Massimiliano Mancini

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 9 papers; trusts 1,488 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 371 citations into it; #9,748 of 35,345 by trust here, #7,112 by citations
• Computer Vision and Pattern Recognition — 371 citations into it; #7,321 of 21,575 by trust here, #5,620 by citations
• Representation & Self-Supervised Learning — 184 citations into it; #2,636 of 3,145 by trust here, #2,217 by citations
• Meta & Continual Learning — 55 citations into it; #367 of 1,041 by trust here, #342 by citations
• Optimization & Training — 38 citations into it
• Model Architectures — 34 citations into it
• Natural Language Processing — 33 citations into it; #4,570 of 5,688 by trust here, #5,296 by citations
• Evaluation & Benchmarks — 24 citations into it

Whom Massimiliano Mancini trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ross B. Girshick
7. Yann LeCun
8. Ilya Sutskever
9. Karen Simonyan
10. Christian Szegedy

Who trusts Massimiliano Mancini overall

1. Arthur Douillard
2. Fabio Cermelli
3. José Camacho-Collados
4. Francesco Barbieri
5. Evani Radiya-Dixit
6. Haohan Wang
7. Fengchun Qiao
8. Haim Dubossarsky
9. Kaiyang Zhou
10. Oleksiy Ostapenko

Papers from Massimiliano Mancini's perspective

Ordered by how strongly the researchers Massimiliano Mancini 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Modeling the Background for Incremental Learning in Semantic Segmentation (2020) — 373 citations
• Best Sources Forward: Domain Generalization through Source-Specific Nets (2018) — 173 citations
• AdaGraph: Unifying Predictive and Continuous Domain Adaptation Through Graphs (2019) — 77 citations
• Adding New Tasks to a Single Network with Weight Trasformations using Binary Masks (2018) — 56 citations
• Semi-Supervised and Unsupervised Deep Visual Learning: A Survey (2022) — 210 citations
• Prototype-based Incremental Few-Shot Segmentation (2020) — 44 citations
• A Few Guidelines for Incremental Few-Shot Segmentation (2020) — 10 citations
• Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition (2019) — 26 citations
• Embedding Words and Senses Together via Joint Knowledge-Enhanced Training (2016) — 80 citations