Julian Risch

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 64 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #17,551 of 35,345 by trust here, #19,873 by citations
• Natural Language Processing — 43 citations into it; #2,640 of 5,688 by trust here, #3,826 by citations
• Safety, Robustness & Interpretability — 29 citations into it; #1,160 of 2,251 by trust here, #1,673 by citations
• Information Systems — 26 citations into it
• Communication — 15 citations into it
• AI Ethics, Fairness & Society — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it; #2,356 of 3,145 by trust here, #3,140 by citations
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Julian Risch trusts overall

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

1. Jeffrey Pennington
2. Piotr Bojanowski
3. Sepp Hochreiter
4. J. Urgen Schmidhuber
5. Karen Simonyan
6. Geoffrey E. Hinton
7. Matthew D. Zeiler
8. Richard Socher
9. Edouard Grave
10. Yoshua Bengio

Who trusts Julian Risch overall

1. Hayden S. Helm
2. Betty van Aken
3. Yangjun Zhang
4. Svetlana Kiritchenko
5. Brandon Duderstadt
6. Ammar Mohammed
7. Michael Wiegand
8. Pengjie Ren
9. Keita Kurita
10. Isar Nejadgholi

Papers from Julian Risch's perspective

Ordered by how strongly the researchers Julian Risch 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Challenges for Toxic Comment Classification: An In-Depth Error Analysis (2018) — 250 citations
• Offensive Language Detection Explained (2020) — 39 citations
• Domain-specific word embeddings for patent classification (2019)
• Aggression Identification Using Deep Learning and Data Augmentation (2018) — 71 citations