Anant Raj
Artificial Intelligence · 2014–2017
A researcher in the Science corpus: 5 papers; trusts 212 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 73 citations into it; #16,617 of 35,345 by trust here, #11,072 by citations
- Computer Vision and Pattern Recognition — 50 citations into it; #13,809 of 21,575 by trust here, #14,478 by citations
- Optimization & Training — 38 citations into it; #1,974 of 2,601 by trust here, #1,468 by citations
- Computational Mechanics — 30 citations into it; #6,715 of 10,001 by trust here, #2,644 by citations
- Representation & Self-Supervised Learning — 22 citations into it
- Statistics and Probability — 22 citations into it
- Statistical Learning Theory — 21 citations into it; #845 of 1,038 by trust here, #539 by citations
- Numerical Analysis — 9 citations into it
Whom Anant Raj trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Christopher K. I. Williams
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Diederik P. Kingma
- Kaiming He
- Antonio Torralba
- Jerome H. Friedman
- Alex Smola
Who trusts Anant Raj overall
- Gabriella Csurka
- Abhishek Kumar
- Sara Beery
- Tao Wang
- Yuhua Chen
- Risi Kondor
- Xinge Zhu
- Qi Cai
- Bo Dai
- Tri Dao
Papers from Anant Raj's perspective
Ordered by how strongly the researchers Anant Raj 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Scalable Kernel Methods via Doubly Stochastic Gradients (2014) — 238 citations
- Subspace Alignment Based Domain Adaptation for RCNN Detector (2015) — 74 citations
- Approximate Steepest Coordinate Descent (2017) — 17 citations
- Safe Adaptive Importance Sampling (2017) — 60 citations
- Local Group Invariant Representations via Orbit Embeddings (2016) — 40 citations