# denoising diffusion (blogs) — RSS Amplifier Recent posts from the 2 feeds in the RSS Amplifier directory that cover denoising diffusion. Page: Feed: --- ## [A framework for making sense of metrics in technical organizations](https://mbernste.github.io/posts/metrics/) _2026-02-10 · Matthew N. Bernstein · Matthew N. Bernstein_ If you work in a quantitative or technical field, there is little doubt that you or your team has worked long and hard to define which metrics to measure and track. Using data-driven metrics is a critical practice for making rational decisions and deciphering truth in a complex and noisy world. However, as others have pointed out, an over-reliance on metrics can lead to poor outcomes. In an effort… ## [Understanding attention](https://mbernste.github.io/posts/attention/) _2025-12-21 · Matthew N. Bernstein · Matthew N. Bernstein_ Attention is a type of layer in a neural network that is widely regarded to be one of the most important breakthroughs that enabled the development of modern AI systems and large language models. At its heart, attention is a mechanism for explicitly drawing relationships between items in a set. In natural language processing, the set being processed are words (or tokens) and attention enables the… ## [Migrating to Substack](https://xcorr.net/2025/06/04/migrating-to-substack/) _2025-06-04 · xcorr · xcorr: AI & neuro_ After 15 happy years on WordPress.com, I’ve moved to Substack at neuroai.science. I’ve already published more than a dozen long-form articles on neuroscience and AI over there in newsletter format. You can subscribe or read the archives completely free. Substack makes it easy to create sticky experiences for audiences and reach them where they are–in \[…\] ## [Demystifying Euler’s number](https://mbernste.github.io/posts/eulers_number/) _2025-01-26 · Matthew N. Bernstein · Matthew N. Bernstein_ Euler’s number $e := 2.71828\\dots$ has, to me, always been a semi-mysterious number. While I understood many facts about $e$, I never felt I ever truly understood what it really was – it’s core essence so to speak. I believe that part of the reason for my confusion is that $e$ is often taught coming from two seemingly different perspectives: Either it is introduced in the context of compound… ## [Reproducing kernel Hilbert spaces and the kernel trick](https://mbernste.github.io/posts/rkhs/) _2024-12-14 · Matthew N. Bernstein · Matthew N. Bernstein_ If you’re a practitioner of machine learning, then there is little doubt you have seen or used an algorithm that falls into the general category of kernel methods. The premier example of such methods is the support vector machine. When introduced to these algorithms, one is taught that one must provide the algorithm with a kernel function that, intuitively, computes a degree of “similarity”… ## [Dot product](https://mbernste.github.io/posts/dot_product/) _2024-12-09 · Matthew N. Bernstein · Matthew N. Bernstein_ The dot product is a fundamental operation on two Euclidean vectors that captures a notion of similarity between the vectors. In this post, we’ll define the dot product and offer a number of angles for which to intuit the idea captured by this fundamental operation. ## [Intuiting biology (Part 1: Order and chaos in the crowded cell)](https://mbernste.github.io/posts/intuit_biology_goodsell/) _2024-11-24 · Matthew N. Bernstein · Matthew N. Bernstein_ Cells are crowded spaces packed with biomolecules colliding and interacting with one another. Despite this chaotic environment, biologists routinely describe intracellular functions using the clean mathematical language of networks. In this post I will attempt to reconcile these two seemingly contradictory perspectives of the cell. This post will serve as a first part in a series of blog posts I… ## [Notes on \_The Art of War\_ by Sun Tzu](https://mbernste.github.io/posts/art_of_war_part1/) _2024-11-16 · Matthew N. Bernstein · Matthew N. Bernstein_ I am currently reading Sun Tzu’s Art of War and am finding much wisdom in it. I have been taking notes during my reading and I thought I’d share them in this post. Here I cover Books 1 and 2. ## [Denoising diffusion probabilistic models (Part 2: Theoretical justification)](https://mbernste.github.io/posts/diffusion_part2/) _2024-10-20 · Matthew N. Bernstein · Matthew N. Bernstein_ In Part 1 of this series, we introduced the denoising diffusion probabilistic model for modeling and sampling from complex distributions. We described the diffusion model as a model that can generate new samples by learning how to reverse a diffusion process. In this post, we provide more theoretical justification for the objective function used to fit diffusion models and make connections between…