BIG-bench Machine Learning
2295 papers with code • 1 benchmarks • 1 datasets
This branch include most common machine learning fundamental algorithms.
UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction.
We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70, 000 fashion products from 10 categories, with 7, 000 images per category.
PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation.
We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.
It involves predicting the co-ordinates of the FKPs, e. g. nose tip, center of eyes, etc, for a given face.