Jasmijn Bastings

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 8 papers; trusts 176 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 248 citations into it; #7,863 of 35,345 by trust here, #5,495 by citations
• Computer Vision and Pattern Recognition — 131 citations into it
• Natural Language Processing — 130 citations into it
• Evaluation & Benchmarks — 63 citations into it; #2,901 of 3,147 by trust here, #2,799 by citations
• Safety, Robustness & Interpretability — 43 citations into it; #629 of 2,251 by trust here, #1,312 by citations
• Model Architectures — 37 citations into it; #742 of 2,278 by trust here, #999 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Efficiency & Compression — 15 citations into it

Whom Jasmijn Bastings trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Ronald J. Williams
6. Kaiming He
7. Alex Krizhevsky
8. Yann LeCun
9. Richard Socher
10. Kyunghyun Cho

Who trusts Jasmijn Bastings overall

1. Bodhisattwa Prasad Majumder
2. Nicola De Cao
3. Michael Sejr Schlichtkrull
4. Ana Marasovic
5. Sarthak Jain
6. Diane Bouchacourt
7. Qian Li
8. Nazneen Rajani
9. Alexis Ross
10. Diego Marcheggiani

Papers from Jasmijn Bastings's perspective

Ordered by how strongly the researchers Jasmijn Bastings 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
• The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models (2020) — 224 citations
• Interpretable Neural Predictions with Differentiable Binary Variables (2019) — 236 citations
• Graph Convolutional Encoders for Syntax-aware Neural Machine Translation (2017) — 519 citations
• The MultiBERTs: BERT Reproductions for Robustness Analysis (2021) — 110 citations
• Dissecting Recall of Factual Associations in Auto-Regressive Language Models (2023) — 547 citations
• Scaling Up Models and Data with t5x and seqio (2022) — 224 citations
• We Need To Talk About Random Splits (2020) — 113 citations