32 citations · 94 across the 30 of their papers we have counts for
34 papers
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust
Leonid Erlygin, Vladimir Zholobov, Valeriia Baklanova +2
Machine learning models play a vital role in time series forecasting. These models, however, often overlook an important element: point uncertainty estimates. Incorporating these e…
ScaleFace: Uncertainty-aware Deep Metric Learning
Roman Kail, Kirill Fedyanin, Nikita Muravev +2
The performance of modern deep learning-based systems dramatically depends on the quality of input objects. For example, face recognition quality would be lower for blurry or corru…
QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise
Egor Shvetsov, Dmitry Osin, Alexey Zaytsev +4
There is a constant need for high-performing and computationally efficient neural network models for image super-resolution: computationally efficient models can be used via low-ca…
Effective training-time stacking for ensembling of deep neural networks
Polina Proscura, Alexey Zaytsev
Ensembling is a popular and effective method for improving machine learning (ML) models. It proves its value not only in classical ML but also for deep learning. Ensembles enhance…
Transfer learning for ensembles: reducing computation time and keeping the diversity
Ilya Shashkov, Nikita Balabin, Evgeny Burnaev +1
Transferring a deep neural network trained on one problem to another requires only a small amount of data and little additional computation time. The same behaviour holds for ensem…
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective
Gleb Bazhenov, Sergei Ivanov, Maxim Panov +2
The problem of out-of-distribution detection for graph classification is far from being solved. The existing models tend to be overconfident about OOD examples or completely ignore…