On the search for new learning rules for ANNs

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Meta-Learning: A Survey (2018, neutral): “r et al., 1997) or the learning rate for gradient descent (Daniel et al., 2016) to the task at hand. Inspired by the feeling that backpropagation is an unlikely learning mechanism for our own brains, Bengio et al. (1995) replace backpropagation with simple biologically-inspired parametric rules (or ”
Learned Optimizers that Scale and Generalize (2017, methodology): “A series of papers from Bengio et al. (1990; 1992; 1995) presents methods for learning parameterized local neural network update rules that avoid back-propagation.”
Meta-Learning in Neural Networks: A Survey (2020, background): “[46], [47] proposed to meta-learn biologically plausible learning rules.”
Meta-learning curiosity algorithms (2020, background): “…al., 2017; Gaier & Ha, 2019), searching over programs to solve games (Wilson et al., 2018; Kelly & Heywood, 2017; Silver et al., 2019) and to optimize neural networks (Bengio et al., 1995; Bello et al., 2017), and neural networks that learn programs (Reed & De Freitas, 2015; Pierrot et al., 2019).”
A large-scale benchmark for few-shot program induction and synthesis (2021, background): “Learning to few-shot learn meta-learning (Schmidhuber, 1987; Bengio et al., 1995; Thrun & Pratt, 1998) aims at learning priors from many tasks to generalize to a new task from small amounts of data.”