Brian M. Belgodere

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it
• Materials Chemistry — 50 citations into it
• Computational Theory and Mathematics — 49 citations into it
• Molecular Biology — 42 citations into it
• Graph Learning — 27 citations into it

Whom Brian M. Belgodere trusts overall

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

1. Geoffrey E. Hinton
2. Jacob Devlin
3. Esben Jannik Bjerrum
4. Diederik P. Kingma
5. Yoshua Bengio
6. Alex Krizhevsky
7. Ashish Vaswani
8. Ilya Sutskever
9. Oriol Vinyals
10. Kaiming He

Papers from Brian M. Belgodere's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Do Large Scale Molecular Language Representations Capture Important Structural Information? (2021) — 13 citations
• Large-scale chemical language representations capture molecular structure and properties (2021) — 591 citations
• P2L: Predicting Transfer Learning for Images and Semantic Relations (2019)