J. Krichmar
Cognitive Neuroscience · 2001–2022
A researcher in the Science corpus: 9 papers; trusts 82 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 108 citations into it; #2,198 of 10,852 by trust here, #5,396 by citations
- Artificial Intelligence — 86 citations into it
- Cellular and Molecular Neuroscience — 69 citations into it; #516 of 2,998 by trust here, #1,976 by citations
- Computer Vision and Pattern Recognition — 40 citations into it; #4,323 of 21,575 by trust here, #11,083 by citations
- Electrical and Electronic Engineering — 35 citations into it
- Neuroscience — 35 citations into it
- Reinforcement Learning — 29 citations into it
- Representation & Self-Supervised Learning — 25 citations into it
Whom J. Krichmar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kenji Doya
- E. Todorov
- Richard S. Sutton
- Olivier Sigaud
- Kent C. Berridge
- Andrew Barto
- Duy Nguyen-Tuong
- Freek Stulp
- M. Millan
- William H. Alexander
Who trusts J. Krichmar overall
- Andrea Soltoggio
- Thomas M. Smith
- Leandro Nunes de Castro
- Dario Floreano
- Mehdi Khamassi
- Kenneth O. Stanley
- Claudio Mattiussi
- Gianluca Baldassarre
- E. D. Di Paolo
- Yoonseob Kim
Papers from J. Krichmar's perspective
Ordered by how strongly the researchers J. Krichmar 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
- Reinforcement Learning: An Introduction
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Human-level control through deep reinforcement learning
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Attention-Based Structural-Plasticity (2019) — 12 citations
- The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World (2008) — 138 citations
- Biological underpinnings for lifelong learning machines (2022) — 294 citations
- An efficient automated parameter tuning framework for spiking neural networks (2014) — 73 citations
- Attention-Based Structural-Plasticity (2019)
- Value and reward based learning in neurorobots (2013) — 20 citations
- Neuromodulation as a robot controller (2009) — 51 citations
- as a Robot Controller Neuromodulation as a Robot Controller A Brain-Inspired Strategy for Controlling Autonomous Robots (2009) — 27 citations
- Evolutionary robotics: The biology, intelligence, and technology of self-organizing machines (2001) — 1,140 citations