Davide Bacciu
Artificial Intelligence · 2018–2022
A researcher in the Science corpus: 7 papers; trusts 255 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 192 citations into it; #30,479 of 35,345 by trust here, #12,244 by citations
- Computer Vision and Pattern Recognition — 110 citations into it; #18,784 of 21,575 by trust here, #9,079 by citations
- Graph Learning — 69 citations into it
- Meta & Continual Learning — 46 citations into it
- Representation & Self-Supervised Learning — 43 citations into it
- Model Architectures — 35 citations into it
- Molecular Biology — 33 citations into it
- Statistical and Nonlinear Physics — 28 citations into it
Whom Davide Bacciu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Thomas Kipf
- Max Welling
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Ilya Sutskever
- Radford M. Neal
Who trusts Davide Bacciu overall
Papers from Davide Bacciu's perspective
Ordered by how strongly the researchers Davide Bacciu 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Avalanche: an End-to-End Library for Continual Learning (2021) — 219 citations
- A Fair Comparison of Graph Neural Networks for Graph Classification (2019) — 527 citations
- A Gentle Introduction to Deep Learning for Graphs (2019) — 326 citations
- Continual Learning with Gated Incremental Memories for sequential data processing (2020) — 27 citations
- Continual Pre-Training Mitigates Forgetting in Language and Vision (2022) — 111 citations
- Practical Recommendations for Replay-based Continual Learning Methods (2022) — 32 citations
- Generative Kernels for Tree-Structured Data (2018) — 24 citations