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Machine Intelligence Laboratory

Many Computer Vision tasks are focused on extracting and detecting objects on an image, and some of our projects are also related to this task.

The latest project by our team is the Optical Character Recognition (OCR) — decisions testing system. OCR — decision consists of the set of methods and models for image text detection and character recognition. This system output is the list of quad coordinates (boxes) hedging the words and the text inside them.

By the end of the article you will know:

  1. The reason why building OCR — decisions testing system is not a trivial matter.

Sometimes, in the process of working with ML models, funny things may happen, ranging from an odd output to amusing errors in models' performance. While collecting such things, a certain picture starts forming and we begin to observe the interesting patterns and understand how can we not only laugh at these cases but also learn and take advantage of them.

In this tutorial, we are going to tell you how can you quickly detect bugs in models' performance and fight them.

1. Playing "may haves"

The most proper way of detecting bugs in a model's performance is to go through the results, which are…

Since the very beginning of the company, MIL Team has been working as the external service team in cooperation with the large companies’ R&D centres, like Huawei and Samsung. We have successfully implemented 16 R&D projects so far, including large open-source projects and small MVP decisions.

Here comes the question, why the companies possessing valuable human and technological resources choose to initiate the projects with our team? In this article, we are going to discuss 5 main reasons to get involved with an outsource-team in developing an internal product. …

By reading this tutorial you will know how to use TopicNet and ARTM libraries for building topic models with regularizers.

Why do we need regularizers?

Building the topic model for a collection of documents helps us to recognize the topics of the documents and determine the words describing these topics. If we want the model to have certain properties, regularizers are the best solution.

What about the relative coefficients of regularization?

The first step of building a regularized topic model is to select the hyperparameter — regularization coefficient τ showing how strong the regularizer affects the model. The optimization of this coefficient is a challenging task as its value depends on various…

Machine Intelligence Laboratory was founded in 2017, since then, the members of our team have been tirelessly working: we implemented 16 R&D projects, published 3 Q2 articles, organised 9 events, started the Science Club and MVP projects development. We are happy to share our experience and open the doorway into the lab’s inner workings for you to have a possibility not only evaluate the finished product but also observe the development process.

Ever since the lab was founded, MIL. Team has been successfully solving business-related problems for big companies. For this aim, we apply AI approaches implementing both research and…

All of us get to know dozens of audial information ranging from voice messages in Telegram to the lectures recorded by your classmates. Wouldn’t it be nice to “turn off” all the irrelevant noises and enjoy the essence? Well, that’s possible! The programs based on machine learning algorithms are the key solution. How can we evaluate their performance, though?

By the end of the article you will know about:

  • the advantages of manual annotation for audio quality evaluation over automatic methods;
  • two main subjective metrics applied in the audio denoising task;
  • the features of noise intensity level evaluation;
  • the ways…

Machine Intelligence Laboratory

MIL. Team is the united and professional group of researchers, developers and engineers conducting R&D projects in the field of Artificial Intelligence.

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