102 citations · 108 across the 8 of their papers we have counts for
5 papers
Large Learning Rates Improve Generalization: But How Large Are We Talking About?
Ekaterina Lobacheva, Eduard Pockonechnyy, Maxim Kodryan +1
Inspired by recent research that recommends starting neural networks training with large learning rates (LRs) to achieve the best generalization, we explore this hypothesis in deta…
Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems
Evgeny Bobrov, Sergey Troshin, Nadezhda Chirkova +4
Channel decoding, channel detection, channel assessment, and resource management for wireless multiple-input multiple-output (MIMO) systems are all examples of problems where machi…
Ultimate tensorization: compressing convolutional and FC layers alike
Timur Garipov, Dmitry Podoprikhin, Alexander Novikov +1
Convolutional neural networks excel in image recognition tasks, but this comes at the cost of high computational and memory complexity. To tackle this problem, [1] developed a tens…
Submodular relaxation for inference in Markov random fields
Anton Osokin, Dmitry Vetrov
In this paper we address the problem of finding the most probable state of a discrete Markov random field (MRF), also known as the MRF energy minimization problem. The task is know…
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Roman Shapovalov, Dmitry Vetrov, Anton Osokin +1
Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training. In this paper w…