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Sparse dictionary learning (also known as sparse coding or SDL) is a representation learning method which aims to find a sparse representation of the input data in the form of a linear combination of basic elements as well as those basic elements themselves. These elements are called atoms, and they compose a dictionary.
find the value (if any) that is bound to a given key. The argument to this operation is the key, and the value is returned from the operation. If no value is found, some lookup functions raise an exception, while others return a default value (such as zero, null, or a specific value passed to the constructor).
The value is usually chosen to be a quiet NaN with an all-zero payload and an arbitrarily-defined sign bit. On RISC-V, most floating-point operations only ever generate the canonical NaN, even if a NaN is given as the operand (the payload is not propagated). [18] [b] ARM can enable a "default NaN" mode for this behavior. [20] WebAssembly has ...
With this terminology, the above definition of the lexicographical order becomes more concise: Given a partially or totally ordered set A, and two words a and b over A such that b is non-empty, then one has a < b under lexicographical order, if at least one of the following conditions is satisfied: a is a prefix of b
The bfloat16 (brain floating point) [1] [2] floating-point format is a computer number format occupying 16 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point.
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Quantifiers, which include one, many, some and all, are used to specify the noun in terms of quantity or number. The noun must agree with the number of the determiner, e.g. one man (sg.) but all men (pl.). Determiners are the first constituents in a noun phrase. [194]