Hlib Babii

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 113 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it; #26,219 of 35,345 by trust here, #21,316 by citations
• Natural Language Processing — 79 citations into it; #5,470 of 5,688 by trust here, #3,687 by citations
• Information Systems — 73 citations into it; #5,995 of 8,771 by trust here, #3,402 by citations
• Software — 37 citations into it
• Code Generation & Program Synthesis — 32 citations into it
• Signal Processing — 23 citations into it
• Large Language Models — 20 citations into it
• Program Analysis & Verification — 19 citations into it

Whom Hlib Babii trusts overall

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

1. Miltiadis Allamanis
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Premkumar T. Devanbu
6. Ilya Sutskever
7. Alex Krizhevsky
8. Charles Sutton
9. Tomas Mikolov
10. Jimmy Ba

Who trusts Hlib Babii overall

1. Michael Pradel
2. Georgios Gousios
3. Kevin Jesse
4. Maliheh Izadi
5. Max Schäfer
6. F. Liu
7. Prem Devanbu
8. Yangruibo Ding
9. Seohyun Kim

Papers from Hlib Babii's perspective

Ordered by how strongly the researchers Hlib Babii 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code (2020) — 235 citations
• Can OpenAI's Codex Fix Bugs?: An evaluation on QuixBugs (2022) — 165 citations
• Modeling Vocabulary for Big Code Machine Learning (2019) — 21 citations