Patrick Schramowski

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 7 papers; trusts 402 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 220 citations into it; #26,035 of 35,345 by trust here, #3,624 by citations
• Computer Vision and Pattern Recognition — 149 citations into it; #18,661 of 21,575 by trust here, #3,124 by citations
• Safety, Robustness & Interpretability — 78 citations into it; #2,102 of 2,251 by trust here, #1,119 by citations
• Natural Language Processing — 55 citations into it
• Multimodal Learning — 53 citations into it
• Evaluation & Benchmarks — 45 citations into it
• Large Language Models — 38 citations into it
• Representation & Self-Supervised Learning — 37 citations into it

Whom Patrick Schramowski trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Kaiming He
4. Ilya Sutskever
5. Alex Krizhevsky
6. Yoshua Bengio
7. Alec Radford
8. Karen Simonyan
9. Yann LeCun
10. Jia Deng

Who trusts Patrick Schramowski overall

1. Rohit Gandikota
2. Andrew Kyle Lampinen
3. Shilin Lu
4. Sami Sarsa
5. Wolfgang Stammer
6. Chongyu Fan
7. Gabriel Erion-Barner
8. Lin Guan
9. Lixiang Yan
10. Kristian Kersting

Papers from Patrick Schramowski's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• LAION-5B: An open large-scale dataset for training next generation image-text models (2022) — 5,389 citations
• Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices (2019) — 77 citations
• Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020) — 250 citations
• Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models (2022) — 584 citations
• Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content? (2022) — 191 citations
• Large pre-trained language models contain human-like biases of what is right and wrong to do (2021) — 398 citations
• Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations (2020) — 139 citations