Davide Abati
Computer Vision and Pattern Recognition · 2017–2020
A researcher in the Science corpus: 5 papers; trusts 367 researchers and is trusted by 35 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 116 citations into it; #10,757 of 21,575 by trust here, #7,249 by citations
- Artificial Intelligence — 102 citations into it; #5,299 of 35,345 by trust here, #4,298 by citations
- Representation & Self-Supervised Learning — 25 citations into it; #1,792 of 3,145 by trust here, #1,479 by citations
- Optimization & Training — 22 citations into it
- Model Architectures — 21 citations into it
- Cognitive Neuroscience — 17 citations into it
- Meta & Continual Learning — 17 citations into it; #192 of 1,041 by trust here, #159 by citations
- Generative Models — 13 citations into it
Whom Davide Abati trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Diederik P. Kingma
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Sergey Ioffe
- Hugo Larochelle
- David G. Lowe
Who trusts Davide Abati overall
- Gyuhak Kim
- Jiangpeng He
- Zixuan Ke
- Changnan Xiao
- Yiduo Guo
- Matteo Boschini
- Jingkang Yang
- Hanqiu Deng
- Feng Zhu
- Bin Liu
Papers from Davide Abati's perspective
Ordered by how strongly the researchers Davide Abati trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Conditional Channel Gated Networks for Task-Aware Continual Learning (2020) — 208 citations
- Dark Experience for General Continual Learning: a Strong, Simple Baseline (2020) — 1,381 citations
- Latent Space Autoregression for Novelty Detection (2018) — 489 citations
- Classifying Signals on Irregular Domains via Convolutional Cluster Pooling (2019) — 11 citations
- Predicting the Driver's Focus of Attention: The DR(eye)VE Project (2017) — 322 citations