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  2. Saliency map - Wikipedia

    en.wikipedia.org/wiki/Saliency_map

    In computer vision, a saliency map is an image that highlights either the region on which people's eyes focus first or the most relevant regions for machine learning models. [1] The goal of a saliency map is to reflect the degree of importance of a pixel to the human visual system or an otherwise opaque ML model.

  3. Process map - Wikipedia

    en.wikipedia.org/wiki/Process_map

    Process map is a global-system process model that is used to outline the processes that make up the business system and how they interact with each other. Process map shows the processes as objects , which means it is a static and non-algorithmic view of the processes.

  4. Business process mapping - Wikipedia

    en.wikipedia.org/wiki/Business_process_mapping

    Business process mapping, also known as process charting, has become much more prevalent and understood in the business world in recent years. Process maps can be used in every section of life or business. The Major Steps of Process Improvement using Process Mapping Process identification - identify objectives, scope, players and work areas.

  5. What is the DALL-E mini AI art generator that’s taking over ...

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  6. Instruction pipelining - Wikipedia

    en.wikipedia.org/wiki/Instruction_pipelining

    In computer engineering, instruction pipelining is a technique for implementing instruction-level parallelism within a single processor. Pipelining attempts to keep every part of the processor busy with some instruction by dividing incoming instructions into a series of sequential steps (the eponymous "pipeline") performed by different processor units with different parts of instructions ...

  7. Instruction scheduling - Wikipedia

    en.wikipedia.org/wiki/Instruction_scheduling

    Avoid illegal or semantically ambiguous operations (typically involving subtle instruction pipeline timing issues or non-interlocked resources). The pipeline stalls can be caused by structural hazards (processor resource limit), data hazards (output of one instruction needed by another instruction) and control hazards (branching).

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  9. DALL-E - Wikipedia

    en.wikipedia.org/wiki/DALL-E

    DALL-E, DALL-E 2, and DALL-E 3 (stylised DALL·E, and pronounced DOLL-E) are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The first version of DALL-E was announced in January 2021. In the following year, its successor DALL-E 2 was released.