A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints such as bit balance and code orthogonality, it is not uncommon for existing models to employ a large number (>4) of losses.

We show that these conditions are closely connected: all sufficiently high-entropy distributions are instantaneously realizable at anyhistory of selected items.

The datasets contain numerous grammatical and orthographic errors, poor pronunciation, limited vocabulary, and the content lacks cultural relevance to the language community.

The vital part of every learning-based algorithm is an optimization procedure, e.g., Stochastic Gradient Descent.

Persistent homology hasbecome animportant tool forextracting geometric and topological features from data, whose multi-scale features are summarized in a persistencediagram.