Identifying and Correcting Label Bias in Machine Learning

As machine learning (ML) becomes more effective and widespread it is becoming more prevalent in systems with real-life impact, from loan recommendations to job application decisions. With the growing usage comes the risk of bias – biased training data could lead to biased ML algorithms, which in turn could perpetuate discrimination and bias in society. […]

GPipe – Training Giant Neural Nets using Pipeline Parallelism

In recent years the size of machine learning datasets and models has been constantly increasing, allowing for improved results on a wide range of tasks. At the same time hardware acceleration (GPUs, TPUs) has also been improving but at a significantly slower pace. The gap between model growth and hardware improvement has increased the importance […]