Divyam Madaan
Artificial Intelligence · 2021–2022
A researcher in the Science corpus: 4 papers; trusts 329 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 132 citations into it
- Computer Vision and Pattern Recognition — 86 citations into it
- Representation & Self-Supervised Learning — 56 citations into it
- Meta & Continual Learning — 39 citations into it
- Optimization & Training — 22 citations into it
- Evaluation & Benchmarks — 12 citations into it
- Model Architectures — 10 citations into it
- Radiology, Nuclear Medicine and Imaging — 10 citations into it
Whom Divyam Madaan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yann LeCun
- Yoshua Bengio
- Ross B. Girshick
- Diederik P. Kingma
- Ilya Sutskever
- Marc'Aurelio Ranzato
- Jia Deng
Who trusts Divyam Madaan overall
Papers from Divyam Madaan's perspective
Ordered by how strongly the researchers Divyam Madaan 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)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Online Coreset Selection for Rehearsal-based Continual Learning (2021) — 187 citations
- What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2022) — 48 citations
- Rethinking the Representational Continuity: Towards Unsupervised Continual Learning (2021) — 18 citations
- Representational Continuity for Unsupervised Continual Learning (2021) — 152 citations