Today at Stanford, we released Levanter, a Jax-based framework for training foundation models. It is now open-source on Github under Apache 2.0.
Read MoreRelease of Levanter 1.0
Stanford
Fast and Simple Image Search with Foundation Models
In this blog post, I will walk you through how to build a fast and simple image search tool. I developed an image search application that uses multimodal foundation models to search for highly accurate and relevant results. By following this blog post and our code base, you can easily build one yourself!
Read MoreBuild an Automated Cross-Domain Question Answering System
Question Answering models are often used to automate the response to human questions by leveraging a knowledge base. My team at Stanford aims to build a robust question answering system that works across datasets from multiple domains. We explore two transformer-based Sparsely-Gated Mixture-of-Experts architectures and conduct an extensive ablation study to reach the best performance.
Read MoreMy First Week of CS330 at Stanford
Meta Learning is one of the promising lines of work that aim to solve the small data problems in machine learning field. Currently, many people working on AI are thinking day and night about how to scale AI systems and improve their profit margins. One main challenge to solve is how to quickly build an AI model that reaches human-level performance on classes with only a few samples.
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