Yogesh Kumar Balaji

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 8 papers; trusts 924 researchers and is trusted by 65 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 246 citations into it; #5,062 of 21,575 by trust here, #2,836 by citations
• Artificial Intelligence — 188 citations into it; #7,984 of 35,345 by trust here, #5,600 by citations
• Generative Models — 89 citations into it; #784 of 1,956 by trust here, #486 by citations
• Representation & Self-Supervised Learning — 77 citations into it; #704 of 3,145 by trust here, #839 by citations
• Optimization & Training — 27 citations into it; #867 of 2,601 by trust here, #1,009 by citations
• Meta & Continual Learning — 25 citations into it
• Radiology, Nuclear Medicine and Imaging — 18 citations into it
• Statistical and Nonlinear Physics — 18 citations into it

Whom Yogesh Kumar Balaji trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Ilya Sutskever
7. Yann LeCun
8. Karen Simonyan
9. Ross B. Girshick
10. Alex Graves

Who trusts Yogesh Kumar Balaji overall

1. Kaiyang Zhou
2. Mohammad Rostami
3. Prithvijit Chattopadhyay
4. Yongxin Yang
5. Qi-Yan Dou
6. Yusuke Iwasawa
7. Toshihiko Matsuura
8. Garrett Wilson
9. Axel Sauer
10. Jindong Wang

Papers from Yogesh Kumar Balaji's perspective

Ordered by how strongly the researchers Yogesh Kumar Balaji trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers (2022) — 1,087 citations
• Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation (2017) — 527 citations
• Generate to Adapt: Aligning Domains Using Generative Adversarial Networks (2017) — 680 citations
• Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models (2023) — 326 citations
• Learning to Balance Specificity and Invariance for In and Out of Domain Generalization (2020) — 239 citations
• The Effectiveness of Memory Replay in Large Scale Continual Learning (2020) — 41 citations
• Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation (2020) — 132 citations
• MetaReg: Towards Domain Generalization using Meta-Regularization (2018) — 786 citations