Shihao Ji
Computer Vision and Pattern Recognition · 2008–2021
A researcher in the Science corpus: 5 papers; trusts 211 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 63 citations into it; #11,987 of 21,575 by trust here, #10,905 by citations
- Artificial Intelligence — 61 citations into it; #19,090 of 35,345 by trust here, #20,377 by citations
- Natural Language Processing — 29 citations into it
- Representation & Self-Supervised Learning — 26 citations into it
- Generative Models — 23 citations into it
- Signal Processing — 20 citations into it; #1,264 of 7,572 by trust here, #2,525 by citations
- Optimization & Training — 19 citations into it
- Computational Mechanics — 18 citations into it; #1,425 of 10,001 by trust here, #2,156 by citations
Whom Shihao Ji trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David L. Donoho
- Joel A. Tropp
- Tomas Mikolov
- Michael E. Tipping
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- David John Cameron MacKay
- Robert Tibshirani
- Stéphane Mallat
Who trusts Shihao Ji overall
- Giacomo Oliveri
- Andrea Massa
- Zai Yang
- Hamid Palangi
- Akiko Eriguchi
- Michalis K. Titsias
- Jiameng Gao
- Kazuma Hashimoto
- Lihan He
- Lawrence Carin
Papers from Shihao Ji's perspective
Ordered by how strongly the researchers Shihao Ji 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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)
- 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
- Improving Text-to-Image Synthesis Using Contrastive Learning (2021) — 93 citations
- BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies (2015) — 77 citations
- Parallelizing Word2Vec in Shared and Distributed Memory (2016) — 72 citations
- Multitask Compressive Sensing (2009) — 500 citations
- Bayesian Compressive Sensing (2008) — 2,366 citations