Raman Arora
Artificial Intelligence · with Bilmes · 2013–2018
A researcher in the Science corpus: 7 papers; trusts 229 researchers and is trusted by 67 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 96 citations into it; #26,795 of 35,345 by trust here, #16,771 by citations
- Computer Vision and Pattern Recognition — 58 citations into it; #7,949 of 21,575 by trust here, #7,584 by citations
- Representation & Self-Supervised Learning — 29 citations into it; #513 of 3,145 by trust here, #704 by citations
- Computational Mechanics — 24 citations into it
- Optimization & Training — 23 citations into it
- Signal Processing — 19 citations into it
- Model Architectures — 16 citations into it
- Natural Language Processing — 15 citations into it
Whom Raman Arora trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Alex Krizhevsky
- Ruslan Salakhutdinov
- Ilya Sutskever
- Diederik P. Kingma
- Francis R. Bach
- Kaiming He
- Michael I. Jordan
Who trusts Raman Arora overall
- Weiran Wang
- Peng Hu
- Francesco Croce
- Changqing Zhang
- Imran A. Sheikh
- Matthias Hein
- David Lopez-Paz
- Liangli Zhen
- Aviv Eisenschtat
- Yijie Lin
Papers from Raman Arora's perspective
Ordered by how strongly the researchers Raman Arora 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
- Gradient-based learning applied to document recognition
- 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
- On Deep Multi-View Representation Learning (2015) — 1,043 citations
- Visual Robot Task Planning (2018) — 58 citations
- Understanding Deep Neural Networks with Rectified Linear Units (2016) — 740 citations
- Learning from Multiview Correlations in Open-domain Videos (2018) — 11 citations
- The Physical Systems Behind Optimization Algorithms (2016) — 19 citations
- Accelerated Mini-batch Randomized Block Coordinate Descent Method (2014) — 74 citations
- Deep Canonical Correlation Analysis (2013) — 2,085 citations