Statistical Learning
UNION: Unsupervised 3D Object Detectionusing Object Appearance-based Pseudo-Classes
The code is available at github.com/TedLentsch/UNION. Figure 1: UNION discovers mobile objects (e.g.
Alleviating the Semantic Gap for Generalized fMRI-to-Image Reconstruction Tao Fang
CLIP model to map the training data to a compact feature representation, which essentially extends the sparse semantics of training data to dense ones, thus alleviating the semantic gap of the instances nearby known concepts (i.e., inside the
TikhonovRegularizationisOptimalTransportRobust underMartingaleConstraints
Regularization is an important tool in machine learning which is used in, for instance, reducing overfitting[23].