Simone Scardapane

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 12 papers; trusts 855 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 334 citations into it; #13,383 of 35,345 by trust here, #11,490 by citations
• Computer Vision and Pattern Recognition — 246 citations into it; #7,336 of 21,575 by trust here, #7,584 by citations
• Optimization & Training — 75 citations into it
• Model Architectures — 59 citations into it
• Representation & Self-Supervised Learning — 57 citations into it
• Computer Networks and Communications — 50 citations into it
• Media Technology — 46 citations into it; #3,311 of 3,621 by trust here, #3,522 by citations
• Efficiency & Compression — 43 citations into it

Whom Simone Scardapane trusts overall

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

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

Who trusts Simone Scardapane overall

1. Laura Palagi
2. Sangwon Jung
3. Jiangpeng He
4. Gabriel Erion-Barner
5. Christos Louizos
6. Yaniv Leviathan
7. Hongjoon Ahn
8. Michele Scarpiniti
9. Tsubasa Ochiai
10. Dianhui Wang

Papers from Simone Scardapane's perspective

Ordered by how strongly the researchers Simone Scardapane 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Group sparse regularization for deep neural networks (2016) — 500 citations
• Avalanche: an End-to-End Library for Continual Learning (2021) — 219 citations
• Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence (2023) — 1,661 citations
• Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022) — 37 citations
• MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing (2021) — 31 citations
• Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications (2020) — 32 citations
• Why Should We Add Early Exits to Neural Networks? (2020) — 152 citations
• Differentiable Branching In Deep Networks for Fast Inference (2020) — 32 citations
• Efficient Continual Learning in Neural Networks with Embedding Regularization (2019) — 47 citations
• Randomness in neural networks: an overview (2017) — 343 citations
• A Framework for Parallel and Distributed Training of Neural Networks (2016) — 27 citations
• Distributed learning for Random Vector Functional-Link networks (2015) — 148 citations