Daniel Belkin

Nature Communications · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 0 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Whom Daniel Belkin trusts overall

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

1. Yann LeCun
2. David E. Rumelhart
3. Geoffrey E. Hinton
4. J. Joshua Yang

Papers from Daniel Belkin's perspective

Ordered by how strongly the researchers Daniel Belkin 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. Gradient-based learning applied to document recognition
3. Long Short-Term Memory
4. ImageNet classification with deep convolutional neural networks
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet: A large-scale hierarchical image database

Their papers

• A novel true random number generator based on a stochastic diffusive memristor (2017) — 390 citations
• Reinforcement learning with analogue memristor arrays (2019) — 334 citations
• Efficient and self-adaptive in-situ learning in multilayer memristor neural networks (2018) — 828 citations
• Long short-term memory networks in memristor crossbar arrays (2018) — 368 citations