Apoorv Khandelwal

Computer Vision and Pattern Recognition · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 244 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 69 citations into it; #21,462 of 21,575 by trust here, #6,795 by citations
• Artificial Intelligence — 62 citations into it; #35,166 of 35,345 by trust here, #9,641 by citations
• Multimodal Learning — 32 citations into it; #1,209 of 1,211 by trust here, #561 by citations
• Robotics & Control — 11 citations into it

Whom Apoorv Khandelwal trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. Tsung-Yi Lin
10. Alec Radford

Who trusts Apoorv Khandelwal overall

1. S. Gadre
2. Arjun Majumdar
3. Suraj Nair
4. Mitchell Wortsman
5. Matt Deitke
6. Yunfan Jiang
7. Gabriel Ilharco
8. Wenlong Huang
9. Aravind Rajeswaran
10. Haotian Liu

Papers from Apoorv Khandelwal's perspective

Ordered by how strongly the researchers Apoorv Khandelwal 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge (2022) — 950 citations
• Simple but Effective: CLIP Embeddings for Embodied AI (2021) — 294 citations