[Research] Deep Critical Learning (i.e., Deep Robustness) In The Era of Big Data

• Improving-Mean-Absolute-Error-against-CCE

  3 30 0.0 Shell

  Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters

Here are related papers on the fitting and generalization of deep learning: * ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State * Understanding deep learning requires rethinking generalization * A Closer Look at Memorization in Deep Networks * ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks * Blog link: https://xinshaoamoswang.github.io/blogs/2020-06-07-Progressive-self-label-correction/ * Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters * Derivative Manipulation: Example Weighting via Emphasis Density Funtion in the context of DL * Novelty: moving from loss design to derivative design

• DerivativeManipulation

  2 10 10.0 Shell

  In the context of Deep Learning: What is the right way to conduct example weighting? How do you understand loss functions and so-called theorems on them?

Here are related papers on the fitting and generalization of deep learning: * ProSelfLC: Progressive Self Label Correction Towards A Low-Temperature Entropy State * Understanding deep learning requires rethinking generalization * A Closer Look at Memorization in Deep Networks * ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks * Blog link: https://xinshaoamoswang.github.io/blogs/2020-06-07-Progressive-self-label-correction/ * Mean Absolute Error Does Not Treat Examples Equally and Gradient Magnitude’s Variance Matters * Derivative Manipulation: Example Weighting via Emphasis Density Funtion in the context of DL * Novelty: moving from loss design to derivative design

• InfluxDB

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• fitting-random-labels

  1 176 10.0 Python

  Example code for the paper "Understanding deep learning requires rethinking generalization"

Code for https://arxiv.org/abs/1611.03530 found: https://github.com/pluskid/fitting-random-labels

• IDN

  1 32 10.0 Python

  AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

• ProSelfLC

  1 50 10.0 HTML

  Discontinued noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation. [Moved to: https://github.com/XinshaoAmosWang/ProSelfLC-AT]

Code for https://arxiv.org/abs/2005.03788 found: https://github.com/XinshaoAmosWang/ProSelfLC

• SaaSHub

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