Meta-learning with backpropagation

By A. S. Younger, Sepp Hochreiter, Peter R. Conwell. 2001

62 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Representation & Self-Supervised Learning

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Citations in the corpus

RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning (2016, background): “Another line of work (Hochreiter et al., 2001; Younger et al., 2001; Andrychowicz et al., 2016; Li & Malik, 2016) studies meta-learning over the optimization process.”
CrossTransformers: spatially-aware few-shot transfer (2020, background): “The meta-learner either learns to produce new parameters directly from the new data [9, 33, 56, 62, 64, 72, 73], or learns to produce an update rule to iteratively optimize the base learner to fit the new data [2, 6, 8, 38, 63, 97].”
Meta-learning with differentiable closed-form solvers (2018, background): “Younger et al. (2001) and Hochreiter et al. (2001) observed that, because of the sequential nature of a learning algorithm, a recurrent neural network can be considered as a meta-learning system.”
Learning to Learn with Generative Models of Neural Network Checkpoints (2022, background): “They are typically trained with unrolled optimization (Hochreiter et al., 2001; Younger et al., 2001; Gregor & LeCun, 2010; Andrychowicz et al., 2016; Ravi & Larochelle, 2017; Wichrowska et al., 2017; Lv et al., 2017; Metz et al., 2019; 2022) or reinforcement learning (Li & Malik, 2016; 2017).”
Meta-Learning in Neural Networks: A Survey (2020, methodology): “Proposals for training meta-learning systems using gradient descent and backpropagation were first made in 1991 [50] followed by more extensions in 2001 [51], [52], with [29] giving an overview of the literature at that time.”
Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning (2018, methodology): “, 2016), or by prescribing an optimization algorithm or update rule for the learner (Bengio et al., 1990; Schmidhuber, 1992; Younger et al., 2001; Andrychowicz et al., 2016; Li and Malik, 2016; Ravi and Larochelle, 2018).”
Evolved Policy Gradients (2018, background): “Finally, the work of (11; 45; 1; 14) studies meta-learning over the optimization process in which meta-learner makes explicit updates to a parametrized model in supervised settings.”
Learning to Adapt: Meta-Learning for Model-Based Control (2018, methodology): “hrough approaches such as deciding the learner’s architecture (Baker et al., 2016), or by prescribing an optimization algorithm or update rule for the learner (Bengio et al., 1990; Schmidhuber, 1992; Younger et al., 2001; Andrychowicz et al., 2016; Li and Malik, 2016; Ravi and Larochelle, 2018). Ano”
Adaptive behavior with fixed weights in RNN: an overview (2002, methodology): “Here we discuss two class I meta-learning tasks described in [ 5 ] and propose their solutions with RMLP.”