Nicola De Cao

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 16 papers; trusts 797 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 412 citations into it; #5,251 of 35,345 by trust here, #3,348 by citations
• Natural Language Processing — 249 citations into it; #1,506 of 5,688 by trust here, #1,018 by citations
• Computer Vision and Pattern Recognition — 179 citations into it; #5,817 of 21,575 by trust here, #4,372 by citations
• Generative Models — 69 citations into it; #569 of 1,956 by trust here, #587 by citations
• Information Retrieval & Search — 55 citations into it
• Evaluation & Benchmarks — 53 citations into it
• Large Language Models — 52 citations into it; #260 of 799 by trust here, #445 by citations
• Model Architectures — 49 citations into it; #1,858 of 2,278 by trust here, #1,940 by citations

Whom Nicola De Cao trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Yann LeCun
7. Ronald J. Williams
8. Danilo Jimenez Rezende
9. Jimmy Ba
10. Max Welling

Who trusts Nicola De Cao overall

1. Kevin Meng
2. William L. Hamilton
3. Michael Sejr Schlichtkrull
4. Eric Mitchell
5. Michael R. Glass
6. Sewon Min
7. Michele Bevilacqua
8. James Thorne
9. Patrick Lewis
10. Akari Asai

Papers from Nicola De Cao's perspective

Ordered by how strongly the researchers Nicola De Cao 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Editing Factual Knowledge in Language Models (2021) — 715 citations
• Question Answering by Reasoning Across Documents with Graph Convolutional Networks (2018) — 245 citations
• Autoregressive Entity Retrieval (2020) — 551 citations
• Hyperspherical Variational Auto-Encoders (2018) — 473 citations
• Explorations in Homeomorphic Variational Auto-Encoding (2018) — 131 citations
• A Memory Efficient Baseline for Open Domain Question Answering (2020) — 46 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Block Neural Autoregressive Flow (2019) — 142 citations
• Highly Parallel Autoregressive Entity Linking with Discriminative Correction (2021) — 41 citations
• Multilingual Autoregressive Entity Linking (2021) — 100 citations
• MolGAN: An implicit generative model for small molecular graphs (2018) — 1,103 citations
• Moving the Eiffel Tower to ROME: Tracing and Editing Facts in GPT (2021)
• KILT: a Benchmark for Knowledge Intensive Language Tasks (2020) — 820 citations
• The Power Spherical distribution (2020) — 42 citations
• Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking (2020) — 298 citations
• How Do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking (2020) — 104 citations