Sai Rajeswar
Computer Vision and Pattern Recognition · 2017–2020
A researcher in the Science corpus: 8 papers; trusts 608 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 201 citations into it; #21,122 of 21,575 by trust here, #4,690 by citations
- Artificial Intelligence — 154 citations into it; #34,642 of 35,345 by trust here, #6,569 by citations
- Generative Models — 126 citations into it
- Natural Language Processing — 55 citations into it
- Optimization & Training — 34 citations into it
- Representation & Self-Supervised Learning — 31 citations into it; #2,180 of 3,145 by trust here, #1,543 by citations
- Statistical and Nonlinear Physics — 30 citations into it
- Evaluation & Benchmarks — 27 citations into it
Whom Sai Rajeswar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Ilya Sutskever
- Ronald J. Williams
- Ruslan Salakhutdinov
- Alex Graves
Who trusts Sai Rajeswar overall
- Jiaming Song
- Yoshua Bengio
- David A. McAllester
- Stefano Ermon
- Yonglong Tian
- Chen Sun
- Sergey Levine
- Md. Akmal Haidar
- Xingchao Peng
- Yilun Du
Papers from Sai Rajeswar's perspective
Ordered by how strongly the researchers Sai Rajeswar 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
- 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)
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Mutual Information Neural Estimation (2018) — 1,674 citations
- Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data (2018) — 465 citations
- Adversarial Generation of Natural Language (2017) — 210 citations
- Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images Using a View-Based Representation (2020) — 11 citations
- Towards Text Generation with Adversarially Learned Neural Outlines (2018) — 52 citations
- Hierarchical Adversarially Learned Inference (2018) — 29 citations
- MINE: Mutual Information Neural Estimation (2018) — 747 citations
- The Octopus Approach to the Alexa Competition : A Deep Ensemble-based Socialbot (2017)