mlsanityData to 14 Oct 2026

Neural Computing

arXiv 2107.02744 ↗First posted 6 Jul 2021cs.NE cs.AI cs.LGcited by 2

Not on the map. Why?

Abstract (arXiv)

This chapter aims to provide next-level understanding of the problems of the world and the solutions available to those problems, which lie very well within the domain of neural computing, and at the same time are intelligent in their approach, to invoke a sense of innovation among the educationalists, researchers, academic professionals, students and people concerned, by highlighting the work done by major researchers and innovators in this field and thus, encouraging the readers to develop newer and more advanced techniques for the same. By means of this chapter, the societal problems are discussed and various solutions are also given by means of the theories presented and researches done so far. Different types of neural networks discovered so far and applications of some of those neural networks are focused on, apart from their theoretical understanding, the working and core concepts involved in the applications.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 6 Jul 2021.

Builds on

11 of 40 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2017Mask R-CNNcited by 7,277
  4. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  5. 2014Going Deeper with Convolutionscited by 4,241
  6. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  7. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  8. 2016YOLO9000: Better, Faster, Strongercited by 1,431
  9. 2017Dynamic Routing Between Capsulescited by 689
  10. 2014Neural Turing Machinescited by 644
  11. 2015Learning Deconvolution Network for Semantic Segmentationcited by 365

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.