The latest blog posts to take off on Hacker News. 6,335 posts.
Silver, a professor at University College London and Principal Research Scientist at DeepMind, is recognized as a central figure in the growing and impactful area of deep reinforcement learning, leading the team that developed AlphaGo.
A few weeks ago I wrote about Kuhn’s theory of paradigm shifts and how it relates to Bayesian inference. In this post I want to back up a little bit and explain what Bayesian inference is, and eventually rediscover the idea of a paradigm shift just from understanding how Bayesian inference works.
We propose a method to learn weakly symmetric deformable 3D object categories from raw single-view images, without ground-truth 3D, multiple views, 2D/3D keypoints, prior shape models or any other supervision.
When we hear about ML - whether it’s about machines learning to play Go or computers generating plausible human language - we often think about deep learning.
By the end of this blog post, we aim to improve the results of my previous blog post of approximating circular bokeh with a sum of 4 separable filters and reducing its artifacts (left) through an o…
AI Platform Pipelines provides enterprise-ready infrastructure for deploying and running structured ML workflows, and pipeline tools for building, debugging, and sharing pipelines and components.
A blog by Sam Greydanus
Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which…
The scientific community has galvanised in response to the recent COVID-19 outbreak, building on decades of basic research characterising this virus family. Labs at the forefront of the outbreak response shared genomes of the virus in open access databases, which enabled researchers to rapidly develop tests for this…
All the essential Deep Learning Algorithms you need to know including models used in Computer Vision and Natural Language Processing
A visual introduction to self-supervised learning methods for visual representations.
Computer Science, Machine Learning, Programming, Art, Mathematics, Philosophy, and Short Fiction
Posted by Adam Roberts, Staff Software Engineer and Colin Raffel, Senior Research Scientist, Google Research Over the past few years, transfer le...
Andreessen-Horowitz has always been the most levelheaded of the major current year VC firms. While other firms were levering up on “cleantech” and nonsensical biotech startups that viol…
Why prediction has overtaken perception as the biggest challenge in the field.
The 86th JPEG meeting was held in Sydney, Australia. Among the different activities that took place, the JPEG Committee issued a Call for Evidence on learning-based image coding solutions. In addition, a First Call for Evidence on Point Cloud Coding was issued in the framework of JPEG Pleno Point Cloud Coding…
Ray Tracing Essentials is a seven-part video series hosted by the editor of Ray Tracing Gems, NVIDIA’s Eric Haines.
How Neural Networks can be used in graph data
SUMMARY A hand-check of the 15,000 images in the widely used Udacity Dataset 2 found problems in 4,986 of them (33%), including thousands of unlabeled vehicles, hundreds of unlabeled pedestrians, dozens of unlabeled cyclists, and 217 completely unannotated images that contained real objects. Phantom annotations…
The latest trend in AI is that larger natural language models provide better accuracy; however, larger models are difficult to train because of cost, time, and ease of code integration. Microsoft is releasing an open-source library called DeepSpeed, which vastly advances large model training by improving scale, speed…
This figure was adapted from a similar image published in DistilBERT. Turing Natural Language Generation (T-NLG) is a 17 billion parameter language model by Microsoft that outperforms the state of the art on many downstream NLP tasks. We present a demo of the model, including its freeform generation, question…
Throughout our lives, we build up memories that are retained over a diverse array of timescales, from minutes to months to years to decades. When reading a book, we can recall characters who were introduced many chapters ago, or in an earlier book in a series, and reason about their motivations and likely actions in…
