In this report, we focus on federated learning, an approach for training ML models on distributed edge node data while ensuring privacy and minimizing communication costs.
In this report, we focus on multi-task learning, a new approach to machine learning that allows algorithms to master tasks in parallel.
In this report, we show how using the semantic content of items can help solve common recommendation pitfalls such as the cold start problem, and open up new product possibilities.
In this report, we show how to make models interpretable without sacrificing their capabilities or accuracy.
Here, we show how to use probabilistic programming and Bayesian inference to easily build tools that make better predictions for more effective decision making.
Learn how to use deep learning and embeddings to make text computable for a variety of business applications and products.
Deep learning: Image analysis
This report explores the history and current state of deep learning, explains how to apply it, and predicts future developments.
Probabilistic methods for realtime streams
Here, we explore probabilistic methods that offer highly efficient models for extracting value from streams of data as they are generated.
Natural language generation
In this report, we look at how machine systems can turn highly structured data into human language narrative.
Read the Fast Forward Labs blog
Teaching machines to recognize faces: a tale of two outcomesMachine learning used for facial recognition can have both potentially life-saving and privacy-killi...
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