Qihang Lin

Artificial Intelligence · 2010–2019

A researcher in the Science corpus: 11 papers; trusts 137 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 122 citations into it; #15,344 of 35,345 by trust here, #15,236 by citations
• Optimization & Training — 111 citations into it; #1,610 of 2,601 by trust here, #1,766 by citations
• Computational Mechanics — 85 citations into it; #4,817 of 10,001 by trust here, #3,652 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• Computational Theory and Mathematics — 24 citations into it
• Computer Networks and Communications — 23 citations into it
• Management Science and Operations Research — 21 citations into it
• Numerical Analysis — 19 citations into it

Whom Qihang Lin trusts overall

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

1. Yurii E. Nesterov
2. Ohad Shamir
3. Shai Shalev-Shwartz
4. Tong Zhang
5. Arkadi Nemirovski
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Francis R. Bach
9. John C. Duchi
10. Lin Xiao

Who trusts Qihang Lin overall

1. Jakub Konecný
2. Jialei Wang
3. Dong Yin
4. Michael I. Jordan
5. Maher Nouiehed
6. Nicolas Loizou
7. Chi Jin
8. Ching-pei Lee
9. Jason D. Lee
10. Sai Praneeth Karimireddy

Papers from Qihang Lin's perspective

Ordered by how strongly the researchers Qihang Lin 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Distributed Stochastic Variance Reduced Gradient Methods (2015) — 16 citations
• Model-Agnostic Linear Competitors - When Interpretable Models Compete and Collaborate with Black-Box Models (2019) — 4 citations
• A Unified Analysis of Stochastic Momentum Methods for Deep Learning (2018) — 140 citations
• Solving Weakly-Convex-Weakly-Concave Saddle-Point Problems as Successive Strongly Monotone Variational Inequalities (2018) — 36 citations
• BLOCK-NORMALIZED GRADIENT METHOD: AN EMPIRICAL STUDY FOR TRAINING DEEP NEURAL NETWORK (2018) — 36 citations
• Solving Weakly-Convex-Weakly-Concave Saddle-Point Problems as Weakly-Monotone Variational Inequality (2018) — 51 citations
• Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement (2017)
• Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization (2016) — 131 citations
• Generalized Inverse Classification (2016) — 68 citations
• Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity (2015) — 53 citations
• Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso (2010) — 111 citations