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Posted by Fitsum Reda and Janne Kontkanen, Google Research Frame interpolation is the process of synthesizing in-between images from a given set of images. The technique is often used for temporal up-sampling to increase the refresh rate of videos or to create slow motion effects. Nowadays, with digital cameras and smartphones, we often take several …
Near-real-time delivery of data and insights enable businesses to rapidly respond to their customers’ needs. Real-time data can come from a variety of sources, including social media, IoT devices, infrastructure monitoring, call center monitoring, and more. Due to the breadth and depth of data being ingested from multiple sources, businesses look for solutions to protect …
Creating robust and reusable machine learning (ML) pipelines can be a complex and time-consuming process. Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. Creating and running a full pipeline during experimentation adds unwanted overhead and cost to the development lifecycle. In this post, we …
Read more “Reduce cost and development time with Amazon SageMaker Pipelines local mode”
Planes taxiing for long periods due to ground traffic — or circling the airport while awaiting clearance to land — don’t just make travelers impatient. They burn fuel unnecessarily, harming the environment and adding to airlines’ costs. Searidge Technologies, based in Ottawa, Canada, has created AI-powered software to help the aviation industry avoid such issues, …
Read more “Searidge Technologies Offers a Safety Net for Airports”
A central issue in machine learning is how to train models on sensitive user data. Industry has widely adopted a simple algorithm: Stochastic Gradient Descent with noise (a.k.a. Stochastic Gradient Langevin Dynamics). However, foundational theoretical questions about this algorithm’s privacy loss remain open — even in the seemingly simple setting of smooth convex losses over …
Read more “Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss”
Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing …
Read more “FLAIR: Federated Learning Annotated Image Repository”
Online commercial app marketplaces serve millions of apps to billions of users in an efficient manner. Bandit optimization algorithms are used to ensure that the recommendations are relevant, and converge to the best performing content over time. However, directly applying bandits to real-world systems, where the catalog of items is dynamic and continuously refreshed, is …
Read more “Two-Layer Bandit Optimization for Recommendations”
A functional data ecosystem must incorporate notions of Ontology in order to be scalable and sustainable. Editor’s note: This is the first post in the Palantir RFx Blog Series, which breaks down some of the key pillars of a data ecosystem using language commonly found in formal solicitations such as RFIs and RFPs. Each post …
Read more “Ontology: Finding meaning in data (Palantir RFx Blog Series, #1)”
This series tackles the rarely simple and often messy solicitation process. We explore how organizations can better evaluate digital transformation software. Welcome to the RFx Blog Series, which explores the question: how should commercial organizations evaluate digital transformation software? In this series, we use language commonly found in formal solicitations, including specific questions and functional …
Read more “Evaluating Software (Palantir RFx Blog Series, #0)”