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12
Machine Learning for Engineers course at TU/e (2IMM15)
Gp_drf
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11
Official code for "Efficient Deep Gaussian Process Models for Variable-Sized Inputs" - accepted in IJCNN2019
Convnets As Gps
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10
Code for "Deep Convolutional Networks as shallow Gaussian Processes"
Deep Bayesian Quadrature Policy Optimization
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9
Official implementation of the AAAI 2021 paper Deep Bayesian Quadrature Policy Optimization.
Dyhpo
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8
[NeurIPS 2022] Supervising the Multi-Fidelity Race of Hyperparameter Configurations
Deep_learning_onboarding
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8
List of resources for deep learning
Deepstructuredmixtures
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7
Code for Deep Structured Mixtures of Gaussian Processes (DSMGPs)
Blackbox Optimization Using Rnns
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7
This project is a part of "Neural Information Processing Project" taught at TU Berlin.
Time Series
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6
Time-Series models for multivariate and multistep forecasting, regression, and classification
Gaussianprocesses
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5
Modern Gaussian Processes: Scalable Inference and Novel Applications
Spavae
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5
Dependency-aware deep generative models for multitasking analysis of spatial genomics data
Doubly Stochastic Deep Gaussian Process
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5
Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference
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