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Tag: graph neural networks

Why OpenAI might be hedging its bets on quantum AI

Analysis Quantum computing has remained a decade away for over a decade now, but according to industry experts it may hold the secret to...

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Identifying defense coverage schemes in NFL’s Next Gen Stats

This post is co-written with Jonathan Jung, Mike Band, Michael Chi, and Thompson Bliss at the National Football League. A coverage scheme refers...

Part 3. AI — A New Approach to Research, Innovation and Entrepreneurship

Credit: “…it is the scale of physics where life emerges, but that life itself is a broader phenomenon recurring across different scales, from chemistry to...

Bio Eats World: Using AI to Take Bio Farther

In this episode, Vijay Pande speaks with Jakob Uszkoreit, the cofounder and CEO of Inceptive. Together, they discuss all things AI. We’re publishing the transcript...

Applying machine learning in financial markets: A review of state-of-the-art methods

Is it possible to predict the stock market using an AI-based stock price prediction system? Can machine learning truly be used for stock prediction? Stock...

Power recommendations and search using an IMDb knowledge graph – Part 3

This three-part series demonstrates how to use graph neural networks (GNNs) and Amazon Neptune to generate movie recommendations using the IMDb and Box Office...

What to Expect in 2023: AI and Graph Technology

2023 will bring exciting advances in AI and graph technology. One of the most compelling innovations will be the ability for quantum programs to be...

Power recommendations and search using an IMDb knowledge graph – Part 2

This three-part series demonstrates how to use graph neural networks (GNNs) and Amazon Neptune to generate movie recommendations using the IMDb and Box Office...

Israel’s cybersecurity startup CyVers secures $8M Seed funding to make Web3 and crypto exchanges safer

The wild news coming out of the crypto market triggered by the collapse of the now-bankrupt crypto exchange FTX has had a negative impact...

Busy GPUs: Sampling and pipelining method speeds up deep learning on large graphs

Graphs, a potentially extensive web of nodes connected by edges, can be used to express and interrogate relationships between data, like social connections, financial...

Machine learning in bioprocess development: from promise to practice

Mitchell T. et al.Machine learning.Annu. Rev. Comput. Sci. 1990; 4: 417-433Ender T.R. Balestrini-Robinson S. Surrogate modeling.in: Loper M.L. Modeling and Simulation in the Systems...

The DataHour: Bias and Fairness in NLP

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary...

Deep Learning Applications For Material Sciences: Methods, Recent Developments

New technical paper titled “Recent advances and applications of deep learning methods in materials science” from researchers at NIST, UCSD, Lawrence Berkeley National Laboratory,...

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