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
Multi-Task Sci-Fi: Havurtat
Each report we do features a science-fiction story inspired by the report topic. For our multi-task ...
Progress in machine learning interpretability
Our goal when we do research is to address capabilities and technologies that we expect to become pr...
New Dynamics for Topic Models
Topic models can extract key themes from large collections of documents in an unsupervised manner, w...
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