Lichan Hong

Artificial Intelligence · 2010–2023

A researcher in the Science corpus: 15 papers; trusts 230 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 249 citations into it; #34,490 of 35,345 by trust here, #4,445 by citations
• Computer Vision and Pattern Recognition — 145 citations into it; #18,058 of 21,575 by trust here, #6,954 by citations
• Information Systems — 107 citations into it; #5,668 of 8,771 by trust here, #224 by citations
• Representation & Self-Supervised Learning — 46 citations into it; #3,126 of 3,145 by trust here, #747 by citations
• Natural Language Processing — 43 citations into it; #3,583 of 5,688 by trust here, #3,282 by citations
• Management Science and Operations Research — 41 citations into it; #7,762 of 11,946 by trust here, #296 by citations
• Model Architectures — 40 citations into it
• Recommender Systems — 37 citations into it

Whom Lichan Hong trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Kaiming He
5. Alex Krizhevsky
6. Yann LeCun
7. Ilya Sutskever
8. R. Caruana
9. Haewoon Kwak
10. Ross B. Girshick

Who trusts Lichan Hong overall

1. Mark Graham
2. Alexandra Olteanu
3. Utsab Barman
4. Isaac L. Johnson
5. Brent J. Hecht
6. Scott A. Hale
7. Carlos Castillo
8. Bart Thomee
9. Bo Han
10. Zhe Zhao

Papers from Lichan Hong's perspective

Ordered by how strongly the researchers Lichan Hong 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts (2018) — 1,663 citations
• Recommending what video to watch next: a multitask ranking system (2019) — 418 citations
• Wide & Deep Learning for Recommender Systems (2016) — 4,198 citations
• DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning (2021) — 204 citations
• Sampling-bias-corrected neural modeling for large corpus item recommendations (2019) — 344 citations
• Fairness in Recommendation Ranking through Pairwise Comparisons (2019) — 457 citations
• Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network (2010) — 1,299 citations
• Recommender Systems with Generative Retrieval (2023) — 342 citations
• Small Towers Make Big Differences (2020) — 11 citations
• Self-supervised Learning for Large-scale Item Recommendations (2020) — 329 citations
• DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems (2020) — 865 citations
• Learning to Embed Categorical Features without Embedding Tables for Recommendation (2020) — 88 citations
• Factorized Deep Retrieval and Distributed TensorFlow Serving (2018)
• Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles (2011) — 576 citations
• Language Matters In Twitter: A Large Scale Study (2011) — 256 citations