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Improving-Mean-Absolute-Error-against-CCE Alternatives
Similar projects and alternatives to Improving-Mean-Absolute-Error-against-CCE based on common topics and language
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ProSelfLC-AT
noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.
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DerivativeManipulation
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?
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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fitting-random-labels
Example code for the paper "Understanding deep learning requires rethinking generalization"
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ProSelfLC
Discontinued noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation. [Moved to: https://github.com/XinshaoAmosWang/ProSelfLC-AT]
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IDN
AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
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SaaSHub
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Hence, a higher number means a better Improving-Mean-Absolute-Error-against-CCE alternative or higher similarity.
Improving-Mean-Absolute-Error-against-CCE reviews and mentions
Posts with mentions or reviews of Improving-Mean-Absolute-Error-against-CCE.
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[Research] Deep Critical Learning (i.e., Deep Robustness) In The Era of Big Data
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
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[Research] Not all our papers get published, therefore it is enjoyable to see our released papers become a true foundation for other works
Code for https://arxiv.org/abs/1903.12141 found: https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE
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[R] CVPR 2021-Progressive Self Label Correction (ProSelfLC) for Training Robust Deep Neural Networks
https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE#open-reviews-and-discussion
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Stats
Basic Improving-Mean-Absolute-Error-against-CCE repo stats
3
30
0.0
over 3 years ago
XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of Improving-Mean-Absolute-Error-against-CCE is Shell.
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