Nikolaos Pappas
Artificial Intelligence · with Smith · 2016–2021
A researcher in the Science corpus: 8 papers; trusts 475 researchers and is trusted by 42 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 273 citations into it; #5,897 of 35,345 by trust here, #3,957 by citations
- Natural Language Processing — 205 citations into it; #3,572 of 5,688 by trust here, #2,172 by citations
- Computer Vision and Pattern Recognition — 125 citations into it; #8,700 of 21,575 by trust here, #4,502 by citations
- Model Architectures — 98 citations into it; #627 of 2,278 by trust here, #491 by citations
- Representation & Self-Supervised Learning — 42 citations into it
- Efficiency & Compression — 22 citations into it; #540 of 1,264 by trust here, #342 by citations
- Evaluation & Benchmarks — 20 citations into it
- Optimization & Training — 20 citations into it
Whom Nikolaos Pappas trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ilya Sutskever
- Kyunghyun Cho
- Richard Socher
- Sepp Hochreiter
- Yann LeCun
- Alex Krizhevsky
- Tomas Mikolov
Who trusts Nikolaos Pappas overall
- Tri Dao
- Lesly Miculicich
- Jungo Kasai
- Tongtong Su
- Peng Zhou
- Songlin Yang
- J. Ainslie
- Shuhao Cao
- Hao Peng
- Yi Tay
Papers from Nikolaos Pappas's perspective
Ordered by how strongly the researchers Nikolaos Pappas 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention (2020) — 2,953 citations
- Random Feature Attention (2021) — 447 citations
- Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation (2020) — 156 citations
- Document-Level Neural Machine Translation with Hierarchical Attention Networks (2018) — 292 citations
- Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation (2020) — 51 citations
- Multilingual Hierarchical Attention Networks for Document Classification (2017) — 145 citations
- Multilingual Visual Sentiment Concept Matching (2016) — 15 citations
- Conference on Empirical Methods in Natural Language Processing Proceedings of the Fourth International Workshop on Natural Language Processing for Social Media Socialnlp@emnlp2016 Chairs' Welcome Identifying and Categorizing Disaster-related Tweets Why Do They Leave: Modeling Participation in Online