Nicola Cancedda

Artificial Intelligence · 2023

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it

Whom Nicola Cancedda trusts

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

1. Timo Schick (in Artificial Intelligence)
2. Hinrich Schütze (in Artificial Intelligence)

Papers from Nicola Cancedda's perspective

Ordered by how strongly the researchers Nicola Cancedda trusts endorse each paper. Same corpus, a different order for every researcher.

1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
2. Language Models are Few-Shot Learners (Neural Information Processing Systems)
3. Language Models are Unsupervised Multitask Learners
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Pointer Sentinel Mixture Models
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. REALM: Retrieval-Augmented Language Model Pre-Training (International Conference on Machine Learning)
9. ImageNet classification with deep convolutional neural networks
10. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Their papers

• Toolformer: Language Models Can Teach Themselves to Use Tools (2023) — 4,753 citations