Satyasai Jagannath Nanda
Artificial Intelligence · 2014
A researcher in the Science corpus: 1 paper; trusts 35 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 69 citations into it; #18,010 of 35,345 by trust here, #28,481 by citations
- Computational Theory and Mathematics — 31 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it; #11,410 of 21,575 by trust here, #18,266 by citations
- Biomedical Engineering — 8 citations into it
- Molecular Biology — 8 citations into it
- Statistics and Probability — 8 citations into it
- Information Systems — 7 citations into it
- Signal Processing — 7 citations into it
Whom Satyasai Jagannath Nanda trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sanghamitra Bandyopadhyay
- Ujjwal Maulik
- Xin-She Yang
- Kalyanmoy Deb
- Anil K. Jain
- Shu-Chuan Chu
- John H. Holland
- Bahriye Basturk
- Kevin M. Passino
- Abdolreza Hatamlou
Who trusts Satyasai Jagannath Nanda overall
- Laith Mohammad Abualigah
- Ye Ren
- Ahamad Tajudin Abdul Khader
- Le Zhang
- Ponnuthurai Nagaratnam Suganthan
- Essam Said Hanandeh
Papers from Satyasai Jagannath Nanda's perspective
Ordered by how strongly the researchers Satyasai Jagannath Nanda trusts endorse each paper. Same corpus, a different order for every researcher.
- Genetic Algorithms in Search Optimization and Machine Learning
- Genetic Algorithms in Search
- Optimization by Simulated Annealing
- Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Data clustering: a review
- A Cluster Separation Measure
- Multi-objective optimization using evolutionary algorithms
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A survey on nature inspired metaheuristic algorithms for partitional clustering (2014) — 528 citations