Research Group

Research Group

We help to research advanced models of Artificial

Intelligence in the following directions:

Generative models

We study the problems of generative models: time frame-by-frame connectivity of the generated video, convergence of models during training in denoising tasks, interpretability and controllability of generation processes within codecs.

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Predictive analytics

We create diagnostic methods for complex systems for timely and preventive impact.

Computer Vision

We investigate complex architectures (img2img, Transformer) for detection, segmentation, resolution enhancement, 3D reconstructions, etc.

Zero- and Few-shot approaches

We solve problems with a significant lack of data: there are several examples in the sample with a large number of classes.
We apply small-choice approaches to computer vision tasks (video stream detection and segmentation) and natural language processing (text classification and tagging).

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NLP Analytics

We create a complete set of text analysis methods: hard and soft clustering, summarization, classification and tagging, search tasks.

Wearable devices

We apply algorithms to data from inertial and biomedical sensors to solve problems of HAR, pressure detection, navigation, etc.

Working with sound

We use generative and discriminative deep learning approaches for denoising audio tracks, changing their style and detecting emotions.

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Codecs

We study the applicability of AI methods to encoding information of various nature: from the video stream to the human genome.

Interpreted models

We create deep learning models that can be interpreted and managed in the process of use.

Clients of research teams form the goals of R&D projects. We have highlighted the most common research requests:

Preliminary Research
Preliminary and detailed study of the subject area with the search for scientific articles, available data, and open source in order to obtain a base of materials for a quick dive into a new area and form requirements for future R&D results
Technological Pipeline Development
Development of a complete technological pipeline for solving a problem based on client data, based on existing algorithms and open source in order to obtain a strong basic solution for subsequent modifications and growth of target metrics

Negative Effect Removal
Elimination of a key negative effect in the operation of the existing model: from excessive sensitivity to fluctuations in the data to the quality of work in corner cases. The goal is to grow certain metrics of the customer model within the given constraints
Industrial Pipeline Development
Development of a Proof-of-the-Concept pipeline for solving an industrial business problem based on artificial intelligence algorithms in the absence of known approaches to its solution. The goal is the fundamental possibility of solving such a problem

Flexibility

Flexible approach to managing the project scope and transparency of the development process

Methodology

Using the methodology of AI projects to reduce risks and systematically achieve breakthrough results

Qualification

Deep understanding of both basic business needs and the use of SotA AI methods

Iteration

Short iterations with the transfer of a useful and exploitable result

Research results are fully transferred to the client

Software implementation
Easy to use library for with readable and reproducible code
Materials base
Base of materials with reviews for a quick dive into the field and a technical report

Trained Models
Parameters of trained models packed in the format required by the client
Anything else
We can prepare project artifacts in the format required by the customer