Generalization is a crucial aspect of neural networks, as it refers to the model's ability to learn from given data and apply the learned information to new, unseen data[2]. In this blog, we will discuss generalization in neural networks and explain the research paper "Slicing Mutual Information Generalization Bounds for Neural Networks"[1].
Generalization in Neural Networks
When training a neural network, it is essential to ensure that the model performs well on data it has not trained on. If the neural network generalizes well to the given data, it means that it can effectively apply the learned information to new, unseen data[2]. However, achieving good generalization is often challenging, and various techniques have been developed to prevent overfitting and improve the model's ability to generalize[10].
Slicing Mutual Information Research Paper
The research paper "Slicing Mutual Information Generalization Bounds for Neural Networks" focuses on the generalization capacity of algorithms that slice the parameter space, i.e., train on a random lower-dimensional subspace[1]. The authors derive information-theoretic bounds on the generalization error in this regime and discuss an intriguing connection to the k-Sliced Mutual Information, an alternative measure of statistical dependence that scales well with dimension[1].
The paper addresses the limitations of traditional mutual information (MI) bounds for modern machine learning applications, such as deep learning, where evaluating MI is difficult in high-dimensional settings[1]. Motivated by recent reports of significant low-loss compressibility of neural networks, the authors study the generalization capacity of algorithms that slice the parameter space[1].
The computational and statistical benefits of this approach allow the authors to empirically estimate the input-output information of these neural networks and compute their information-theoretic generalization bounds, a task that was previously out of reach[1].
Conclusion
Understanding and improving generalization in neural networks is crucial for developing effective models that can perform well on new, unseen data. The research paper "Slicing Mutual Information Generalization Bounds for Neural Networks" provides valuable insights into the generalization capacity of algorithms that slice the parameter space and introduces an alternative measure of statistical dependence, the k-Sliced Mutual Information, which scales well with dimension[1]. By exploring these concepts, researchers and practitioners can develop better neural network models that generalize well to new data and ultimately improve the performance of machine learning applications.
Citations:
[1] https://research.ibm.com/publications/slicing-mutual-information-generalization-bounds-for-neural-networks
[2] https://www.kdnuggets.com/2019/11/generalization-neural-networks.html
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[5] https://towardsdatascience.com/neural-networks-dont-generalize-the-way-you-think-they-do-de520bed2053
[6] https://openreview.net/pdf?id=cbLcwK3SZi
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[9] https://www.biomedcentral.com/getpublished/writing-resources/blogs-for-authors
[10] https://www.cs.toronto.edu/~lczhang/321/notes/notes09.pdf
[11] https://proceedings.mlr.press/v206/wongso23a/wongso23a.pdf
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[17] https://blog.paperspace.com/constructing-neural-networks-from-scratch/
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[19] https://arxiv.org/pdf/2302.02432.pdf
[20] https://developers.google.com/machine-learning/crash-course/generalization/video-lecture
[21] https://blog.feedspot.com/neural_network_blogs/
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[23] https://www.mdpi.com/1099-4300/25/7/1063
[24] https://www.frontiersin.org/articles/10.3389/frai.2022.890016
[25] https://www.edureka.co/blog/what-is-a-neural-network/