Stephen Merity
Artificial Intelligence · 2016–2018
A researcher in the Science corpus: 6 papers; trusts 557 researchers and is trusted by 1,626 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 145 citations into it; #320 of 35,345 by trust here, #1,081 by citations
- Natural Language Processing — 81 citations into it; #256 of 5,688 by trust here, #351 by citations
- Model Architectures — 68 citations into it; #166 of 2,278 by trust here, #232 by citations
- Computer Vision and Pattern Recognition — 62 citations into it; #1,641 of 21,575 by trust here, #3,156 by citations
- Optimization & Training — 34 citations into it; #320 of 2,601 by trust here, #483 by citations
- Multimodal Learning — 14 citations into it; #140 of 1,211 by trust here, #377 by citations
- Large Language Models — 10 citations into it; #266 of 799 by trust here, #549 by citations
- Signal Processing — 10 citations into it
Whom Stephen Merity trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Richard Socher
- Alex Krizhevsky
- Tomas Mikolov
- Jimmy Ba
- Yann LeCun
- Sepp Hochreiter
Who trusts Stephen Merity overall
- Richard Socher
- Jeremy Howard
- Nitish Shirish Keskar
- Yoshua Bengio
- Caiming Xiong
- Percy Liang
- Zihang Dai
- Ruslan Salakhutdinov
- Chengyue Gong
- Sebastian Ruder
Papers from Stephen Merity's perspective
Ordered by how strongly the researchers Stephen Merity trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- 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
- 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)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Pointer Sentinel Mixture Models (2016) — 4,314 citations
- Dynamic Memory Networks for Visual and Textual Question Answering (2016) — 770 citations
- Regularizing and Optimizing LSTM Language Models (2017) — 1,163 citations
- Quasi-Recurrent Neural Networks (2016) — 474 citations
- An Analysis of Neural Language Modeling at Multiple Scales (2018) — 178 citations
- Revisiting Activation Regularization for Language RNNs (2017) — 45 citations