Sebastian Raschka

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 363 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #15,115 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 97 citations into it
• Safety, Robustness & Interpretability — 46 citations into it
• Optimization & Training — 25 citations into it
• Signal Processing — 23 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Model Architectures — 20 citations into it
• Information Systems — 16 citations into it

Whom Sebastian Raschka trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Thomas G. Dietterich
8. Tianqi Chen
9. Ilya Sutskever
10. Leo Breiman

Who trusts Sebastian Raschka overall

1. Shaker El-Sappagh
2. José Maria Alonso
3. Laurence Hawker
4. Vahid Mirjalili
5. K. Ruwani
6. S. M. Riazul Islam
7. Joshua Patterson
8. Murat Kuzlu
9. Momin M. Malik
10. M. Rashid

Papers from Sebastian Raschka's perspective

Ordered by how strongly the researchers Sebastian Raschka 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images (2017) — 117 citations
• Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning (2018) — 1,070 citations
• Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (2020) — 705 citations
• Rank consistent ordinal regression for neural networks with application to age estimation (2019) — 370 citations
• FlowSAN: Privacy-Enhancing Semi-Adversarial Networks to Confound Arbitrary Face-Based Gender Classifiers (2019) — 50 citations
• MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack (2018) — 611 citations
• Python machine learning : machine learning and deep learning with Python, scikit-learn, and TensorFlow (2017) — 552 citations