Uizard Research
Explore the research and technology that makes Uizard possible.
The scientific contributions listed on this page showcase some of the technology powering Uizard. The presented methods, experiments, and results are published for educational purposes only.
We share the science, but we keep some secrets. 😉
September 30 2021
How deep learning is transforming design: NLP and CV applications
If you have ever tried creating a user interface, you probably quickly realized that design is hard.Choosing the right colors, using fonts that match, making your layout balanced…
March 27 2021
Improving UI Layout Understanding with Hierarchical Positional Encodings
How can we modify transformers for UI-centric tasks? Layout Understanding is a sub-field of AI that enables machines to better process the semantics and information within layouts such as user interfaces (UIs), text documents, forms, presentation slides, graphic design compositions, etc. Companies already invest extensive resources into their web/mobile applications' UI and user experience (UX), with Fast Company reporting that every $1 spent on UX can return between 2-100x on its investment.
February 17 2021
Generating Design Systems using Deep Learning
How do you enable people to easily design beautiful mobile and web apps when they have no design background? Easy: you just throw some neural networks at the problem! Well, kinda...
September 4 2020
From Research to Production with Deep Semi-Supervised Learning
Semi-Supervised Learning algorithms have shown great potential in training regimes when access to labeled data is scarce but access to unlabeled data is plentiful. In this post, we discuss how Semi-supervisedlearning approaches can be useful for machine learning production environmentsand the lessons we've learned using them at Uizard.
December 18 2019
RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms
Semi-Supervised Learning (SSL) algorithms have shown great potential in training regimes when access to labeled data is scarce but access to unlabeled data is plentiful. However, our experiments illustrate several shortcomings that prior SSL algorithms suffer from. In particular, poor performance when unlabeled and labeled data distributions differ. To address these observations, we develop RealMix, which achieves state-of-the-art results on standard benchmark datasets across different labeled and unlabeled set sizes while overcoming the aforementioned challenges.
June 5 2018
Code2Pix: Deep Learning Compiler for Graphical User Interfaces
This work is a collaboration between Uizard and UC Berkeley's Statistics Undergraduate Student Association. We provide a deep learning solution to the problem of generating Graphical User Interfaces (GUIs) from a textual description. Additionally, intermediary work hints at the possibility of a single textual to GUI renderer that works across multiple platforms.
November 30 2017
Teaching Machines to Understand User Interfaces
Semi-Supervised Learning algorithms have shown great potential in training regimes when access to labeled data is scarce but access to unlabeled data is plentiful. In this post, we discuss how Semi-supervisedlearning approaches can be useful for machine learning production environmentsand the lessons we've learned using them at Uizard.
May 23 2017
pix2code: Generating Code from a Graphical User Interface Screenshot
Semi-Supervised Learning algorithms have shown great potential in training regimes when access to labeled data is scarce but access to unlabeled data is plentiful. In this post, we discuss how Semi-supervisedlearning approaches can be useful for machine learning production environmentsand the lessons we've learned using them at Uizard.
