mlsanityData to 14 Oct 2026

Lecture Notes: Neural Network Architectures

arXiv 2304.05133 ↗First posted 11 Apr 2023cs.LG math.OCcited by 2

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Abstract (arXiv)

These lecture notes provide an overview of Neural Network architectures from a mathematical point of view. Especially, Machine Learning with Neural Networks is seen as an optimization problem. Covered are an introduction to Neural Networks and the following architectures: Feedforward Neural Network, Convolutional Neural Network, ResNet, and Recurrent Neural Network.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 11 Apr 2023.

Builds on

12 of 28 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  5. 2018Neural Ordinary Differential Equationscited by 3,779
  6. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  7. 2021Convex optimizationcited by 2,515
  8. 2016Identity Mappings in Deep Residual Networkscited by 1,965
  9. 2017Stable Architectures for Deep Neural Networkscited by 341
  10. 2020Fractional Deep Neural Network via Constrained Optimizationcited by 13
  11. 2021Deep Neural Nets with Fixed Bias Configurationcited by 4
  12. 2022An Optimal Time Variable Learning Framework for Deep Neural Networkscited by 3

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023How regularization affects the geometry of loss functionscited by 1
  2. 2023Time Regularization in Optimal Time Variable Learningcited by 0