Enzo Baccarelli
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 5 papers; trusts 145 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 79 citations into it
- Computer Vision and Pattern Recognition — 79 citations into it
- Computer Networks and Communications — 55 citations into it
- Optimization & Training — 27 citations into it
- Representation & Self-Supervised Learning — 5 citations into it
Whom Enzo Baccarelli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Eleonora Borgia
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Md. Redowan Mahmud
- Yann LeCun
- Kaiming He
- Rajkumar Buyya
- Christian Szegedy
- Diederik P. Kingma
Who trusts Enzo Baccarelli overall
Papers from Enzo Baccarelli's perspective
Ordered by how strongly the researchers Enzo Baccarelli 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
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
Their papers
- EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications (2019) — 36 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
- Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017) — 315 citations