Yingbo Zhou
Artificial Intelligence · 2011–2023
A researcher in the Science corpus: 15 papers; trusts 372 researchers and is trusted by 56 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 313 citations into it; #5,766 of 35,345 by trust here, #3,440 by citations
- Computer Vision and Pattern Recognition — 149 citations into it; #5,155 of 21,575 by trust here, #2,648 by citations
- Natural Language Processing — 132 citations into it
- Model Architectures — 58 citations into it
- Large Language Models — 57 citations into it
- Representation & Self-Supervised Learning — 44 citations into it; #1,387 of 3,145 by trust here, #672 by citations
- Signal Processing — 43 citations into it; #3,925 of 7,572 by trust here, #4,201 by citations
- Optimization & Training — 39 citations into it; #1,235 of 2,601 by trust here, #1,419 by citations
Whom Yingbo Zhou trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Alex Graves
- Diederik P. Kingma
- Sergey Ioffe
- Ronald J. Williams
- Ilya Sutskever
Who trusts Yingbo Zhou overall
- Jorge Armando Mendez Mendez
- Luowei Zhou
- Sitao Xiang
- Shuang Ma
- Lei Huang
- Vitaliy Liptchinsky
- Chen Sun
- Arthur Douillard
- Jinyu Li
- Quang Pham
Papers from Yingbo Zhou's perspective
Ordered by how strongly the researchers Yingbo Zhou 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- End-to-End Dense Video Captioning with Masked Transformer (2018) — 599 citations
- Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (2019) — 532 citations
- Challenges in representation learning: A report on three machine learning contests (2013) — 2,020 citations
- Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks (2016) — 131 citations
- Online Structured Meta-learning (2020) — 29 citations
- RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering (2021) — 212 citations
- Improving End-to-End Speech Recognition with Policy Learning (2017) — 40 citations
- CodeGen2: Lessons for Training LLMs on Programming and Natural Languages (2023) — 243 citations
- L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models (2023) — 44 citations
- CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis (2022) — 1,548 citations
- A Conversational Paradigm for Program Synthesis (2022) — 194 citations
- An Investigation of Phone-Based Subword Units for End-to-End Speech Recognition (2020) — 42 citations
- Differentially Private Deep Learning with Smooth Sensitivity (2020) — 13 citations
- Human Identification Using Finger Images (2012) — 613 citations
- Human Identification Using Palm-Vein Images (2011) — 304 citations