Jason Ramapuram

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 5 papers; trusts 576 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 180 citations into it; #5,605 of 21,575 by trust here, #5,565 by citations
• Artificial Intelligence — 174 citations into it; #7,714 of 35,345 by trust here, #7,728 by citations
• Representation & Self-Supervised Learning — 85 citations into it
• Generative Models — 35 citations into it; #637 of 1,956 by trust here, #1,221 by citations
• Model Architectures — 34 citations into it
• Multimodal Learning — 32 citations into it
• Signal Processing — 31 citations into it
• Optimization & Training — 28 citations into it

Whom Jason Ramapuram trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Yann LeCun
7. L. Eon Bottou
8. Radford M. Neal
9. Ilya Sutskever
10. Max Welling

Who trusts Jason Ramapuram overall

1. Fei Ye
2. Adrian G. Bors
3. Rahaf Aljundi
4. Matthias De Lange
5. Blake Camp
6. Jaya Krishna Mandivarapu
7. Rui Gao
8. Mustafa Burak Gurbuz
9. Timothée Lesort
10. Qingfeng Lan

Papers from Jason Ramapuram's perspective

Ordered by how strongly the researchers Jason Ramapuram 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Self-Supervised MultiModal Versatile Networks (2020) — 416 citations
• Lifelong Generative Modeling (2017) — 134 citations
• Continual Classification Learning Using Generative Models (2018) — 57 citations
• Stabilizing Transformer Training by Preventing Attention Entropy Collapse (2023) — 184 citations
• Do Self-Supervised and Supervised Methods Learn Similar Visual Representations? (2021) — 31 citations