Jitesh H. Panchal
Statistics and Probability · 2013–2019
A researcher in the Science corpus: 2 papers; trusts 36 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 26 citations into it; #9,652 of 12,932 by trust here, #6,853 by citations
- Mechanics of Materials — 25 citations into it; #4,803 of 6,291 by trust here, #3,944 by citations
- Materials Chemistry — 24 citations into it; #7,832 of 11,513 by trust here, #9,794 by citations
- Computational Theory and Mathematics — 19 citations into it
- Artificial Intelligence — 17 citations into it; #26,006 of 35,345 by trust here, #24,417 by citations
- Mechanical Engineering — 14 citations into it; #5,982 of 8,300 by trust here, #5,911 by citations
- Statistical and Nonlinear Physics — 11 citations into it; #7,038 of 10,127 by trust here, #5,894 by citations
- Aerospace Engineering — 7 citations into it
Whom Jitesh H. Panchal trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David L. McDowell
- Graeme W. Milton
- S. Niezgoda
- Panos Y. Papalambros
- Marc C. Kennedy
- D. Warner
- B. Adams
- Ole Sigmund
- Surya R. Kalidindi
- Maria J. Bayarri
Who trusts Jitesh H. Panchal overall
Papers from Jitesh H. Panchal's perspective
Ordered by how strongly the researchers Jitesh H. Panchal trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions