Ila Rani Fiete
Cognitive Neuroscience · 2006–2021
A researcher in the Science corpus: 7 papers; trusts 43 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 91 citations into it; #2,056 of 10,852 by trust here, #3,875 by citations
- Cellular and Molecular Neuroscience — 84 citations into it; #848 of 2,998 by trust here, #1,000 by citations
- Neuroscience — 19 citations into it
- Sensory Systems — 17 citations into it
- Computer Vision and Pattern Recognition — 16 citations into it; #16,017 of 21,575 by trust here, #20,271 by citations
- Artificial Intelligence — 13 citations into it; #10,106 of 35,345 by trust here, #22,463 by citations
- Cell Biology — 10 citations into it
- Electrical and Electronic Engineering — 10 citations into it; #3,125 of 16,539 by trust here, #11,977 by citations
Whom Ila Rani Fiete trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Xiao-Jing Wang
- John J. Hopfield
- Sebastian Thrun
- Geoffrey E. Hinton
- Mark C. Fuhs
- Sam T. Roweis
- Richard S. Sutton
- Yoram Burak
- L. V. D. van der Maaten
- David H. Hubel
Who trusts Ila Rani Fiete overall
- Yoshua Bengio
- H. Francis Song
- Chaopeng Shen
- Mikail Khona
- Johannes Friedrich
- Aaron C. Courville
- Sergey Levine
- Andrea Vedaldi
- Quoc V. Le
- Ruslan Salakhutdinov
Papers from Ila Rani Fiete's perspective
Ordered by how strongly the researchers Ila Rani Fiete 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
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Reinforcement Learning: An Introduction
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Gradient learning in spiking neural networks by dynamic perturbation of conductances. (2006) — 133 citations
- Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems (2016) — 60 citations
- Attractor and integrator networks in the brain (2021) — 339 citations
- The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep (2019) — 362 citations
- A model of grid cell development through spatial exploration and spike time-dependent plasticity. (2014) — 101 citations
- Testing odor response stereotypy in the Drosophila mushroom body. (2008) — 174 citations
- Accurate Path Integration in Continuous Attractor Network Models of Grid Cells (2008) — 777 citations