The latest blog posts to take off on Hacker News. 3,092 posts.
Applying probabilistic models to data usually involves integrating a complex, multi-dimensional probability distribution. For example, calculating the expectation/mean of a model distribution invol…
Why Can a Machine Beat Mario but not Pokemon? By now, you’ve probably heard of bots playing video games at superhuman levels. These bots can be programmed explicitly, reacting to set inputs with …
Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. Unfortunately, current…
An illustrated essay on generative algorithms written by Anders Hoff.
Python Programming tutorials from beginner to advanced on a massive variety of topics. All video and text tutorials are free.
In our last article " Why Love Generative Art ?" we had a blast putting the genre into the context of modern art history. In this article we interview contemporary generative art prodigy (my words, not his) Manolo Gamboa Naon from Argentina. Manolo's work feels like it is the re
It was clear when Ouster started developing the OS-1 three years ago that deep learning research for cameras was outpacing lidar research…
This is the first of a two-part series about Bayesian bandit algorithms. Check out the second post here. Let’s talk about Bayesianism. It’s developed a reputation (not entirely justified, but not entirely unjustified either) for being too mathematically sophisticated or too computationally intensive to work at scale…
Over the last 50 years, our world has turned digital at breakneck speed. No art form has captured this transitional time period - our time period - better than generative art. Generative art takes full advantage of everything that computing has to offer, producing elegant and compelling artworks tha
Here, I provide an in-depth analysis of GPUs for deep learning/machine learning and explain what is the best GPU for your use-case and budget.
There was a paper recently where a research team trained a machine learning algorithm (a GAN they called AttnGAN) to generate pictures based on written descriptions. It’s like Visual Chatbot in reverse. When it was just trained to generate pictures of birds, it did pretty well, actually.
Suggested Citation: Bai, H. (2018) Evidence that A Large Amount of Low Quality Responses on MTurk Can Be Detected with Repeated GPS Coordinates. Retrieved from: https://www.maxhuibai.com/blog/evidence-that-responses-from-repeating-gps-are-random
For the past year, I’ve been working on implementing well known model architectures and building web applications, so I have a fair amount…
Markov chains, named after Andrey Markov , are mathematical systems that hop from one "state" (a situation or set of values) to another. For example, if you made a Markov chain model of a baby's behavior, you might include "playing," "eating", "sleeping," and "crying" as states, which together with other behaviors…
Evolving Floorplans - 2017.
TL;DR: In this post, I build an app that classifies whether an audio sample is a kick drum, snare drum, or other drum sample with 87% accuracy using 🎉machine learning. 🎉
At the kind invitation of Rob May and the Botchain team, I had the opportunity recently to keynote Brains and Chains, an interesting conference in New York expl
Introduction I read a lot of deep learning papers, typically a few/week. I've read probably several thousands of papers. My general problem with papers in machine learning or deep learning is that often they sit
Improving artificial intelligence research with scientific testing, design practice, and meta-rational choice of methods and criteria
Build a program synthesis tool, to generate programs from specifications, in 20 lines of code using Rosette.
By Zachary C. Lipton* & Jacob Steinhardt* *equal authorship Originally presented at ICML 2018: Machine Learning Debates [arXiv link] Published in Communications of the ACM 1 Introduction Collectively, machine learning (ML) researchers are engaged in
One day in the mid-1920s, a Moscow newspaper reporter named Solomon Shereshevsky entered the laboratory of the psychologist Alexander Luria. Shereshevsky's boss at the newspaper had noticed that Shereshevsky never needed to take any notes, but somehow still remembered all he was told, and had suggested he get his…
Deep neural networks have been shown to be vulnerable to the adversarial example phenomenon: all models tested so far can have their classifications dramatically altered by small image perturbations . The following predictions were for instance made by a state-of-the-art network trained to recognize celebrities :
Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We…
Deep code search Gu et al., ICSE’18 The problem with searching for code is that the query, e.g. “read an object from xml,” doesn’t look very much like the source code snippets tha…
Everyone has heard of machine learning now, and every big company is working on projects around ‘AI’. We know this is a Next Big Thing. But we don’t yet have a settled sense of quite what machine learning means - what it will mean for tech companies or for companies in the broader economy, how to
