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Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generator as a deep generative prior, is a promising tool for image restoration under corruptions. However, the performance of GAN-inversion can be limited by a lack of robustness to unknown gross …
This post is co-written with Mahima Agarwal, Machine Learning Engineer, and Deepak Mettem, Senior Engineering Manager, at VMware Carbon Black VMware Carbon Black is a renowned security solution offering protection against the full spectrum of modern cyberattacks. With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) …
The climate is changing. This makes predicting the path of extreme-weather events, among other challenges, all the more difficult. AI powered by NVIDIA technology can help tackle such challenges in climate science and increase the accuracy of weather prediction. Dive deeper into AI for climate science at NVIDIA GTC, a global conference for the era …
We study the fundamental question of how to define and measure the distance from calibration for probabilistic predictors. While the notion of perfect calibration is well-understood, there is no consensus on how to quantify the distance from perfect calibration. Numerous calibration measures have been proposed in the literature, but it is unclear how they compare …
By Burak Bacioglu, Meenakshi Jindal Asset Management at Netflix At Netflix, all of our digital media assets (images, videos, text, etc.) are stored in secure storage layers. We built an asset management platform (AMP), codenamed Amsterdam, in order to easily organize and manage the metadata, schema, relations and permissions of these assets. It is also responsible …
Read more “Elasticsearch Indexing Strategy in Asset Management Platform (AMP)”
Posted by Danny Driess, Student Researcher, and Pete Florence, Research Scientist, Robotics at Google Recent years have seen tremendous advances across machine learning domains, from models that can explain jokes or answer visual questions in a variety of languages to those that can produce images based on text descriptions. Such innovations have been possible due …
Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Data Wrangler enables you to access data from a wide variety of popular sources (Amazon S3, Amazon Athena, Amazon Redshift, Amazon EMR and Snowflake) and over 40 other third-party sources. …
Introduction All beings on this planet are affected by atmospheric phenomena we call the weather. Because of this, humans have invented all sorts of measuring tools, and luckily have loads of data from observations. All this data has been on quite a technological journey; from being collected on paper and local servers in basements, to now …
Read more “Using ML to predict the weather and climate risk”
This blog post captures a conversation between Palantirians Elizabeth Watts and Alejandro Davila that originally appeared on InternMakers’ YouTube channel. InternMakers is an online community co-founded by Alejandro and his classmate Mariana, with a mission to help all students get tech internships. In this conversation, Elizabeth, Recruiting Events and Programs Lead at Palantir, discusses Palantir’s …
By Benson Ma, ZZ ZimmermanWith contributions from Alok Ahuja, Shravan Heroor, Michael Krasnow, Todor Minchev, Inder Singh Introduction At Netflix, we test hundreds of different device types every day, ranging from streaming sticks to smart TVs, to ensure that new version releases of the Netflix SDK continue to provide the exceptional Netflix experience that our customers …