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Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of prompt engineering in generative AI; One-shot learning (computer vision)
MLIR (Multi-Level Intermediate Representation) is a unifying software framework for compiler development. [1] MLIR can make optimal use of a variety of computing platforms such as central processing units (CPUs), graphics processing units (GPUs), data processing units (DPUs), Tensor Processing Units (TPUs), field-programmable gate arrays (FPGAs), artificial intelligence (AI) application ...
One-shot learning is an object categorization problem, found mostly in computer vision. Whereas most machine learning -based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.
CLIP can perform zero-shot image classification tasks. This is achieved by prompting the text encoder with class names and selecting the class whose embedding is closest to the image embedding. For example, to classify an image, they compared the embedding of the image with the embedding of the text "A photo of a {class}.", and the {class} that ...
'Less than one'-shot learning - An extreme few-shot learning problem setting where a learner must recognize more object categories than the number of examples it is shown. This is different from both one-shot learning and zero-shot learning. One way to achieve 'less than one'-shot learning is to label a small number of examples using soft ...
High-level synthesis (HLS), sometimes referred to as C synthesis, electronic system-level (ESL) synthesis, algorithmic synthesis, or behavioral synthesis, is an automated design process that takes an abstract behavioral specification of a digital system and finds a register-transfer level structure that realizes the given behavior.
Compilers: Principles, Techniques, and Tools [1] is a computer science textbook by Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman about compiler construction for programming languages. First published in 1986, it is widely regarded as the classic definitive compiler technology text.
Consideration: Syntax, implementation, and other factors are considered. Languages like Python interpret code at runtime, whereas languages like C++ follow an approach of basing its compiler off of C's compiler. [11] Create an implementation: A first implementation is written. Compilers will convert to other formats, usually ending up as low ...