Christopher Tauchmann

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 70 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Natural Language Processing — 16 citations into it
• Evaluation & Benchmarks — 14 citations into it; #2,206 of 3,147 by trust here, #3,134 by citations
• Large Language Models — 11 citations into it
• Safety, Robustness & Interpretability — 11 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Christopher Tauchmann trusts overall

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

1. Chin-Yew Lin
2. George Giannakopoulos
3. Yvette Graham
4. Ani Nenkova
5. Geoffrey E. Hinton
6. Ilya Sutskever
7. Yoshua Bengio
8. Kaiming He
9. Alec Radford
10. Diederik P. Kingma

Who trusts Christopher Tauchmann overall

1. Sebastian Gehrmann
2. Elizabeth Clark
3. Thibault Sellam

Papers from Christopher Tauchmann's perspective

Ordered by how strongly the researchers Christopher Tauchmann 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content? (2022) — 191 citations
• Language Agnostic Automatic Summarization Evaluation (2020) — 4 citations