Leveraging 3D Engines for Data Generation in Deep Learning
Applied to the specific case of scene text detection.
In the field of computer vision, synthetic data generation is especially interesting since the number of relevant resources and tools have grown and improved significantly over the years. The development has not been in the field of machine learning though, but rather in game engines such as Unreal Engine, Blender and Unity. Often produced by professional designers, realistic scenes are produced offering great details.Read more
Explainable AI in medicine: Detecting AF in ECG data
Machine learning models usually do not explain their predictions. This is a significant barrier to adaptation in domains like medicine, where understanding how the model works is vital.
In this blog post, we take a look at how explainability techniques can be used on a deep learning model predicting atrial fibrillation from sinus ECG (electrocardiogram) data.Read more
Zero-Shot Learning in NLP
Recent transformer-based language models, such as RoBERTa, ALBERT, and OpenAI GPT, have shown a powerful ability to learn universal language representations. However, in many real-world scenarios, the lack and cost of labeled data is still a limiting factor.
Zero-shot learning (ZSL) is a form of transfer learning that aims to learn patterns from labeled data in order to detect classes that were never seen during training. As the lack of labeled data and scalability is a regular problem in machine learning applications, ZSL has gained much attention in recent years thanks to its ability to predict unseen classes.
MLOps – Deploying a recommender system in a production environment
When developing an integrated ML system, surprisingly little amount of time is spent on actual model development. The majority of time is spent creating the right prerequisites for model deployment – that is MLOps.
The following sections describe how we work with MLOps at Ahlsell. We’ll go through data infusion and processing, modeling, and evaluation pipelines as well as how we put it all together in an automated CI/CD pipeline.Read more
Graph neural networks
Graph Neural Networks (GNNs) are neural network architectures that learn on graph-structured data. In recent years, GNN's have rapidly improved in terms of ease-of-implementation and performance, and more success stories being reported. In this post, we will briefly introduce these networks, their development, and the features that have lead to their success.
We will dive deeper into three use-cases, citation networks and drug discovery, using the package Deep graph library (DGL), and e-commerce using Pytorch geometric.Read more
Enabling real time e-sport tracking with streaming video object detection
The esports industry has seen tremendous growth lately. Each tournament is streamed live and reaches several million viewers all around the world, increasing the demand for live updates of games, players, e.g. for live betting and more.
To improve the experience of watching these tournaments, Abios Gaming provides an API for live information on games, teams, and players. To strengthen Abios Gaming’s offer, and to enable real-time monitoring of esport games, Modulai joined forces with their tech team to build a deep learning object detection solution, to extract information on-the-fly from real-time video streams of gaming tournaments.Read more
Classification of hypoglycemia causes in blood sugar time series
We give an overview of a study conducted in collaboration with Daniel Espes and Per-Ola Carlsson at Uppsala University aiming to improve the treatment of type 1 diabetes.
Type 1 diabetes is one of the most common chronic disorders among children and adolescents but affects people of all ages and globally more than 5 million people are affected.Read more