The distance between the weights of the neural network is meaningful

• URL: http://arxiv.org/abs/2102.00396v1
• Date: Sun, 31 Jan 2021 06:44:49 GMT
• Title: The distance between the weights of the neural network is meaningful
• Authors: Liqun Yang, Yijun Yang, Yao Wang, Zhenyu Yang, Wei Zeng
• Abstract summary: In the application of neural networks, we need to select a suitable model based on the problem complexity and the dataset scale. This paper proves that the distance between the neural network weights in different training stages can be used to estimate the information accumulated by the network in the training process directly.
• Score: 9.329400348695435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the application of neural networks, we need to select a suitable model based on the problem complexity and the dataset scale. To analyze the network's capacity, quantifying the information learned by the network is necessary. This paper proves that the distance between the neural network weights in different training stages can be used to estimate the information accumulated by the network in the training process directly. The experiment results verify the utility of this method. An application of this method related to the label corruption is shown at the end.

Related papers

• Residual Random Neural Networks [0.0]
  Single-layer feedforward neural network with random weights is a recurring motif in the neural networks literature. We show that one can obtain good classification results even if the number of hidden neurons has the same order of magnitude as the dimensionality of the data samples.
  arXiv  Detail & Related papers  (2024-10-25T22:00:11Z)
• Opening the Black Box: predicting the trainability of deep neural networks with reconstruction entropy [0.0]
  We present a method for predicting the trainable regime in parameter space for deep feedforward neural networks. For both the MNIST and CIFAR10 datasets, we show that a single epoch of training is sufficient to predict the trainability of the deep feedforward network.
  arXiv  Detail & Related papers  (2024-06-13T18:00:05Z)
• Addressing caveats of neural persistence with deep graph persistence [54.424983583720675]
  We find that the variance of network weights and spatial concentration of large weights are the main factors that impact neural persistence. We propose an extension of the filtration underlying neural persistence to the whole neural network instead of single layers. This yields our deep graph persistence measure, which implicitly incorporates persistent paths through the network and alleviates variance-related issues.
  arXiv  Detail & Related papers  (2023-07-20T13:34:11Z)
• Adaptive Neural Networks Using Residual Fitting [2.546014024559691]
  We present a network-growth method that searches for explainable error in the network's residuals and grows the network if sufficient error is detected. Within these tasks, the growing network can often achieve better performance than small networks that do not grow.
  arXiv  Detail & Related papers  (2023-01-13T19:52:30Z)
• Decomposing neural networks as mappings of correlation functions [57.52754806616669]
  We study the mapping between probability distributions implemented by a deep feed-forward network. We identify essential statistics in the data, as well as different information representations that can be used by neural networks.
  arXiv  Detail & Related papers  (2022-02-10T09:30:31Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Dive into Layers: Neural Network Capacity Bounding using Algebraic Geometry [55.57953219617467]
  We show that the learnability of a neural network is directly related to its size. We use Betti numbers to measure the topological geometric complexity of input data and the neural network. We perform the experiments on a real-world dataset MNIST and the results verify our analysis and conclusion.
  arXiv  Detail & Related papers  (2021-09-03T11:45:51Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• MLDS: A Dataset for Weight-Space Analysis of Neural Networks [0.0]
  We present MLDS, a new dataset consisting of thousands of trained neural networks with carefully controlled parameters. This dataset enables new insights into both model-to-model and model-to-training-data relationships.
  arXiv  Detail & Related papers  (2021-04-21T14:24:26Z)
• Learning Neural Network Subspaces [74.44457651546728]
  Recent observations have advanced our understanding of the neural network optimization landscape. With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks. With a similar computational cost as training one model, we learn lines, curves, and simplexes of high-accuracy neural networks.
  arXiv  Detail & Related papers  (2021-02-20T23:26:58Z)
• Backprojection for Training Feedforward Neural Networks in the Input and Feature Spaces [12.323996999894002]
  We propose a new algorithm for training feedforward neural networks which is fairly faster than backpropagation. The proposed algorithm can be used for both input and feature spaces, named as backprojection and kernel backprojection, respectively.
  arXiv  Detail & Related papers  (2020-04-05T20:53:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.