Di Jin

Artificial Intelligence · with Jin · 2017–2022

A researcher in the Science corpus: 13 papers; trusts 347 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 428 citations into it; #13,638 of 35,345 by trust here, #4,119 by citations
• Natural Language Processing — 271 citations into it; #5,406 of 5,688 by trust here, #1,443 by citations
• Computer Vision and Pattern Recognition — 142 citations into it
• Evaluation & Benchmarks — 71 citations into it
• Statistical and Nonlinear Physics — 71 citations into it
• Representation & Self-Supervised Learning — 49 citations into it; #2,968 of 3,145 by trust here, #1,204 by citations
• Safety, Robustness & Interpretability — 49 citations into it; #699 of 2,251 by trust here, #660 by citations
• Signal Processing — 42 citations into it

Whom Di Jin trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Yann LeCun
7. Sepp Hochreiter
8. Richard Socher
9. Kaiming He
10. Jacob Devlin

Who trusts Di Jin overall

1. Linyang Li
2. Siddhant Garg
3. Adyasha Maharana
4. Michihiro Yasunaga
5. M. V. Koroteev
6. Eleftheria Briakou
7. Chenglei Si
8. Xipeng Qiu
9. Santiago González-Carvajal
10. Goutham Ramakrishnan

Papers from Di Jin's perspective

Ordered by how strongly the researchers Di Jin 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. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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

• Publicly Available Clinical BERT Embeddings (2019) — 2,728 citations
• What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams (2020) — 1,893 citations
• Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment (2019) — 1,382 citations
• GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022) — 43 citations
• Deep Learning for Text Style Transfer: A Survey (2020) — 303 citations
• IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation (2019) — 77 citations
• SpeechMix - Augmenting Deep Sound Recognition Using Hidden Space Interpolations (2020) — 19 citations
• Graph Neural Networks for Graphs With Heterophily: A Survey (2022) — 315 citations
• A Comprehensive Survey on Community Detection With Deep Learning (2021) — 454 citations
• Augmenting NLP models using Latent Feature Interpolations (2020) — 30 citations
• TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP (2020) — 949 citations
• Is BERT Really Robust? Natural Language Attack on Text Classification and Entailment (2019)
• PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings (2017) — 4 citations