Maximilian Sesselmann

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Maximilian Sesselmann trusts overall

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

  1. Christian Szegedy

Papers from Maximilian Sesselmann's perspective

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

  1. ImageNet classification with deep convolutional neural networks
  2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
  4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  5. Dropout: a simple way to prevent neural networks from overfitting
  6. Rethinking the Inception Architecture for Computer Vision
  7. Caffe: Convolutional Architecture for Fast Feature Embedding
  8. Identity Mappings in Deep Residual Networks
  9. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
  10. Gradient-based learning applied to document recognition

Their papers