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between #supervised and #unsupervised learning approaches there is a whole gray matter where the #robustml resides
Some say - #DeepLearning We say - not-so-robust-learning :) More evidence is piling. #robustML
Natural Adversarial Examples are real-world and unmodified examples which cause classifiers to be consistently confused. The new dataset has 7,500 images, which we personally labeled over several months. Paper: arxiv.org/abs/1907.07174 Dataset and code: github.com/hendrycks/natu…
"Machine learning will always produce an answer" is an uneducated statement by academics eager to one up on others. It is true in absence of systems around ML. #robustML takes care of all this
Reliability is one of the metric of #RobustML MachieLearning is a two step process - Training step, Inferencing (production) step. Reliability metrics are required in both. A modern compute/software infrastructure will play a very important role in addition to data/methods.
Blockchain technology can add to the robustness of ML technology #robustML - #traceability
CNNs are designed to process and classify images for computer vision and many other tasks. But slight modifications — say, a few darker pixels within an image — may cause a CNN to produce a drastically different classification. NOT a #robustML
Enabling machines to process data from various sources to identify fraud or detect defects ("Anomaly Detection Automation") was the most appealing idea to lenders, followed by "Borrower Default Risk Assessment."
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