Jiyan Yang

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 11 papers; trusts 187 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 235 citations into it; #19,582 of 35,345 by trust here, #9,760 by citations
• Computer Vision and Pattern Recognition — 157 citations into it
• Information Systems — 81 citations into it
• Optimization & Training — 76 citations into it; #1,830 of 2,601 by trust here, #1,072 by citations
• Recommender Systems — 31 citations into it
• Model Architectures — 30 citations into it
• Computational Mechanics — 25 citations into it; #5,948 of 10,001 by trust here, #4,749 by citations
• Statistical Learning Theory — 24 citations into it

Whom Jiyan Yang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Andrea Vedaldi
5. Andrew Zisserman
6. Haim Avron
7. Li Fei-Fei
8. Kaiming He
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Jiyan Yang overall

1. David Lopez-Paz
2. Nicolas Loizou
3. Francis R. Bach
4. Raghu Bollapragada
5. Haim Avron
6. Frank E. Curtis
7. Alessandro Rudi
8. Shusen Wang
9. Albert S. Berahas
10. Haishan Ye

Papers from Jiyan Yang's perspective

Ordered by how strongly the researchers Jiyan Yang 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Study of BFLOAT16 for Deep Learning Training (2019) — 492 citations
• Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems (2020) — 107 citations
• Random Laplace Feature Maps for Semigroup Kernels on Histograms (2014) — 54 citations
• DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction (2022) — 17 citations
• Software-hardware co-design for fast and scalable training of deep learning recommendation models (2021) — 209 citations
• High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models (2021) — 49 citations
• Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction (2020) — 94 citations
• CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery (2020) — 47 citations
• Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems (2019) — 114 citations
• Sub-sampled Newton Methods with Non-uniform Sampling (2016) — 124 citations
• Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels (2014) — 178 citations