Gerard de Melo
Artificial Intelligence · 2011–2024
A researcher in the Science corpus: 17 papers; trusts 579 researchers and is trusted by 31 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 319 citations into it; #10,486 of 35,345 by trust here, #6,468 by citations
- Computer Vision and Pattern Recognition — 153 citations into it; #8,014 of 21,575 by trust here, #8,021 by citations
- Natural Language Processing — 123 citations into it
- Information Systems — 79 citations into it; #951 of 8,771 by trust here, #1,284 by citations
- Representation & Self-Supervised Learning — 54 citations into it
- Management Science and Operations Research — 36 citations into it
- Evaluation & Benchmarks — 33 citations into it
- Information Retrieval & Search — 30 citations into it; #396 of 1,022 by trust here, #395 by citations
Whom Gerard de Melo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Kaiming He
- Ronald J. Williams
- Ilya Sutskever
- Tomas Mikolov
- Yann LeCun
- Karen Simonyan
Who trusts Gerard de Melo overall
- Sebastian Hofstätter
- Sean MacAvaney
- Yuhao Zhang
- Rodrigo Nogueira
- Gabor Angeli
- Christopher D. Manning
- Ryan T. McDonald
- Andrew Yates
- Jyun-Yu Jiang
- Eric Crawford
Papers from Gerard de Melo's perspective
Ordered by how strongly the researchers Gerard de Melo trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- SCALOR: Generative World Models with Scalable Object Representations (2019) — 154 citations
- Relation Classification via Multi-Level Attention CNNs (2016) — 513 citations
- Deriving a Web-Scale Common Sense Fact Database (2011) — 41 citations
- NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation (2021) — 104 citations
- Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain? (2021) — 159 citations
- PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain? (2023) — 310 citations
- LINDA: distributed web-of-data-scale entity matching (2012) — 99 citations
- YAGO2: exploring and querying world knowledge in time, space, context, and many languages (2011) — 376 citations
- Knowledge Graphs (2020) — 2,597 citations
- NextLevelBERT: Masked Language Modeling with Higher-Level Representations for Long Documents (2024)
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
- Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
- Character Matters: Video Story Understanding with Character-Aware Relations (2020) — 11 citations
- Reinforcement Knowledge Graph Reasoning for Explainable Recommendation (2019) — 562 citations
- Scalable Object-Oriented Sequential Generative Models (2019) — 16 citations
- Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval (2017) — 125 citations
- PACRR: A Position-Aware Neural IR Model for Relevance Matching (2017) — 165 citations