L. C. Lamb
Artificial Intelligence · 2003–2020
A researcher in the Science corpus: 11 papers; trusts 82 researchers and is trusted by 28 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 120 citations into it; #6,745 of 35,345 by trust here, #6,014 by citations
- Computer Vision and Pattern Recognition — 40 citations into it
- Natural Language Processing — 31 citations into it; #1,901 of 5,688 by trust here, #2,364 by citations
- Model Architectures — 28 citations into it
- Cognitive Neuroscience — 21 citations into it; #7,007 of 10,852 by trust here, #6,902 by citations
- Computational Theory and Mathematics — 21 citations into it; #11,801 of 12,182 by trust here, #4,365 by citations
- Probabilistic & Bayesian Methods — 16 citations into it
- Knowledge Graphs & Representation — 11 citations into it; #169 of 226 by trust here, #122 by citations
Whom L. C. Lamb trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- A.S. d'Avila Garcez
- Leslie G. Valiant
- Geoffrey E. Hinton
- Ronald Fagin
- Alex Krizhevsky
- Yoshua Bengio
- David E. Rumelhart
- Ilya Sutskever
- Diederik P. Kingma
- Richard Socher
Who trusts L. C. Lamb overall
- A.S. d'Avila Garcez
- Tarek R. Besold
- Luciano Serafini
- Won Ik Cho
- Emilio Ferrara
- Marco Gori
- Danielle Saunders
- Maxwell Nye
- Son Dinh Tran
- Adithya Renduchintala
Papers from L. C. Lamb's perspective
Ordered by how strongly the researchers L. C. Lamb 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)
- 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Neurosymbolic AI: the 3rd wave (2020) — 562 citations
- Assessing gender bias in machine translation: a case study with Google Translate (2018) — 403 citations
- Neural-Symbolic Cognitive Reasoning (2008) — 232 citations
- Neural-Symbolic Learning and Reasoning: A Survey and Interpretation (2017) — 472 citations
- Learning to Solve NP-Complete Problems - A Graph Neural Network for the Decision TSP (2018) — 206 citations
- Learning and Representing Temporal Knowledge in Recurrent Networks (2011) — 50 citations
- Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019) — 392 citations
- Neural-Symbolic Learning and Reasoning: Contributions and Challenges (2015) — 201 citations
- A Connectionist Cognitive Model for Temporal Synchronisation and Learning (2007) — 26 citations
- A Connectionist Computational Model for Epistemic and Temporal Reasoning (2006) — 31 citations
- Reasoning about Time and Knowledge in Neural Symbolic Learning Systems (2003) — 38 citations