Ankit Agrawal

Materials Chemistry · with Choudhary · 2014–2019

A researcher in the Science corpus: 8 papers; trusts 18 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 84 citations into it; #5,591 of 11,513 by trust here, #3,689 by citations
• Artificial Intelligence — 39 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Inorganic Chemistry — 26 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 11 citations into it

Whom Ankit Agrawal trusts overall

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

1. Yoshua Bengio
2. Leo Breiman
3. Trevor J. Hastie
4. Geoffrey E. Hinton
5. Felix A. Faber
6. Yann LeCun
7. Alex Krizhevsky
8. Ilya Sutskever
9. James E. Saal
10. Tin Kam Ho

Who trusts Ankit Agrawal overall

1. Seeram Ramakrishna
2. Dane Morgan
3. E. Giessen
4. Logan T. Ward
5. Zheng Xiong
6. Ryan Jacobs
7. Kamal Choudhary
8. Navid Zobeiry
9. Wei Li
10. Valentin G. Stanev

Papers from Ankit Agrawal's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. GENERATIVE ADVERSARIAL NETS
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Rectified Linear Units Improve Restricted Boltzmann Machines

Their papers

• Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science (2016) — 1,189 citations
• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials (2016) — 1,611 citations
• Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations (2017) — 297 citations
• Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets (2018) — 343 citations
• ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition (2018) — 460 citations
• Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches (2019) — 170 citations
• Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection (2017) — 900 citations
• Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014)