Research graph
References from Node perturbation can effectively train multi-layer neural networks. Local targets link to admitted publications; unresolved targets remain external evidence.
Correlations are ruining your gradient descent
2024 · External reference
Constrained parameter inference as a principle for learning
10.48550/arxiv.2203.13203 · 2023 · External reference
Natural gradient works efficiently in learning
10.1162/089976698300017746 · 1998 · External reference
Mach band type lateral inhibition in different sense organs
10.1085/jgp.50.3.519 · 1967 · External reference
Decorrelation speeds up vision transformers
2025 · External reference
A fast stochastic error-descent algorithm for supervised learning and optimization
1992 · External reference
Node perturbation learning without noiseless baseline
10.1016/j.neunet.2010.12.001 · 2011 · External reference
The recent excitement about neural networks
10.1038/337129a0 · 1989 · External reference
Efficient deep learning with decorrelated backpropagation
2024 · External reference
Error-driven input modulation: solving the credit assignment problem without a backward pass
2022 · External reference
Model-free distributed learning
10.1109/72.80205 · 1990 · External reference
Natural neural networks
2015 · External reference
An image is worth 16x16 words: transformers for image recognition at scale
2020 · External reference
Noise in the nervous system
10.1038/nrn2258 · 2008 · External reference
Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances
10.1152/jn.01311.2006 · 2007 · External reference
Forming sparse representations by local anti-Hebbian learning
10.1007/bf02331346 · 1990 · External reference
Learning without feedback: fixed random learning signals allow for feedforward training of deep neural networks
10.3389/fnins.2021.629892 · 2021 · External reference
Enabling training of neural networks on noisy hardware
10.3389/frai.2021.699148 · 2021 · External reference
Competitive learning: from interactive activation to adaptive resonance
10.1111/j.1551-6708.1987.tb00862.x · 1987 · External reference
Gradients without backpropagation
2022 · External reference
Deep residual learning for image recognition
2016 · External reference
The forward-forward algorithm: some preliminary investigations
2022 · External reference
On the stability and scalability of node perturbation learning
10.52202/068431-2314 · 2022 · External reference
The rise of intelligent matter
10.1038/s41586-021-03453-y · 2021 · External reference
Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1
10.1523/jneurosci.4188-12.2013 · 2013 · External reference
Error forward-propagation: reusing feedforward connections to propagate errors in deep learning
2018 · External reference
Learning multiple layers of features from tiny images
2009 · External reference
Tiny ImageNet visual recognition challenge
2015 · External reference
Efficient backprop
2002 · External reference
Random synaptic feedback weights support error backpropagation for deep learning
10.1038/ncomms13276 · 2016 · External reference
The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors
1970 · External reference
Learning deep architectures via generalized whitened neural networks
2017 · External reference
Direct feedback alignment provides learning in deep neural networks
2016 · External reference
Scaling forward gradient with local losses
2022 · External reference
Equilibrium propagation: bridging the gap between energy-based models and backpropagation
10.3389/fncom.2017.00024 · 2017 · External reference
A distributed solution for data orthonormalization
1991 · External reference
Speeding-up backpropagation by data orthonormalization
1991 · External reference
Locally weighted projection regression: an O(n) algorithm for incremental real time learning in high dimensional space
2000 · External reference
Whitening and second order optimization both make information in the dataset unusable during training and can reduce or prevent generalization
2021 · External reference
Brain-inspired learning on neuromorphic substrates
10.1109/jproc.2020.3045625 · 2021 · External reference
