NLMK, “The prediction of rolls wear in the hot strip mill”
Full description on Kaggle
CSD Lab, MIPT, “Computer vision and reinforcement learning”
Abstract. Creation of an improved program for the accident-free movement of a car at an unregulated crossroad using video from an unmanned aerial vehicle. This will require the usage of deep learning approaches to recognize cars on the images and reinforcement training to plan their movement.
Huawei, “Metric learning for facial descriptors”
The problem: Construct and implement the metric (similarity function) which brings us the better percent of recognition (identification\verification) of human faces on photos in comparison to L2 baseline.
Prerequisites: Python 3.
Abstract: State-of-the-art recognition (identification) methods generally use the following paradigm: firstly vector (descriptor) of some object (e.g. photo) is calculated using deep neural net, and then using some simple metric (e.g. L2 in Euclidian space) we select the closest one(-s) descriptor from the preliminary computed set of descriptors of objects using the same neural net, among which we’ll search the appropriate pair request<->DB. For verification systems the metric is applied between the descriptor of object-request and the preliminary computed for this object descriptor-template, and a result conclusion on whether this is the same object or not is done based on comparison of this metric with some predefined threshold.