Sergey Ioffe
Computer Vision and Pattern Recognition · 2001–2017
A researcher in the Science corpus: 9 papers; trusts 739 researchers and is trusted by 15,681 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 108 citations into it; #173 of 21,575 by trust here, #173 by citations
- Artificial Intelligence — 67 citations into it; #17 of 35,345 by trust here, #42 by citations
- Model Architectures — 29 citations into it; #8 of 2,278 by trust here, #20 by citations
- Optimization & Training — 26 citations into it; #7 of 2,601 by trust here, #13 by citations
- Representation & Self-Supervised Learning — 21 citations into it; #2,217 of 3,145 by trust here, #1,811 by citations
- Evaluation & Benchmarks — 13 citations into it
- Media Technology — 12 citations into it
- Signal Processing — 11 citations into it
Whom Sergey Ioffe trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- Yann LeCun
- Christian Szegedy
- Yoshua Bengio
- Alex Krizhevsky
- Jia Deng
- Jimmy Ba
- Ilya Sutskever
Who trusts Sergey Ioffe overall
- Yoshua Bengio
- Kaiming He
- Quoc V. Le
- Trevor Darrell
- Andrew Zisserman
- Yi Yang
- Sergey Levine
- Andrea Vedaldi
- Abhinav Kumar Gupta
- Luc Van Gool
Papers from Sergey Ioffe's perspective
Ordered by how strongly the researchers Sergey Ioffe trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Rethinking the Inception Architecture for Computer Vision (2015) — 31,459 citations
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (2015) — 47,277 citations
- Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning (2016) — 15,641 citations
- Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models (2017) — 595 citations
- Deep Convolutional Ranking for Multilabel Image Annotation (2013) — 458 citations
- No Fuss Distance Metric Learning Using Proxies (2017) — 713 citations
- Mixtures of trees for object recognition (2001) — 38 citations
- Probabilistic Linear Discriminant Analysis (2006) — 576 citations
- Improved Consistent Sampling, Weighted Minhash and L1 Sketching (2010) — 235 citations