7,754 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
A Framework For Intelligent Multi Agent System Based Neural Network Classification Model
TIntelligent multi agent systems have great potentials to use in different
purposes and research areas. One of the important issues to apply intelligent
multi agent systems in real world and virtual environment is to develop a
framework that support machine learning model to reflect the whole complexity
of the real world. In this paper, we proposed a framework of intelligent agent
based neural network classification model to solve the problem of gap between
two applicable flows of intelligent multi agent technology and learning model
from real environment. We consider the new Supervised Multilayers Feed Forward
Neural Network (SMFFNN) model as an intelligent classification for learning
model in the framework. The framework earns the information from the respective
environment and its behavior can be recognized by the weights. Therefore, the
SMFFNN model that lies in the framework will give more benefits in finding the
suitable information and the real weights from the environment which result for
better recognition. The framework is applicable to different domains
successfully and for the potential case study, the clinical organization and
its domain is considered for the proposed frameworkComment: 7 pages IEEE format, International Journal of Computer Science and
Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423,
http://sites.google.com/site/ijcsis
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
We provide theoretical investigation of curriculum learning in the context of
stochastic gradient descent when optimizing the convex linear regression loss.
We prove that the rate of convergence of an ideal curriculum learning method is
monotonically increasing with the difficulty of the examples. Moreover, among
all equally difficult points, convergence is faster when using points which
incur higher loss with respect to the current hypothesis. We then analyze
curriculum learning in the context of training a CNN. We describe a method
which infers the curriculum by way of transfer learning from another network,
pre-trained on a different task. While this approach can only approximate the
ideal curriculum, we observe empirically similar behavior to the one predicted
by the theory, namely, a significant boost in convergence speed at the
beginning of training. When the task is made more difficult, improvement in
generalization performance is also observed. Finally, curriculum learning
exhibits robustness against unfavorable conditions such as excessive
regularization.Comment: ICML 201
Distributed representations accelerate evolution of adaptive behaviours
Animals with rudimentary innate abilities require substantial learning to transform those abilities into useful skills, where a skill can be considered as a set of sensory - motor associations. Using linear neural network models, it is proved that if skills are stored as distributed representations, then within- lifetime learning of part of a skill can induce automatic learning of the remaining parts of that skill. More importantly, it is shown that this " free- lunch'' learning ( FLL) is responsible for accelerated evolution of skills, when compared with networks which either 1) cannot benefit from FLL or 2) cannot learn. Specifically, it is shown that FLL accelerates the appearance of adaptive behaviour, both in its innate form and as FLL- induced behaviour, and that FLL can accelerate the rate at which learned behaviours become innate
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