Vaibhav Agrawal

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 51 citations into it

Whom Vaibhav Agrawal trusts overall

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

1. Diederik P. Kingma
2. Aapo Hyvärinen
3. Geoffrey E. Hinton
4. Yann LeCun

Papers from Vaibhav Agrawal's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Dropout: a simple way to prevent neural networks from overfitting
6. Layer Normalization
7. Gradient-based learning applied to document recognition
8. Asynchronous Methods for Deep Reinforcement Learning
9. Stochastic Backpropagation and Approximate Inference in Deep Generative Models
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• On the Transfer of Disentangled Representations in Realistic Settings (2020) — 89 citations
• TriFinger: An Open-Source Robot for Learning Dexterity (2020) — 82 citations