Brian L. DeCost

Materials Chemistry · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 187 citations into it; #7,178 of 11,513 by trust here, #8,433 by citations
• Artificial Intelligence — 66 citations into it
• Catalysis — 18 citations into it
• Statistical Learning Theory — 7 citations into it

Whom Brian L. DeCost trusts overall

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

1. Eric R. Ziegel
2. Tian Xie
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. J. Behler
6. Yann LeCun
7. Logan T. Ward
8. Alex Krizhevsky
9. Diederik P. Kingma
10. Keyulu Xu

Who trusts Brian L. DeCost overall

1. Chi Chen
2. Kamal Choudhary

Papers from Brian L. DeCost's perspective

Ordered by how strongly the researchers Brian L. DeCost 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Autonomous experimentation systems for materials development: A community perspective (2021) — 311 citations
• Atomistic Line Graph Neural Network for improved materials property predictions (2021) — 757 citations
• Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape (2018) — 144 citations
• Recent advances and applications of deep learning methods in materials science (2021) — 995 citations
• JARVIS: An Integrated Infrastructure for Data-driven Materials Design (2020) — 371 citations
• Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks (2018) — 301 citations