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New
The latest blog posts to take off on Hacker News. 212 posts.
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Ray Tracing Essentials, Part 1: Basics of Ray Tracing
Ray Tracing Essentials is a seven-part video series hosted by the editor of Ray Tracing Gems, NVIDIA’s Eric Haines.
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Graph Neural Networks – An Overview
How Neural Networks can be used in graph data
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A popular self-driving car dataset is missing labels for hundreds of pedestrians
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…
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Microsoft Zero and DeepSpeed: Memory Efficient Large Neural Network Training
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…
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Turing-NLG: A 17B-parameter language model
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…
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A new model and dataset for long-range memory
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…
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Dopamine and Temporal Difference Learning
Learning and motivation are driven by internal and external rewards. Many of our day-to-day behaviours are guided by predicting, or anticipating, whether a given action will result in a positive (that is, rewarding) outcome. The study of how organisms learn from experience to correctly anticipate rewards has been a…
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HiPlot: High-dimensional interactive plots made easy
We are releasing HiPlot, a lightweight interactive visualization tool to help AI researchers discover correlations and patterns in high-dimensional data.
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Using ‘radioactive data’ to detect if a data set was used for training
Facebook AI has developed a new technique to mark the images in a dataset, so that researchers can then determine if a particular machine learning model...
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An Opinionated Guide to Machine Learning Research
I originally wrote this guide in back in December 2017 for the OpenAI Fellows program
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Standardizing OpenAI’s deep learning framework on PyTorch
We are standardizing OpenAI’s deep learning framework on PyTorch.
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Towards a conversational agent that can chat about anything
Posted by Daniel Adiwardana, Senior Research Engineer, and Thang Luong, Senior Research Scientist, Google Research, Brain Team Modern conversatio...
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Automating receipt processing with deep learning
In this article, I cover the theory behind receipt digitization and implement an end-to-end pipeline using OpenCV and Tesseract. I also review a few important papers that do Receipt Digitization using Deep Learning.
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JetBrains to reimagine IntelliJ as text editor, add machine learning
This is the second part of the roadmap of IntelliJ IDEA and IntelliJ-based IDEs for 2020. The first part of the roadmap was focused mostly on low-level features in the platform. In this post we’ll be
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Table Detection and Extraction Using Deep Learning
Extract table from image with Nanonets table detection OCR. Learn OCR table Deep Learning methods to detect tables in images or PDF documents.
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Reformer, the Efficient Transformer
Posted by Nikita Kitaev, Student Researcher, UC Berkeley and Łukasz Kaiser, Research Scientist, Google Research Understanding sequential data — s...
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AlphaFold: Using AI for Scientific Discovery
In our study published in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries. We’ve built a dedicated, interdisciplinary team in hopes of using AI to push basic research forward: bringing together experts from the fields of structural biology, physics, and…
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A Sober Look at Bayesian Neural Networks
by Carles Gelada and Jacob Buckman WARNING: This is an old version of this blogpost, and if you are a Bayesian, it might make you angry. Click here for an updated post with the same content. Context: About a month ago Carles asserted on Twitter that Bayesian Neural Networks make...
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Autocomplete as an Interface (2015)
the real reason zsh beats bash • how I got twice as fast in python • autocompleting all the things • maximizing user-interface bandwidth
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Using neural networks to solve advanced mathematics equations
Facebook AI has developed the first neural network that uses symbolic reasoning to solve advanced mathematics problems.
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Online speech recognition with wav2letter anywhere
We have open-sourced wav2letter@anywhere, an inference framework for online speech recognition that delivers state-of-the-art performance.
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The Case for Bayesian Deep Learning
The Case for Bayesian Deep Learning Andrew Gordon Wilson Abstract Bayesian inference is especially compelling for deep neural networks. The key distinguishing property of a Bayesian approach is margin
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ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations
Posted by Radu Soricut and Zhenzhong Lan, Research Scientists, Google Research Ever since the advent of BERT a year ago, natural language research...
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Flyte: A Cloud Native Machine Learning and Data Processing Platform
Today Lyft is excited to announce the open sourcing of Flyte, a structured programming and distributed processing platform for highly…