Lucas Caccia
Artificial Intelligence · 2018–2022
A researcher in the Science corpus: 10 papers; trusts 669 researchers and is trusted by 46 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 286 citations into it; #6,127 of 35,345 by trust here, #3,598 by citations
- Computer Vision and Pattern Recognition — 206 citations into it; #5,157 of 21,575 by trust here, #3,124 by citations
- Meta & Continual Learning — 132 citations into it; #122 of 1,041 by trust here, #62 by citations
- Representation & Self-Supervised Learning — 93 citations into it
- Reinforcement Learning — 75 citations into it
- Optimization & Training — 36 citations into it
- Generative Models — 32 citations into it
- Evaluation & Benchmarks — 25 citations into it
Whom Lucas Caccia trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Kaiming He
- Rahaf Aljundi
- Arslan Chaudhry
- Diederik P. Kingma
- Marc'Aurelio Ranzato
- Yann LeCun
- Ilya Sutskever
Who trusts Lucas Caccia overall
- Timothée Lesort
- Oleksiy Ostapenko
- Massimo Caccia
- Prashant Bhat
- Liyuan Wang
- Matteo Boschini
- Jorge Armando Mendez Mendez
- Zheda Mai
- Pau Rodr'iguez L'opez
- Yiduo Guo
Papers from Lucas Caccia's perspective
Ordered by how strongly the researchers Lucas Caccia 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)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Online Continual Learning with Maximally Interfered Retrieval (2019) — 692 citations
- Language GANs Falling Short (2018) — 240 citations
- Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning (2020) — 68 citations
- SPeCiaL: Self-Supervised Pretraining for Continual Learning (2021) — 20 citations
- On Anytime Learning at Macroscale (2021) — 29 citations
- Online Learned Continual Compression with Stacked Quantization Module (2019) — 6 citations
- New Insights on Reducing Abrupt Representation Change in Online Continual Learning (2021) — 295 citations
- Building a Subspace of Policies for Scalable Continual Learning (2022) — 41 citations
- Reducing Representation Drift in Online Continual Learning (2021) — 21 citations
- Online Learned Continual Compression with Adaptive Quantization Modules (2019) — 97 citations