Ben Bogin

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 7 papers; trusts 164 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 192 citations into it; #30,176 of 35,345 by trust here, #5,880 by citations
• Natural Language Processing — 155 citations into it
• Computer Vision and Pattern Recognition — 79 citations into it
• Evaluation & Benchmarks — 67 citations into it
• Information Systems — 25 citations into it
• Model Architectures — 12 citations into it
• Optimization & Training — 5 citations into it

Whom Ben Bogin trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Alex Krizhevsky
6. Dzmitry Bahdanau
7. Kaiming He
8. Yann LeCun
9. Michael Collins
10. Kyunghyun Cho

Who trusts Ben Bogin overall

1. Haoyang Li
2. Pengcheng Yin
3. Bailin Wang
4. Jiaqi Guo
5. Tianbao Xie
6. Zhuang Li
7. Jing Zhang
8. Andreas Madsen
9. Graham Neubig
10. Jichuan Zeng

Papers from Ben Bogin's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020) — 462 citations
• Language Generation with Recurrent Generative Adversarial Networks without Pre-training (2017) — 105 citations
• Evaluating NLP Models via Contrast Sets (2020) — 85 citations
• Obtaining Faithful Interpretations from Compositional Neural Networks (2020) — 43 citations
• Global Reasoning over Database Structures for Text-to-SQL Parsing (2019) — 115 citations
• Grammar-based Neural Text-to-SQL Generation (2019) — 65 citations
• Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research (2024) — 519 citations