Lingjiao Chen
Artificial Intelligence · 2016–2023
A researcher in the Science corpus: 10 papers; trusts 194 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 149 citations into it; #13,596 of 35,345 by trust here, #10,065 by citations
- Computer Vision and Pattern Recognition — 74 citations into it
- Optimization & Training — 67 citations into it
- Natural Language Processing — 27 citations into it
- Evaluation & Benchmarks — 26 citations into it
- Computer Networks and Communications — 24 citations into it
- ML Systems & Hardware — 24 citations into it; #169 of 575 by trust here, #323 by citations
- Computational Mechanics — 22 citations into it
Whom Lingjiao Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Yann LeCun
- Yoshua Bengio
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- L. Eon Bottou
- Karen Simonyan
- Ilya Sutskever
- Andrew Zisserman
Who trusts Lingjiao Chen overall
- Deepesh Data
- Cong Xie
- Xinyang Cao
- Suhas N. Diggavi
- Hongyi Wang
- Lifeng Lai
- Linqi Song
- Oluwasanmi Koyejo
- Luis Muñoz-González
- Jy-yong Sohn
Papers from Lingjiao Chen's perspective
Ordered by how strongly the researchers Lingjiao Chen trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- The Effect of Network Width on the Performance of Large-batch Training (2018) — 24 citations
- FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance (2023) — 768 citations
- SEAL : Interactive Tool for Systematic Error Analysis and Labeling (2022) — 18 citations
- How is ChatGPT's behavior changing over time? (2023) — 676 citations
- Towards Linear Algebra over Normalized Data (2016) — 99 citations
- FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply (2020) — 50 citations
- Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra (2019) — 38 citations
- Draco: Robust Distributed Training against Adversaries (2018)
- DRACO: Robust Distributed Training via Redundant Gradients (2018) — 23 citations
- DRACO: Byzantine-resilient Distributed Training via Redundant Gradients (2018) — 274 citations