3,705 research outputs found
Meta-Learning by the Baldwin Effect
The scope of the Baldwin effect was recently called into question by two
papers that closely examined the seminal work of Hinton and Nowlan. To this
date there has been no demonstration of its necessity in empirically
challenging tasks. Here we show that the Baldwin effect is capable of evolving
few-shot supervised and reinforcement learning mechanisms, by shaping the
hyperparameters and the initial parameters of deep learning algorithms.
Furthermore it can genetically accommodate strong learning biases on the same
set of problems as a recent machine learning algorithm called MAML "Model
Agnostic Meta-Learning" which uses second-order gradients instead of evolution
to learn a set of reference parameters (initial weights) that can allow rapid
adaptation to tasks sampled from a distribution. Whilst in simple cases MAML is
more data efficient than the Baldwin effect, the Baldwin effect is more general
in that it does not require gradients to be backpropagated to the reference
parameters or hyperparameters, and permits effectively any number of gradient
updates in the inner loop. The Baldwin effect learns strong learning dependent
biases, rather than purely genetically accommodating fixed behaviours in a
learning independent manner
Neural network-based colonoscopic diagnosis using on-line learning and differential evolution
In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line back-propagation (BP) is proposed and used to seed an on-line evolution process that applies a differential evolution (DE) strategy to (re-) adapt the neural network to modified environmental conditions. Our approach looks at on-line training from the perspective of tracking the changing location of an approximate solution of a pattern-based, and thus, dynamically changing, error function. The proposed hybrid strategy is compared with other standard training methods that have traditionally been used for training neural networks off-line. Results in interpreting colonoscopy images and frames of video sequences are promising and suggest that networks trained with this strategy detect malignant regions of interest with accuracy
Natural Evolution Strategies as a Black Box Estimator for Stochastic Variational Inference
Stochastic variational inference and its derivatives in the form of
variational autoencoders enjoy the ability to perform Bayesian inference on
large datasets in an efficient manner. However, performing inference with a VAE
requires a certain design choice (i.e. reparameterization trick) to allow
unbiased and low variance gradient estimation, restricting the types of models
that can be created. To overcome this challenge, an alternative estimator based
on natural evolution strategies is proposed. This estimator does not make
assumptions about the kind of distributions used, allowing for the creation of
models that would otherwise not have been possible under the VAE framework
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