Phu Mon Htut
Artificial Intelligence · 2018–2023
A researcher in the Science corpus: 11 papers; trusts 252 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 257 citations into it; #15,636 of 35,345 by trust here, #4,888 by citations
- Natural Language Processing — 211 citations into it; #2,918 of 5,688 by trust here, #1,487 by citations
- Computer Vision and Pattern Recognition — 96 citations into it
- Evaluation & Benchmarks — 93 citations into it; #2,568 of 3,147 by trust here, #464 by citations
- Large Language Models — 38 citations into it
- Representation & Self-Supervised Learning — 37 citations into it
- Optimization & Training — 18 citations into it
- General Social Sciences — 12 citations into it
Whom Phu Mon Htut trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Samuel R. Bowman
- Jimmy Ba
- Kaiming He
- Yann LeCun
- Ruslan Salakhutdinov
Who trusts Phu Mon Htut overall
- Yair Feldman
- Yonatan Belinkov
- Anna Rogers
- Rajarshi Das
- Zhiyong Wu
- Fengbin Zhu
- Wenhan Xiong
- Marta Ruiz Costa-jussà
- Yada Pruksachatkun
- Ran El-Yaniv
Papers from Phu Mon Htut's perspective
Ordered by how strongly the researchers Phu Mon Htut trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- BBQ: A hand-built bias benchmark for question answering (2021) — 808 citations
- Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020) — 210 citations
- Generalized Inner Loop Meta-Learning (2019) — 169 citations
- Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (2019) — 133 citations
- jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models (2020) — 97 citations
- Do Attention Heads in BERT Track Syntactic Dependencies? (2019) — 151 citations
- English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too (2020) — 69 citations
- Training a Ranking Function for Open-Domain Question Answering (2018) — 49 citations
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
- Comparing Test Sets with Item Response Theory (2021) — 64 citations