Boi Faltings
Artificial Intelligence · 2005–2020
A researcher in the Science corpus: 15 papers; trusts 474 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 231 citations into it; #10,547 of 35,345 by trust here, #7,858 by citations
- Computer Vision and Pattern Recognition — 85 citations into it
- Natural Language Processing — 45 citations into it
- Privacy & Security — 37 citations into it
- Safety, Robustness & Interpretability — 36 citations into it
- Meta & Continual Learning — 35 citations into it; #1,004 of 1,041 by trust here, #711 by citations
- Representation & Self-Supervised Learning — 30 citations into it
- Optimization & Training — 28 citations into it; #2,159 of 2,601 by trust here, #2,259 by citations
Whom Boi Faltings trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cynthia Dwork
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
- Yoshua Bengio
- Max Welling
- Ilya Sutskever
- Christian Szegedy
Who trusts Boi Faltings overall
- Adam Richardson
- Sergey I. Nikolenko
- Alessandro Farinelli
- Alex Rogers
- Ahmed El Ouadrhiri
- Chaoyang He
- Xuefei Yin
- Aris Filos-Ratsikas
- Qinbin Li
- Qiang Yang
Papers from Boi Faltings's perspective
Ordered by how strongly the researchers Boi Faltings 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
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems (2019) — 101 citations
- Personalization in Goal-Oriented Dialog (2017) — 105 citations
- Decentralized Anti-coordination Through Multi-agent Learning (2013) — 24 citations
- Federated Learning with Bayesian Differential Privacy (2019) — 212 citations
- Offline and online evaluation of news recommender systems at swissinfo.ch (2014) — 193 citations
- Rewarding High-Quality Data via Influence Functions (2019) — 45 citations
- Non-Discriminatory Machine Learning through Convex Fairness Criteria (2018) — 103 citations
- Generalized Class Incremental Learning (2020) — 48 citations
- ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation (2020) — 68 citations
- Continual Learning for Natural Language Generation in Task-oriented Dialog Systems (2020) — 84 citations
- Federated Generative Privacy (2019) — 72 citations
- Generating Differentially Private Datasets Using GANs (2018) — 41 citations
- Game Theory for Data Science: Eliciting Truthful Information (2017) — 51 citations
- A Scalable Method for Multiagent Constraint Optimization (2005) — 661 citations
- Retrieval, reuse, revision and retention in case-based reasoning (2005) — 636 citations