Jan Balaguer

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 3 papers; trusts 14 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it
• Natural Language Processing — 13 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Model Architectures — 10 citations into it
• Reinforcement Learning — 7 citations into it
• Optimization & Training — 5 citations into it

Whom Jan Balaguer trusts overall

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

1. Alex Graves
2. Ronald J. Williams
3. Diederik P. Kingma
4. Kaiming He
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Jason Aaron Edward Weston
8. Jimmy Ba
9. Jia Deng
10. Alex Krizhevsky

Who trusts Jan Balaguer overall

1. Hongxu Yin
2. Thang D. Bui

Papers from Jan Balaguer's perspective

Ordered by how strongly the researchers Jan Balaguer 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. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Fine-tuning language models to find agreement among humans with diverse preferences (2022) — 336 citations
• PonderNet: Learning to Ponder (2021) — 148 citations
• Comparing continual task learning in minds and machines (2018) — 156 citations