Anthony M. Zador
Cognitive Neuroscience · 1997–2022
A researcher in the Science corpus: 7 papers; trusts 24 researchers and is trusted by 37 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 40 citations into it; #1,112 of 10,852 by trust here, #1,664 by citations
- Cellular and Molecular Neuroscience — 33 citations into it; #1,167 of 2,998 by trust here, #1,612 by citations
- Artificial Intelligence — 22 citations into it; #13,956 of 35,345 by trust here, #21,878 by citations
- Computer Vision and Pattern Recognition — 16 citations into it
- Neuroscience — 14 citations into it
- Model Architectures — 8 citations into it
- Molecular Biology — 8 citations into it
- Cognitive Science & Psychology — 7 citations into it
Whom Anthony M. Zador trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Joaquin Vanschoren
- Anthony J. Bell
- Sinno Jialin Pan
- Yann LeCun
- Terrence J. Sejnowski
- Geoffrey E. Hinton
- Yoshua Bengio
- Qiang Yang
- W. Maas
- David E. Rumelhart
Who trusts Anthony M. Zador overall
- Gianluca Lentini
- Sara Hooker
- Giorgio Grioli
- Nicholas J. Priebe
- Max Welling
- Adam Gaier
- Gerard David Howard
- C. Poo
- S. Katzner
- T. Hromádka
Papers from Anthony M. Zador's perspective
Ordered by how strongly the researchers Anthony M. Zador 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)
- Gradient-based learning applied to document 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)
- 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)
- Going deeper with convolutions
Their papers
- A critique of pure learning and what artificial neural networks can learn from animal brains (2019) — 547 citations
- Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex (2003) — 1,446 citations
- Catalyzing next-generation Artificial Intelligence through NeuroAI (2022) — 299 citations
- Grand Challenges for Global Brain Sciences (2016)
- Sparse Representation of Sounds in the Unanesthetized Auditory Cortex (2008) — 605 citations
- Postsynaptic Receptor Trafficking Underlying a Form of Associative Learning (2005) — 802 citations
- Dynamic Stochastic Synapses as Computational Units (1997)