Slava Shechtman
Artificial Intelligence · 2019–2021
A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 33 citations into it; #11,807 of 35,345 by trust here, #19,873 by citations
- Signal Processing — 24 citations into it; #7,037 of 7,572 by trust here, #4,373 by citations
- Speech & Audio — 18 citations into it; #517 of 1,076 by trust here, #1,009 by citations
- Natural Language Processing — 12 citations into it
Whom Slava Shechtman trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jean-Marc Valin
- Diederik P. Kingma
- Mike Schuster
- Sepp Hochreiter
- Jan Skoglund
- Jimmy Ba
- Geoffrey E. Hinton
- J. Urgen Schmidhuber
- Masanori Morise
- Jonathan Shen
Who trusts Slava Shechtman overall
- Tao Tu
- Alexander H. Liu
- Xu Tan
- Adrian La'ncucki
- Tao Qin
- Hung-yi Lee
- Raul Fernandez
- Yuan-Jui Chen
- Frank K. Soong
- Hiroki Kanagawa
Papers from Slava Shechtman's perspective
Ordered by how strongly the researchers Slava Shechtman trusts endorse each paper. Same corpus, a different order for every researcher.
- 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
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Supervised and unsupervised approaches for controlling narrow lexical focus in sequence-to-sequence speech synthesis (2021) — 19 citations
- Sequence to Sequence Neural Speech Synthesis with Prosody Modification Capabilities (2019) — 35 citations
- High quality, lightweight and adaptable TTS using LPCNet (2019) — 55 citations