M. Pieler

Artificial Intelligence · 2022–2023

A researcher in the Science corpus: 3 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 132 citations into it
• Natural Language Processing — 51 citations into it

Whom M. Pieler trusts overall

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

1. Alec Radford
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Diederik P. Kingma
7. Alex Krizhevsky

Papers from M. Pieler's perspective

Ordered by how strongly the researchers M. Pieler 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Language Models are Unsupervised Multitask Learners
9. Gradient-based learning applied to document recognition
10. Language Models are Few-Shot Learners (Neural Information Processing Systems)

Their papers

• GPT-NeoX-20B: An Open-Source Autoregressive Language Model (2022) — 1,029 citations
• Inverse Scaling: When Bigger Isn't Better (2023) — 235 citations
• Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning (2022) — 15 citations