Published:
In Interval Bound Propagation 101 I derived the general IBP machinery: interval arithmetic over matrices via Rump’s algorithm, and how to propagate those bounds through a feed-forward network layer by layer. This post grounds that theory in a small worked example: a concrete network, a from-scratch numpy implementation of IBP, and the visualizations that fall out of it.
Published:
The purpose of this post is to explain the basics of the Interval Bound Propagation (IBP) method. It is powerful tool which allows to provide guarantees about output ranges of a neural network. I mostly follow the notation from deep reinforcement learning (RL), therefore the typical neural network for which I want to compute bounds is a neural policy.
Published:
From https://www.nature.com/articles/s42256-022-00568-3 (which is for supervised learning)
Published:
Say you want to estimate a distribution of a latent vector $Z$ based on observations $X$. You can do that with variational inference!
Published:
Conspectus of Sutton & Barto “Reinforcement Learning: An Introduction”
Published:
Conspectus of Sutton & Barto “Reinforcement Learning: An Introduction”
Published:
One of the core concepts of basic reinforcement learning are a Markov Process and a Markov Reward Process.
Published:
Say you want to estimate a distribution of a latent vector $Z$ based on observations $X$. You can do that with variational inference!
Published:
From https://www.nature.com/articles/s42256-022-00568-3 (which is for supervised learning)
Published:
In Interval Bound Propagation 101 I derived the general IBP machinery: interval arithmetic over matrices via Rump’s algorithm, and how to propagate those bounds through a feed-forward network layer by layer. This post grounds that theory in a small worked example: a concrete network, a from-scratch numpy implementation of IBP, and the visualizations that fall out of it.
Published:
The purpose of this post is to explain the basics of the Interval Bound Propagation (IBP) method. It is powerful tool which allows to provide guarantees about output ranges of a neural network. I mostly follow the notation from deep reinforcement learning (RL), therefore the typical neural network for which I want to compute bounds is a neural policy.
Published:
In Interval Bound Propagation 101 I derived the general IBP machinery: interval arithmetic over matrices via Rump’s algorithm, and how to propagate those bounds through a feed-forward network layer by layer. This post grounds that theory in a small worked example: a concrete network, a from-scratch numpy implementation of IBP, and the visualizations that fall out of it.
Published:
The purpose of this post is to explain the basics of the Interval Bound Propagation (IBP) method. It is powerful tool which allows to provide guarantees about output ranges of a neural network. I mostly follow the notation from deep reinforcement learning (RL), therefore the typical neural network for which I want to compute bounds is a neural policy.
Published:
One of the core concepts of basic reinforcement learning are a Markov Process and a Markov Reward Process.
Published:
Conspectus of Sutton & Barto “Reinforcement Learning: An Introduction”
Published:
Say you want to estimate a distribution of a latent vector $Z$ based on observations $X$. You can do that with variational inference!
Published:
In Interval Bound Propagation 101 I derived the general IBP machinery: interval arithmetic over matrices via Rump’s algorithm, and how to propagate those bounds through a feed-forward network layer by layer. This post grounds that theory in a small worked example: a concrete network, a from-scratch numpy implementation of IBP, and the visualizations that fall out of it.
Published:
The purpose of this post is to explain the basics of the Interval Bound Propagation (IBP) method. It is powerful tool which allows to provide guarantees about output ranges of a neural network. I mostly follow the notation from deep reinforcement learning (RL), therefore the typical neural network for which I want to compute bounds is a neural policy.
Published:
From https://www.nature.com/articles/s42256-022-00568-3 (which is for supervised learning)
Published:
Conspectus of Sutton & Barto “Reinforcement Learning: An Introduction”
Published:
Conspectus of Sutton & Barto “Reinforcement Learning: An Introduction”
Published:
One of the core concepts of basic reinforcement learning are a Markov Process and a Markov Reward Process.