Stanislau Semeniuta
Artificial Intelligence · 2016–2018
A researcher in the Science corpus: 3 papers; trusts 190 researchers and is trusted by 51 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 75 citations into it; #4,984 of 35,345 by trust here, #8,706 by citations
- Computer Vision and Pattern Recognition — 51 citations into it; #5,872 of 21,575 by trust here, #8,409 by citations
- Generative Models — 37 citations into it; #605 of 1,956 by trust here, #989 by citations
- Natural Language Processing — 36 citations into it; #2,629 of 5,688 by trust here, #3,421 by citations
- Model Architectures — 24 citations into it; #912 of 2,278 by trust here, #1,433 by citations
- Optimization & Training — 13 citations into it
- Evaluation & Benchmarks — 7 citations into it; #1,270 of 3,147 by trust here, #2,482 by citations
- Statistical and Nonlinear Physics — 6 citations into it
Whom Stanislau Semeniuta trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Sepp Hochreiter
- Alex Krizhevsky
- Tomas Mikolov
- Jimmy Ba
- Yann LeCun
- J. Urgen Schmidhuber
Who trusts Stanislau Semeniuta overall
- David R Ha
- Van-Khanh Tran
- Jiacheng Xu
- Suman V. Ravuri
- Shimi Salant
- Jian Guan
- Yuzhou Liu
- Yumo Xu
- Asli Celikyilmaz
- Zhiting Hu
Papers from Stanislau Semeniuta's perspective
Ordered by how strongly the researchers Stanislau Semeniuta 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Recurrent Dropout without Memory Loss (2016) — 227 citations
- A Hybrid Convolutional Variational Autoencoder for Text Generation (2017) — 267 citations
- On Accurate Evaluation of GANs for Language Generation (2018) — 87 citations