Max Bartolo
Artificial Intelligence · 2018–2022
A researcher in the Science corpus: 7 papers; trusts 282 researchers and is trusted by 24 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 273 citations into it; #7,969 of 35,345 by trust here, #3,523 by citations
- Natural Language Processing — 235 citations into it; #2,445 of 5,688 by trust here, #1,466 by citations
- Computer Vision and Pattern Recognition — 129 citations into it; #12,806 of 21,575 by trust here, #8,122 by citations
- Evaluation & Benchmarks — 114 citations into it; #1,873 of 3,147 by trust here, #1,217 by citations
- Multimodal Learning — 32 citations into it
- Safety, Robustness & Interpretability — 30 citations into it
- Large Language Models — 29 citations into it
- Model Architectures — 17 citations into it
Whom Max Bartolo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Kaiming He
- Richard Socher
- Yann LeCun
- Alex Graves
- Oriol Vinyals
Who trusts Max Bartolo overall
- Alisa Liu
- Douwe Kiela
- Tristan Thrush
- Robin Jia
- James Thorne
- Eric Wallace
- Swaroop Mishra
- Sewon Min
- Alon Talmor
- Ethan Perez
Papers from Max Bartolo's perspective
Ordered by how strongly the researchers Max Bartolo trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (2021) — 1,596 citations
- Dynabench: Rethinking Benchmarking in NLP (2021) — 580 citations
- Interpretation of Natural Language Rules in Conversational Machine Reading (2018) — 174 citations
- Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality (2022) — 639 citations
- Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension (2020) — 200 citations
- Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation (2021) — 114 citations
- Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants (2021) — 39 citations