Máté Lengyel
Cognitive Neuroscience · 2007–2018
A researcher in the Science corpus: 8 papers; trusts 65 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 67 citations into it; #3,978 of 10,852 by trust here, #3,802 by citations
- Artificial Intelligence — 36 citations into it; #13,950 of 35,345 by trust here, #12,580 by citations
- Cellular and Molecular Neuroscience — 22 citations into it
- Optimization & Training — 20 citations into it; #2,341 of 2,601 by trust here, #2,378 by citations
- Probabilistic & Bayesian Methods — 18 citations into it; #742 of 1,525 by trust here, #700 by citations
- Computer Vision and Pattern Recognition — 15 citations into it
- Developmental and Educational Psychology — 11 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
Whom Máté Lengyel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Radford M. Neal
- James Martens
- Richard S. Sutton
- Andrew Barto
- Ben J. A. Kröse
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Shun-ichi Amari
- Jimmy Ba
Who trusts Máté Lengyel overall
- Nathaniel D. Daw
- A. G. Collins
- Demis Hassabis
- Su Young Lee
- Tyler L. Hayes
- Nikolaus Kriegeskorte
- Peter S. Dayan
- Michael J Frank
- John P. O'Doherty
- Dharshan Kumaran
Papers from Máté Lengyel's perspective
Ordered by how strongly the researchers Máté Lengyel 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Bayesian Active Learning for Classification and Preference Learning (2011) — 1,125 citations
- Bayesian learning of visual chunks by human observers (2008) — 276 citations
- Exact natural gradient in deep linear networks and its application to the nonlinear case (2018) — 75 citations
- The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics (2014) — 74 citations
- Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials (2010)
- Statistically optimal perception and learning: from behavior to neural representations (2010) — 727 citations
- Know Thy Neighbour: A Normative Theory of Synaptic Depression (2009) — 16 citations
- Hippocampal Contributions to Control: The Third Way (2007) — 256 citations