Mateja Jamnik

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 6 papers; trusts 42 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it; #25,129 of 35,345 by trust here, #11,327 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Natural Language Processing — 30 citations into it; #4,265 of 5,688 by trust here, #2,808 by citations
• Large Language Models — 21 citations into it
• Prompting & Reasoning — 20 citations into it; #347 of 359 by trust here, #289 by citations
• Computational Theory and Mathematics — 19 citations into it
• Information Systems — 17 citations into it; #8,654 of 8,771 by trust here, #2,307 by citations
• Safety Research — 13 citations into it

Whom Mateja Jamnik trusts overall

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

1. Ilya Sutskever
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Jerome H. Friedman
7. Christian Szegedy
8. Kaiming He
9. Yann LeCun
10. Jimmy Ba

Who trusts Mateja Jamnik overall

1. Zhangir Azerbayev

Papers from Mateja Jamnik's perspective

Ordered by how strongly the researchers Mateja Jamnik 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Autoformalization with Large Language Models (2022) — 324 citations
• Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers (2022) — 144 citations
• You Shouldn't Trust Me: Learning Models Which Conceal Unfairness From Multiple Explanation Methods (2020) — 110 citations
• Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs (2022) — 338 citations
• Evaluating Language Models for Mathematics through Interactions (2023) — 114 citations
• Probabilistic Dual Network Architecture Search on Graphs (2020) — 27 citations