Steven Schockaert

Artificial Intelligence · 2009–2023

A researcher in the Science corpus: 5 papers; trusts 136 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 101 citations into it; #12,515 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 47 citations into it
• Natural Language Processing — 34 citations into it; #2,553 of 5,688 by trust here, #3,754 by citations
• Representation & Self-Supervised Learning — 32 citations into it
• Large Language Models — 17 citations into it; #279 of 799 by trust here, #644 by citations
• Evaluation & Benchmarks — 9 citations into it
• Model Architectures — 9 citations into it

Whom Steven Schockaert trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Kaiming He
5. Tomas Mikolov
6. Ilya Sutskever
7. Diederik P. Kingma
8. Yann LeCun
9. Jacob Devlin
10. Richard Socher

Who trusts Steven Schockaert overall

1. Timo Schick
2. Tara Safavi
3. Vered Shwartz

Papers from Steven Schockaert's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Inducing Relational Knowledge from BERT (2019) — 184 citations
• Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning (2021) — 36 citations
• RAGAs: Automated Evaluation of Retrieval Augmented Generation (2023) — 839 citations
• Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures (2018) — 98 citations
• Frontiers in Artificial Intelligence and Applications (2009) — 556 citations