Sophia Althammer

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 3 papers; trusts 43 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #14,065 of 35,345 by trust here, #15,542 by citations
• Information Retrieval & Search — 44 citations into it; #442 of 1,022 by trust here, #505 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Information Systems — 18 citations into it
• Natural Language Processing — 18 citations into it
• Large Language Models — 11 citations into it

Whom Sophia Althammer trusts overall

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

1. Sebastian Hofstätter
2. Zhuyun Dai
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Nick Craswell
6. Jacob Devlin
7. Yoshua Bengio
8. Bhaskar Mitra
9. Luyu Gao
10. Jimmy J. Lin

Who trusts Sophia Althammer overall

1. Sebastian Hofstätter
2. Sheng-Chieh Lin
3. Jimmy J. Lin
4. Wayne Xin Zhao
5. Jheng-Hong Yang
6. Thibault Formal
7. Xueguang Ma

Papers from Sophia Althammer's perspective

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

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

Their papers

• Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation (2020) — 290 citations
• Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models (2024) — 271 citations
• Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction (2022) — 54 citations