Aytuğ Onan

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 105 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 80 citations into it; #33,945 of 35,345 by trust here, #25,809 by citations
• Natural Language Processing — 30 citations into it
• Information Systems — 28 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Statistical Learning Theory — 7 citations into it
• Statistics and Probability — 6 citations into it

Whom Aytuğ Onan trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Jeffrey L. Elman
2. Charu C. Aggarwal
3. Walaa Medhat
4. David D. Lewis
5. Yoshua Bengio
6. Yann LeCun
7. Tomas Mikolov
8. David Ruppert
9. Yoav Freund
10. Geoffrey E. Hinton

Who trusts Aytuğ Onan overall

1. Iqbal H. Sarker

Papers from Aytuğ Onan's perspective

Ordered by how strongly the researchers Aytuğ Onan trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach (2020) — 345 citations
• Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks (2020) — 376 citations
• A feature selection model based on genetic rank aggregation for text sentiment classification (2017) — 371 citations
• A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification (2016) — 344 citations
• Ensemble of keyword extraction methods and classifiers in text classification (2016) — 605 citations