Remove 2022 Remove Algorithm Remove Clustering
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Google Research, 2022 & beyond: Algorithmic advances

Google Research AI blog

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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Differentially private clustering for large-scale datasets

Google Research AI blog

Posted by Vincent Cohen-Addad and Alessandro Epasto, Research Scientists, Google Research, Graph Mining team Clustering is a central problem in unsupervised machine learning (ML) with many applications across domains in both industry and academic research more broadly. When clustering is applied to personal data (e.g.,

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“Looking beyond GPUs for DNN Scheduling on Multi-Tenant Clusters” paper summary

Mlearning.ai

Enterprises, research and development teams shared GPU clusters for this purpose. on the clusters to get the jobs and allocate GPUs, CPUs, and system memory to the submitted tasks by different users. The authors of [1] propose a resource-sensitive scheduler for shared GPU cluster. SLURM, LFS, Kubernetes, Apache YARN, etc.)

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KDnuggets News, April 6: 8 Free MIT Courses to Learn Data Science Online; The Complete Collection Of Data Repositories – Part 1

KDnuggets

8 Free MIT Courses to Learn Data Science Online; The Complete Collection Of Data Repositories - Part 1; DBSCAN Clustering Algorithm in Machine Learning; Introductory Pandas Tutorial; People Management for AI: Building High-Velocity AI Teams.

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Five machine learning types to know

IBM Journey to AI blog

Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks. Instead of using explicit instructions for performance optimization, ML models rely on algorithms and statistical models that deploy tasks based on data patterns and inferences. What is machine learning?

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FriendlyCore: A novel differentially private aggregation framework

Google Research AI blog

Posted by Haim Kaplan and Yishay Mansour, Research Scientists, Google Research Differential privacy (DP) machine learning algorithms protect user data by limiting the effect of each data point on an aggregated output with a mathematical guarantee. Two adjacent datasets that differ in a single outlier. are both close to a third point ?

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Ending an Ugly Chapter in Chip Design

Flipboard

The crux of the clash was whether Google’s AI solution to one of chip design’s thornier problems was really better than humans or state-of-the-art algorithms. In Circuit Training and Morpheus, a separate algorithm fills in the gaps with the smaller parts, called standard cells. The agent places one block at a time on the chip canvas.

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