Summary Self-Expanding Neural Networks A Natural Gradient Approach arxiv.org
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SENN is a method that solves the problem of determining neural network size by starting small and expanding as necessary during training.
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Key Points
- Self-Expanding Neural Networks (SENN) address the challenge of choosing the appropriate architecture size for a neural network.
- SENN proposes starting with a small architecture and expanding it as necessary during training.
- Two methods for expanding the network are width expansion and inserting a new layer.
- The addition of neurons or layers in SENN is determined based on a fractional increase in the squared norm of the gradient.
- The maximum number of successive additions in a neural network is bounded.
Summaries
25 word summary
Self-Expanding Neural Networks (SENN) address the challenge of determining neural network architecture size by starting with a small architecture and expanding as needed during training.
42 word summary
This paper introduces Self-Expanding Neural Networks (SENN) as a solution to the challenge of determining the appropriate architecture size for a neural network. The authors propose starting with a small architecture and expanding it as needed during training. They present two methods
496 word summary
This paper introduces Self-Expanding Neural Networks (SENN), which address the challenge of choosing the appropriate architecture size for a neural network. Instead of starting with a large architecture, the authors propose starting with a small architecture and expanding it as necessary during
In this document, the authors present Self-Expanding Neural Networks (SENN), which address the problem of adding nodes to neural networks during training. They prove that the number of neurons added simultaneously in SENN is bounded and introduce a computationally efficient
This summary discusses the concept of Self-Expanding Neural Networks (SENN) and how to add more capacity to a neural network without changing the overall function. The authors propose two methods for expanding the network: width expansion and inserting a new layer.
SENN (Self-Expanding Neural Networks) determines when to add more capacity based on a fractional increase in the squared norm of the gradient. A new neuron or layer is added if it provides a sufficient increase in the norm. The initial value of
The summary of the text excerpt is as follows:
The paper discusses self-expanding neural networks (SENN) and proposes a natural gradient approach for training them. The authors argue that a network is considered converged when the changes in loss become small, and
The excerpt discusses Self-Expanding Neural Networks (SENN) and their application in regression and classification tasks. It introduces the trace formula for SENN and the gradient for W. The "correlation coefficient" of new activations with residual gradients is a
The study focuses on self-expanding neural networks (SENNs) and their ability to adapt their size based on the amount of information in a dataset. The researchers trained SENNs on class-balanced subsets of the MNIST dataset and found that the
The excerpted text includes a list of references to various research papers and conference proceedings related to neural networks and machine learning. The references cover topics such as deep convolutional neural networks, backpropagation, optimization methods, activation functions, and the use of
The summary of the text excerpt is as follows:
The excerpt includes references to several papers related to neural networks and their expansion. The authors prove Theorem 1, which states that the maximum number of successive additions in a neural network is bounded. They
The excerpt discusses the residual part of v p not predicted by v c and provides a proof for it. It also presents a block LDU decomposition and decomposes A ?1. The desired result is then obtained by substitution into v T A ?1
The stopping criterion for parameter expansions requires a reduction in loss of at least 12%. The maximum possible reduction in loss is 21%. The total number of added neurons is bounded by a certain value. If the true hessian of the loss is a
In the visualization experiments, a threshold value of 2 is used, while for the image classification experiments, threshold values of 1.007 and 1.03 are used for the whole dataset and variable subset experiments respectively. Higher thresholds result in longer