Deep Learning
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization Chaoqi Chen 1 Luyao T ang
Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one.
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
We apply gradient descent to a general class of objective functions for the reconstruction.
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent
We apply gradient descent to a general class of objective functions for the reconstruction.
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If the paper is accepted we will discuss the differences with our model in the related work section.
Is one annotation enough?
High-quality data is necessary for modern machine learning. However, the acquisition of such data is difficult due to noisy and ambiguous annotations of humans.
