604 citations · 2.5k across the 63 of their papers we have counts for
7 papers · 1 filter
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…
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
Kirill Struminsky, Artyom Gadetsky, Denis Rakitin +2
Structured latent variables allow incorporating meaningful prior knowledge into deep learning models. However, learning with such variables remains challenging because of their dis…
Automating Control of Overestimation Bias for Reinforcement Learning
Arsenii Kuznetsov, Alexander Grishin, Artem Tsypin +3
Overestimation bias control techniques are used by the majority of high-performing off-policy reinforcement learning algorithms. However, most of these techniques rely on pre-defin…
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study
Pavel Andreev, Alexander Fritzler, Dmitry Vetrov
Generative adversarial networks (GANs) have an enormous potential impact on digital content creation, e.g., photo-realistic digital avatars, semantic content editing, and quality e…
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations
Arsenii Ashukha, Andrei Atanov, Dmitry Vetrov
Averaging predictions over a set of models -- an ensemble -- is widely used to improve predictive performance and uncertainty estimation of deep learning models. At the same time,…
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova +2
Training neural networks with batch normalization and weight decay has become a common practice in recent years. In this work, we show that their combined use may result in a surpr…
Towards Practical Credit Assignment for Deep Reinforcement Learning
Vyacheslav Alipov, Riley Simmons-Edler, Nikita Putintsev +2
Credit assignment is a fundamental problem in reinforcement learning, the problem of measuring an action's influence on future rewards. Explicit credit assignment methods have the…