Nupur Kumari

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 456 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 164 citations into it
• Artificial Intelligence — 101 citations into it
• Generative Models — 89 citations into it
• Representation & Self-Supervised Learning — 39 citations into it
• Statistical and Nonlinear Physics — 16 citations into it
• Model Architectures — 11 citations into it
• Signal Processing — 10 citations into it
• Media Technology — 6 citations into it

Whom Nupur Kumari trusts overall

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

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

Who trusts Nupur Kumari overall

1. Rohit Gandikota
2. Tao Yang
3. Ligong Han
4. Shilin Lu
5. Xi Chen
6. Renrui Zhang
7. Yuwei Guo
8. Songwei Ge
9. Yuchao Gu
10. Peiran Ren

Papers from Nupur Kumari's perspective

Ordered by how strongly the researchers Nupur Kumari 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multi-Concept Customization of Text-to-Image Diffusion (2022) — 1,364 citations
• Charting the Right Manifold: Manifold Mixup for Few-shot Learning (2019) — 386 citations
• Ablating Concepts in Text-to-Image Diffusion Models (2023) — 361 citations