Oleksii Kuchaiev
Artificial Intelligence · 2014–2021
A researcher in the Science corpus: 11 papers; trusts 259 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 173 citations into it; #9,646 of 35,345 by trust here, #7,945 by citations
- Natural Language Processing — 89 citations into it; #4,487 of 5,688 by trust here, #2,767 by citations
- Computer Vision and Pattern Recognition — 87 citations into it; #16,299 of 21,575 by trust here, #3,035 by citations
- Model Architectures — 58 citations into it; #916 of 2,278 by trust here, #1,269 by citations
- Signal Processing — 57 citations into it; #2,198 of 7,572 by trust here, #1,504 by citations
- Optimization & Training — 40 citations into it; #2,107 of 2,601 by trust here, #378 by citations
- Speech & Audio — 40 citations into it; #838 of 1,076 by trust here, #590 by citations
- Evaluation & Benchmarks — 19 citations into it
Whom Oleksii Kuchaiev trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ilya Sutskever
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Alex Graves
- Diederik P. Kingma
- Sepp Hochreiter
- Karen Simonyan
- Oriol Vinyals
- J. Urgen Schmidhuber
Who trusts Oleksii Kuchaiev overall
- Tao Lei
- Boris Ginsburg
- Stanislav Beliaev
- Yu Zhang
- Lukasz Kaiser
- Alec Radford
- Scott Gray
- Adam Polyak
- Vitaly Lavrukhin
- Jason Li
Papers from Oleksii Kuchaiev's perspective
Ordered by how strongly the researchers Oleksii Kuchaiev trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Mixed Precision Training (2017) — 2,457 citations
- Factorization tricks for LSTM networks (2017) — 122 citations
- Jasper: An End-to-End Convolutional Neural Acoustic Model (2019) — 288 citations
- OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models (2018) — 33 citations
- NeMo: a toolkit for building AI applications using Neural Modules (2019) — 411 citations
- Quartznet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions (2019) — 328 citations
- Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq (2018) — 52 citations
- SPGISpeech: 5, 000 hours of transcribed financial audio for fully formatted end-to-end speech recognition (2021) — 113 citations
- Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks (2019) — 108 citations
- Training Deep AutoEncoders for Recommender Systems (2018) — 5 citations
- An introduction to computational networks and the computational network toolkit (invited talk) (2014) — 402 citations