Boris Bonev

Boris Bonev

ML Research Scientist @ NVIDIA. Background in Applied Mathematics and Physics.

Posts by Tags

Gaussian Processes

A visual exploration of Gaussian Processes and Infinite Neural Networks

44 minute read

Published:

Gaussian Processes (GPs) generalize Gaussian distributions to random variables that are functions (stochastic processes). As such, they are a powerful tool for regression and Bayesian inference. In the past years, they have received increased attention due to the interest in machine learning. An intriguing connection between GPs and neural network exists when the width of layers in feed-forward neural networks tends towards infinity. For a specific choice of the prior distributions that governs the network parameters, the resulting network will be a GP.

Infinite Neural Networks

A visual exploration of Gaussian Processes and Infinite Neural Networks

44 minute read

Published:

Gaussian Processes (GPs) generalize Gaussian distributions to random variables that are functions (stochastic processes). As such, they are a powerful tool for regression and Bayesian inference. In the past years, they have received increased attention due to the interest in machine learning. An intriguing connection between GPs and neural network exists when the width of layers in feed-forward neural networks tends towards infinity. For a specific choice of the prior distributions that governs the network parameters, the resulting network will be a GP.

Julia

A visual exploration of Gaussian Processes and Infinite Neural Networks

44 minute read

Published:

Gaussian Processes (GPs) generalize Gaussian distributions to random variables that are functions (stochastic processes). As such, they are a powerful tool for regression and Bayesian inference. In the past years, they have received increased attention due to the interest in machine learning. An intriguing connection between GPs and neural network exists when the width of layers in feed-forward neural networks tends towards infinity. For a specific choice of the prior distributions that governs the network parameters, the resulting network will be a GP.

Machine Learning

A visual exploration of Gaussian Processes and Infinite Neural Networks

44 minute read

Published:

Gaussian Processes (GPs) generalize Gaussian distributions to random variables that are functions (stochastic processes). As such, they are a powerful tool for regression and Bayesian inference. In the past years, they have received increased attention due to the interest in machine learning. An intriguing connection between GPs and neural network exists when the width of layers in feed-forward neural networks tends towards infinity. For a specific choice of the prior distributions that governs the network parameters, the resulting network will be a GP.

An Introduction to Numerical Optimization with Python (Part 1)

13 minute read

Published:

This is the first post in a series of posts that I am planning to write on the topic of machine learning. This article introduces fundamental algorithms in numerical optimization. For now, this is the Gradient Descent and Netwon algorithm. I might extend it with momentum based methods and conjugate gradient methods in the future. All of the posts are essentially Jupyter notebooks that I will publish in this repository.

Multivariate Gaussian distribution

A visual exploration of Gaussian Processes and Infinite Neural Networks

44 minute read

Published:

Gaussian Processes (GPs) generalize Gaussian distributions to random variables that are functions (stochastic processes). As such, they are a powerful tool for regression and Bayesian inference. In the past years, they have received increased attention due to the interest in machine learning. An intriguing connection between GPs and neural network exists when the width of layers in feed-forward neural networks tends towards infinity. For a specific choice of the prior distributions that governs the network parameters, the resulting network will be a GP.

Numerical optimization

An Introduction to Numerical Optimization with Python (Part 1)

13 minute read

Published:

This is the first post in a series of posts that I am planning to write on the topic of machine learning. This article introduces fundamental algorithms in numerical optimization. For now, this is the Gradient Descent and Netwon algorithm. I might extend it with momentum based methods and conjugate gradient methods in the future. All of the posts are essentially Jupyter notebooks that I will publish in this repository.

PyTorch

An Introduction to Numerical Optimization with Python (Part 1)

13 minute read

Published:

This is the first post in a series of posts that I am planning to write on the topic of machine learning. This article introduces fundamental algorithms in numerical optimization. For now, this is the Gradient Descent and Netwon algorithm. I might extend it with momentum based methods and conjugate gradient methods in the future. All of the posts are essentially Jupyter notebooks that I will publish in this repository.

Python

An Introduction to Numerical Optimization with Python (Part 1)

13 minute read

Published:

This is the first post in a series of posts that I am planning to write on the topic of machine learning. This article introduces fundamental algorithms in numerical optimization. For now, this is the Gradient Descent and Netwon algorithm. I might extend it with momentum based methods and conjugate gradient methods in the future. All of the posts are essentially Jupyter notebooks that I will publish in this repository.