Gino Brunner

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 148 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 121 citations into it; #26,256 of 35,345 by trust here, #16,771 by citations
• Computer Vision and Pattern Recognition — 70 citations into it
• Natural Language Processing — 55 citations into it; #4,845 of 5,688 by trust here, #4,879 by citations
• Large Language Models — 34 citations into it
• Model Architectures — 32 citations into it; #2,097 of 2,278 by trust here, #1,490 by citations
• Generative Models — 20 citations into it
• Representation & Self-Supervised Learning — 18 citations into it; #2,759 of 3,145 by trust here, #3,009 by citations
• Safety, Robustness & Interpretability — 14 citations into it

Whom Gino Brunner trusts overall

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

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

Who trusts Gino Brunner overall

1. Yonatan Belinkov
2. Derya Soydaner
3. Sarah Wiegreffe
4. Damian Pascual
5. Sarthak Jain
6. Prafulla Dhariwal
7. Nazneen Rajani
8. Yu Zhang
9. Noam Wies
10. Yoav Levine

Papers from Gino Brunner's perspective

Ordered by how strongly the researchers Gino Brunner 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer (2018) — 150 citations
• On Identifiability in Transformers (2019) — 212 citations
• Attentive Multi-Task Deep Reinforcement Learning (2019) — 19 citations
• Of Non-Linearity and Commutativity in BERT (2021) — 20 citations
• Telling BERT’s Full Story: from Local Attention to Global Aggregation (2020) — 22 citations
• Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations (2018) — 12 citations