Niko Wilbert
Cognitive Neuroscience · 2008–2014
A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 25 citations into it; #1,987 of 10,852 by trust here, #4,443 by citations
- Representation & Self-Supervised Learning — 11 citations into it; #1,989 of 3,145 by trust here, #2,624 by citations
- Cellular and Molecular Neuroscience — 10 citations into it
- Electrical and Electronic Engineering — 10 citations into it
- Signal Processing — 9 citations into it
- Artificial Intelligence — 8 citations into it; #13,181 of 35,345 by trust here, #23,731 by citations
- Analytical Chemistry — 6 citations into it; #1,037 of 1,254 by trust here, #893 by citations
- Computer Vision and Pattern Recognition — 6 citations into it; #12,249 of 21,575 by trust here, #16,367 by citations
Whom Niko Wilbert trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Laurenz Wiskott
- Mathias Franzius
- Peter Földiák
- Radford M. Neal
- Pietro Berkes
- Geoffrey E. Hinton
- Wulfram Gerstner
- Athanasios C. Antoulas
- David L. Donoho
- Terrence J. Sejnowski
Who trusts Niko Wilbert overall
- J. Urgen Schmidhuber
- Will Y. Zou
- Nitish Srivastava
- Robert A. Legenstein
- Wendelin Böhmer
- Laurenz Wiskott
- Rico Jonschkowski
- Alberto N. Escalante
- Radim Rehurek
- Jelle Munk
Papers from Niko Wilbert's perspective
Ordered by how strongly the researchers Niko Wilbert trusts endorse each paper. Same corpus, a different order for every researcher.
- Reinforcement Learning: An Introduction
- A Fast Learning Algorithm for Deep Belief Nets
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Pattern Recognition and Machine Learning
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Slow Feature Analysis: Unsupervised Learning of Invariances
Their papers
- Invariant Object Recognition with Slow Feature Analysis (2008) — 74 citations
- Reinforcement Learning on Slow Features of High-Dimensional Input Streams (2010) — 95 citations
- Slow Feature Analysis (2014) — 54 citations
- Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework (2008)