R. V. Rullen
Cognitive Neuroscience · 2001–2021
A researcher in the Science corpus: 4 papers; trusts 59 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 24 citations into it; #2,420 of 10,852 by trust here, #4,260 by citations
- Computer Vision and Pattern Recognition — 22 citations into it
- Artificial Intelligence — 18 citations into it
- Representation & Self-Supervised Learning — 13 citations into it
- Multimodal Learning — 10 citations into it
- Neuroscience — 6 citations into it
- Safety, Robustness & Interpretability — 6 citations into it
Whom R. V. Rullen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David J. Field
- Stéphane Mallat
- Simon J. Thorpe
- Alex Krizhevsky
- Geoffrey E. Hinton
- Kaiming He
- Yann LeCun
- David J. Heeger
- Yoshua Bengio
- Ilya Sutskever
Who trusts R. V. Rullen overall
- Kaushik Roy
- Filip Ponulak
- Simon J. Thorpe
- Zhenan Sun
- Wolf Singer
- Akhilesh R. Jaiswal
- Timothée Masquelier
- Andrzej J. Kasinski
- S. R. Kheradpisheh
- Priyadarshini Panda
Papers from R. V. Rullen's perspective
Ordered by how strongly the researchers R. V. Rullen 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
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Spike-based strategies for rapid processing (2001) — 786 citations
- Does language help generalization in vision models? (2021) — 28 citations
- A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure (2016) — 22 citations
- Rate Coding Versus Temporal Order Coding: What the Retinal Ganglion Cells Tell the Visual Cortex (2001) — 558 citations