Daniel Berenberg

Artificial Intelligence · 2021–2023

A researcher in the Science corpus: 3 papers; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it

Whom Daniel Berenberg trusts overall

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

1. Pascal Vincent
2. Yoshua Bengio
3. Amos Bairoch
4. Geoffrey E. Hinton
5. Hugo Larochelle
6. Diederik P. Kingma
7. Rolf Apweiler
8. Samy Bengio
9. Namrata Anand
10. Alex Krizhevsky

Papers from Daniel Berenberg's perspective

Ordered by how strongly the researchers Daniel Berenberg 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. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Structure-based protein function prediction using graph convolutional networks (2021) — 926 citations
• OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization (2023) — 435 citations
• Function-guided protein design by deep manifold sampling (2021) — 35 citations