Pietro Berkes
Cognitive Neuroscience · 2005–2014
A researcher in the Science corpus: 6 papers; trusts 30 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 52 citations into it; #601 of 10,852 by trust here, #1,681 by citations
- Representation & Self-Supervised Learning — 25 citations into it
- Computer Vision and Pattern Recognition — 22 citations into it
- Signal Processing — 22 citations into it; #2,435 of 7,572 by trust here, #5,086 by citations
- Neuroscience — 18 citations into it
- Artificial Intelligence — 14 citations into it; #12,872 of 35,345 by trust here, #25,809 by citations
- Probabilistic & Bayesian Methods — 13 citations into it
- Analytical Chemistry — 7 citations into it; #1,039 of 1,254 by trust here, #893 by citations
Whom Pietro Berkes trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Laurenz Wiskott
- Aapo Hyvärinen
- David J. Field
- Peter S. Dayan
- Geoffrey E. Hinton
- David H. Hubel
- Terrence J. Sejnowski
- Radford M. Neal
- Patrik O. Hoyer
- David John Cameron MacKay
Who trusts Pietro Berkes overall
- Quoc V. Le
- Laurenz Wiskott
- Will Y. Zou
- Yoshua Bengio
- Dacheng Tao
- Zhang Zhang
- Matthew D. Zeiler
- Mathias Franzius
- Geoffrey E. Hinton
- J. Urgen Schmidhuber
Papers from Pietro Berkes's perspective
Ordered by how strongly the researchers Pietro Berkes 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- 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
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- GENERATIVE ADVERSARIAL NETS
Their papers
- On the Analysis and Interpretation of Inhomogeneous Quadratic Forms as Receptive Fields (2006) — 55 citations
- Slow Feature Analysis (2014) — 54 citations
- Statistically optimal perception and learning: from behavior to neural representations (2010) — 727 citations
- A Structured Model of Video Reproduces Primary Visual Cortical Organisation (2009) — 44 citations
- Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework (2008)
- Slow feature analysis yields a rich repertoire of complex cell properties. (2005) — 324 citations