Machine Learning

Fast and Simple Image Search with Foundation Models

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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!

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Build an Automated Cross-Domain Question Answering System

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Build 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.

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The Importance of Metrics in Machine Learning and How to Use Them

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The Importance of Metrics in Machine Learning and How to Use Them

Metrics are critical in machine learning projects. They help a team to prioritize their resources and concentrate on a single, clear objective. I am always amazed to see that, once my team is aligned on a single metric to optimize, the speed and momentum we will be able to execute. In the end, we will usually be able to accomplish the goals that seem impossible in the beginning.

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Data Labeling of Images for Supervised Learning

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Data Labeling of Images for Supervised Learning

At Landing AI we observed how many projects took an unnecessarily long and painful process to complete. It was due to ambiguous defect definitions or poor labeling quality. In comparison, it will make the life of machine learning engineers much easier, and the whole project lifespan much shorter, by having a dataset with high quality labels. Therefore, it is very important to invest the time in the project’s early stage to clarify defect definitions and formalize labeling.

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Data Validation for Machine Learning - Paper Reading Note

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Data Validation for Machine Learning - Paper Reading Note

This paper reminds me of many time where our model in production perform strangely, so engineers have to spend hours investigate root causes and roll back or push for fixes. Lots of late night works as result of such mistakes. I agree with this paper that such data validation systems, if implemented correctly, can really help save significant amount of engineer hours by catching important errors proactively and diagnose model errors more efficiently.

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Designing Image Acquisition for Machine Vision

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Designing Image Acquisition for Machine Vision

At Landing AI, I have gone through several projects where we developed an end-to-end machine-learning system from “scratch'“. That means before we started on the project, there was no existing data collection procedure, so we had to start from zero and set up cameras.

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My First Week of CS330 at Stanford

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My 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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Tech Talk on Photography →

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Tech Talk on Photography

I did this tech talk at Landing AI this week and I’d like to share it out on my website. It’s about the core concepts in photography and a little on the recent trend of computational photography.

I removed a section that talks about the imaging solution design in one of our internal projects, due to IP restriction. I will later share a blog post on that topic.

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