Leonardo Neves

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 10 papers; trusts 236 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 228 citations into it; #16,530 of 35,345 by trust here, #9,369 by citations
• Natural Language Processing — 160 citations into it; #2,950 of 5,688 by trust here, #2,497 by citations
• Computer Vision and Pattern Recognition — 76 citations into it; #17,211 of 21,575 by trust here, #13,269 by citations
• Representation & Self-Supervised Learning — 40 citations into it
• Safety, Robustness & Interpretability — 28 citations into it
• Evaluation & Benchmarks — 25 citations into it; #1,755 of 3,147 by trust here, #2,095 by citations
• Model Architectures — 25 citations into it
• Graph Learning — 18 citations into it

Whom Leonardo Neves trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Diederik P. Kingma
6. Ilya Sutskever
7. Kaiming He
8. Alex Graves
9. Sepp Hochreiter
10. Ruslan Salakhutdinov

Who trusts Leonardo Neves overall

1. Ruiqi Zhong
2. Francesco Barbieri
3. Seunghyun Yoon
4. Jianfei Yu

Papers from Leonardo Neves's perspective

Ordered by how strongly the researchers Leonardo Neves 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. 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (2020) — 1,004 citations
• On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning (2021) — 84 citations
• Learning from Explanations with Neural Execution Tree (2019) — 41 citations
• LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation (2020) — 22 citations
• NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction (2020) — 58 citations
• TimeLMs: Diachronic Language Models from Twitter (2022) — 375 citations
• Data Augmentation for Graph Neural Networks (2020) — 507 citations
• Learning to Annotate: Modularizing Data Augmentation for Text Classifiers with Natural Language Explanations (2019)
• Visual Attention Model for Name Tagging in Multimodal Social Media (2018) — 304 citations
• Audio-based multimedia event detection using deep recurrent neural networks (2016) — 74 citations