Sparsh Mittal

Electrical and Electronic Engineering · 2015–2018

A researcher in the Science corpus: 3 papers; trusts 51 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 24 citations into it; #3,292 of 16,539 by trust here, #13,165 by citations
• Hardware and Architecture — 22 citations into it; #648 of 1,551 by trust here, #1,091 by citations
• Computer Vision and Pattern Recognition — 19 citations into it
• Artificial Intelligence — 13 citations into it
• Computer Networks and Communications — 10 citations into it

Whom Sparsh Mittal trusts overall

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

1. Adrian Sampson
2. Hadi Esmaeilzadeh
3. Woongki Baek
4. Swagath Venkataramani
5. Yongming Shen
6. Yufei Ma
7. Stelios Sidiroglou
8. Manoj Alwani
9. Michael Ferdman
10. Matthieu Courbariaux

Who trusts Sparsh Mittal overall

1. Marcel Stimberg
2. Md Maruf Hossain Shuvo
3. Romain Brette
4. Syed Kamrul Islam
5. Dan F. M. Goodman
6. Jianlin Cheng
7. Dimitris Chatzopoulos
8. Bashir I. Morshed

Papers from Sparsh Mittal's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Going deeper with convolutions
5. Gradient-based learning applied to document recognition
6. ImageNet Large Scale Visual Recognition Challenge
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet: A large-scale hierarchical image database

Their papers

• A Survey of Techniques for Approximate Computing (2016) — 1,032 citations
• A survey of FPGA-based accelerators for convolutional neural networks (2018) — 363 citations
• A Survey of CPU-GPU Heterogeneous Computing Techniques (2015) — 536 citations