Massimiliano Patacchiola

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 5 papers; trusts 489 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 136 citations into it; #24,810 of 35,345 by trust here, #19,439 by citations
• Computer Vision and Pattern Recognition — 120 citations into it; #15,263 of 21,575 by trust here, #13,269 by citations
• Representation & Self-Supervised Learning — 50 citations into it
• Meta & Continual Learning — 31 citations into it
• Optimization & Training — 26 citations into it
• Evaluation & Benchmarks — 24 citations into it
• Model Architectures — 12 citations into it
• Efficiency & Compression — 10 citations into it

Whom Massimiliano Patacchiola trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Yoshua Bengio
5. Diederik P. Kingma
6. Kaiming He
7. Ilya Sutskever
8. Ruslan Salakhutdinov
9. Chelsea Finn
10. Oriol Vinyals

Who trusts Massimiliano Patacchiola overall

1. Nataniel Ruiz
2. Steinar Laenen
3. Jake Snell
4. Yisheng Song
5. Eunji Chong
6. Zhenyi Wang
7. Richard S. Zemel
8. Luca Bertinetto
9. James M. Rehg
10. Timothy M. Hospedales

Papers from Massimiliano Patacchiola's perspective

Ordered by how strongly the researchers Massimiliano Patacchiola 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Defining Benchmarks for Continual Few-Shot Learning (2020) — 42 citations
• FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification (2022) — 37 citations
• Memory Efficient Meta-Learning with Large Images (2021) — 21 citations
• Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels (2020) — 163 citations
• Head pose estimation in the wild using Convolutional Neural Networks and adaptive gradient methods (2017) — 201 citations