Yilin Shen

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 14 papers; trusts 678 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 250 citations into it; #11,180 of 35,345 by trust here, #8,077 by citations
• Computer Vision and Pattern Recognition — 161 citations into it; #9,262 of 21,575 by trust here, #6,326 by citations
• Natural Language Processing — 85 citations into it; #3,470 of 5,688 by trust here, #5,092 by citations
• Efficiency & Compression — 36 citations into it
• Meta & Continual Learning — 31 citations into it; #647 of 1,041 by trust here, #568 by citations
• Safety, Robustness & Interpretability — 31 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Model Architectures — 24 citations into it

Whom Yilin Shen trusts overall

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

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

Who trusts Yilin Shen overall

1. Zixuan Ke
2. Fei Mi
3. Rui Huang
4. Yixuan Li
5. Qi Zhu
6. Xiangyu Zeng
7. Yijia Shao
8. Haowei Lin
9. Yiyou Sun
10. Hwa-Yeon Kim

Papers from Yilin Shen's perspective

Ordered by how strongly the researchers Yilin Shen 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded (2019) — 299 citations
• Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning (2021) — 213 citations
• Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data (2020) — 700 citations
• Enhancing the Robustness of Prior Network in Out-of-Distribution Detection (2018) — 9 citations
• Language model compression with weighted low-rank factorization (2022) — 241 citations
• Automatic Mixed-Precision Quantization Search of BERT (2021) — 26 citations
• Iterative Delexicalization for Improved Spoken Language Understanding (2019) — 8 citations
• A Progressive Model to Enable Continual Learning for Semantic Slot Filling (2019) — 36 citations
• Adversarial Multi-label Prediction for Spoken and Visual Signal Tagging (2019)
• SLiQA-I: Towards Cold-start Development of End-to-end Spoken Language Interface for Question Answering (2019) — 8 citations
• CRUISE: Cold-Start New Skill Development via Iterative Utterance Generation (2018) — 9 citations
• Learning Out-of-Vocabulary Words in Intelligent Personal Agents (2018) — 7 citations
• User Information Augmented Semantic Frame Parsing Using Progressive Neural Networks (2018) — 7 citations
• A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (2018) — 216 citations