Davide Testuggine

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 119 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 108 citations into it; #31,058 of 35,345 by trust here, #9,275 by citations
• Computer Vision and Pattern Recognition — 79 citations into it; #19,090 of 21,575 by trust here, #7,092 by citations
• Multimodal Learning — 62 citations into it; #1,023 of 1,211 by trust here, #583 by citations
• Natural Language Processing — 57 citations into it
• Evaluation & Benchmarks — 18 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Large Language Models — 11 citations into it
• Model Architectures — 10 citations into it

Whom Davide Testuggine trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Kaiming He
4. Geoffrey E. Hinton
5. Yann LeCun
6. Yoshua Bengio
7. Ilya Sutskever
8. Ross B. Girshick
9. Jimmy Ba
10. Alec Radford

Who trusts Davide Testuggine overall

1. Aashish Jain
2. Michael Sollami

Papers from Davide Testuggine's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020) — 939 citations
• Supervised Multimodal Bitransformers for Classifying Images and Text (2019) — 320 citations
• Opacus: User-Friendly Differential Privacy Library in PyTorch (2021) — 551 citations