Gaussian Process

VMP

DEEP NEURAL NETWORKS AS GAUSSIANP ROCESSES PDF
Gaussian Process Behaviour in Wide Deep Neural Networks PDF
BAYESIAN DEEP CONVOLUTIONAL NETWORKS WITH MANY CHANNELS ARE GAUSSIAN PROCESSES
Deep Neural Networks as Gaussian Processes
Gaussian Process Behaviour in Wide Deep Neural Networks
Rasmussen C.E., Williams C.K.I. Gaussian Processes for Machine Learning-MIT Press (2006).pdf

Gaussian and Deep Gaussian Processes for Inference 2018
Learning unknown ODE models with Gaussian processes
Scaling Gaussian Processes by Yanshuai Cao 2018
Deep learning with differential Gaussian process flows
NUS School of Computing Summer School Gaussian Process Methods in Machine Learning 2018
Journal of Machine Learning Research 19 (2018) 1-46 Submitted 1/18; Revised 8/18; Published 9/18 How Deep Are Deep Gaussian Processes?
1 Gaussian Processes Over Graphs
State Space Gaussian Processes with Non-Gaussian Likelihood
Scalable Gaussian Processes Zhenwen Dai Amazon September 4, 2018 @GPSS2018
A Tutorial on Gaussian Processes 2011
Introducrion in Gauss Proc
Gaussian Processes 2010
Introduction to Gaussian Processes 2015
C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X. c © 2006 Massachusetts Institute of Technology. www.GaussianProcess.org/gpml Gaussian Processes for Machine Learning
What are Gaussian processes?
install-pytorch

A highly efficient and modular implementation of Gaussian Processes in PyTorch
This examples directory provides numerous ipython notebooks that demonstrate the use of GPyTorch
GPyTorch’s documentation

Gaussian processes framework in python
GPy is a Gaussian Process (GP) framework written in Python, from the Sheffield machine learning group
GPy is a Gaussian Process (GP) framework written in python, from the Sheffield machine learning group.
Here are a few notebooks outlining basic functionality of GPy


Simulating the posterior of a Gaussian process
Gaussian process WiKi
Gaussian Processes for Dummies

A simple way to share Jupyter Notebooks
Plotly’s Python API User Gui
Plotly User Guide in Python

Neural Network in Your Browser
Intermediate Python
Tutorials for learning Torch
Get Started with TensorFlow
Google AI Publication database
mit-deep-learning-book-pdf
Deep Bayes в глубинном обучении 2017

Attention based networks
обучение глубоких сетей vi обработка естественного языка
Механизмывниманиявнейронныхсетях

attention-model
Attention mechanism Implementation for Keras
Activation Maps (Layers Outputs) and Gradients in Keras

How to Develop an Encoder-Decoder Model with Attention for Sequence-to-Sequence Prediction in Keras
attention-mechanism
attention
Text classifier for Hierarchical Attention Networks for Document Classification

Sequence To Sequence Attention Models In DyNet
attention_networks.pdf
Effective Approaches to Attention-based Neural Machine Tr anslation pdf
A Brief Overview of Attention Mechanism
What is exactly the attention mechanism introduced to RNN (recurrent neural network)?
A Beginner’s Guide to Attention Mechanisms and Memory Networks
Attention and Memory in Deep Learning and NLP
Механизмы внимания в нейронных сетях — MachineLearning.ru
Tensorflow implementation of attention mechanism for text classification tasks
How to Use Word Embedding Layers for Deep Learning with Keras

КАЗАНСКИЙ ФЕДЕРАЛЬНЫЙ УНИВЕРСИТЕТ ИНСТИТУТ ВЫЧИСЛИТЕЛЬНОЙ МАТЕМАТИКИ И ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ Кафедра математической статистики И. А. КАРЕЕВ ЛЕКЦИИ ПО ТЕОРИИ СЛУЧАЙНЫХ ПРОЦЕССОВ

СХОДИМОСТЬ МЕР И ПРЕОБРАЗОВАНИЕ РАДОНА В БЕСКОНЕЧНОМЕРНЫХ ПРОСТРАНСТВАХ

Чеботарев А.М. Введение в теорию вероятностей и математическую статистику для физиков
В. И. Богачёв. Преобразования и сходимость мер
Халмош П.Р. Как писать математические тексты, УМН, 1971, 26, 5(161), 243–269.pdf

Гауссовских процессов с дифференциалом
Алгоритмыобучениягауссовскихпроцессов длябольшихобъемовданных
Bayes
Регрессиянаосновенестационарныхгауссовскихпроцессовс байесовскойрегуляризацией 2015
Выделение главных направлений в задаче аппроксимации на основе гауссовских процессов

Механизмывниманиявнейронныхсетях
Attention
Effective Approaches to Attention-based Neural Machine Tr anslation
Attention mechanism Implementation for Keras
Attention mechanism Implementation for Keras
Attention and Memory in Deep Learning and NLP
Attention
Deep Residual Learning for Image Recognition
Sequence To Sequence Attention Models In DyNet
How to Develop an Encoder-Decoder Model with Attention for Sequence-to-Sequence Prediction in Keras

Голубев Г.К. Вероятностные методы классической математической статистики.pdf

Financial Time Series Analysis of Stock Data 2018