Blog Posts

While we are proud of the adoption that weak supervision has seen in industry, we are just as excited about the impact it can have in science and medicine. This post reviews recent work that leverages weak supervision to provide real-world value to scientists and clinicians. [Read More]

Multi-task learning applied to heterogeneous task data can often result in suboptimal models (or negative transfer in more technical terms). We provide conceptual insights to explain why negative transfer happens. Based on the explanation, we propose methods to improve multi-task training. Based on our work in ICLR’20. [Read More]

Modern machine learning models require a lot of training data to be successful. Over the past few years, we've been studying programatically creating labels with weak supervision to address this training data bottleneck; instead of relying on manual labels, data programming uses weak supervision---multiple noisy label sources---to automatically generate labeled... [Read More]

Four years ago, the idea that you could use a statistical process to help people label training data from noisy sources sounded absolutely crazy. But since then, the ideas that we formalized into data programming and weak supervision in Snorkel have had a surprisingly broad degree of impact. In fact,... [Read More]

Data augmentation techniques have proven powerful in building machine learning models in applications such as image and text classification. However, most of the machine learning research today is still carried out solving fixed tasks. In the real world, machine learning models in deployment can fail in various ways, due to... [Read More]