Enrico Fini
Artificial Intelligence · 2020–2022
A researcher in the Science corpus: 8 papers; trusts 643 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 230 citations into it; #22,793 of 35,345 by trust here, #11,192 by citations
- Computer Vision and Pattern Recognition — 226 citations into it; #16,755 of 21,575 by trust here, #8,612 by citations
- Representation & Self-Supervised Learning — 126 citations into it
- Meta & Continual Learning — 73 citations into it; #1,029 of 1,041 by trust here, #529 by citations
- Radiology, Nuclear Medicine and Imaging — 41 citations into it
- Optimization & Training — 21 citations into it
- Model Architectures — 18 citations into it
- Efficiency & Compression — 15 citations into it
Whom Enrico Fini trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Ross B. Girshick
- Diederik P. Kingma
- Ilya Sutskever
- Karen Simonyan
- Jia Deng
Who trusts Enrico Fini overall
- K. J. Joseph
- Liyuan Wang
- Kai Han
- Sagar Vaze
- Subhankar Roy
- Sujoy Paul
- Quang Pham
- Yabin Wang
- Mert Kilickaya
- Xingxing Zhang
Papers from Enrico Fini's perspective
Ordered by how strongly the researchers Enrico Fini trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Self-Supervised Models are Continual Learners (2021) — 236 citations
- Online Continual Learning under Extreme Memory Constraints (2020) — 74 citations
- Neighborhood Contrastive Learning for Novel Class Discovery (2021) — 202 citations
- Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning (2021) — 172 citations
- A Unified Objective for Novel Class Discovery (2021) — 266 citations
- Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation (2022) — 97 citations
- A soft nearest-neighbor framework for continual semi-supervised learning (2022) — 26 citations
- Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound (2020) — 462 citations