Abstract: An apparatus, method, program product, and system are disclosed for evicting pages from memory using a neural network. One embodiment of a method for evicting pages from memory using a neural network includes determining state information related to evicting pages from memory. The state information may be determined by a dedicated hardware snooping device that snoops a system bus for the state information. The method includes determining an identifier for a page in memory to be evicted using a neural network. The neural network performs machine learning operations on the state information to identify the page in memory to be evicted. The method includes locating the identified page in memory using the identifier determined by the neural network and evicting the identified page from memory.

Abstract: An apparatus, method, program product, and system are disclosed for evicting pages from memory using a neural network. A state module determines state information related to evicting pages from memory. The state information may be determined by a dedicated hardware snooping device that snoops a system bus for the state information. A learning module determines an identifier for a page in memory to be evicted using a neural network. The neural network may perform machine learning operations on the state information to identify the page in memory to be evicted. An eviction module locates the identified page in memory using the identifier determined by the neural network and evicts the identified page from memory.

Abstract: An approach is provided in which a system analyzes a first subject in a first image taken at a first point in time against a second subject in a second image taken at a second point in time. The first image and the second image are captured at a venue. The system determines, based on a time duration between the first point in time and the second point, a probability that the first subject is at the venue at the second point in time. Next, the system computes, based on the probability, a relevance score that quantifies a relationship between the first subject in the first image and the second subject in the second image. Finally, the system assigns the second image to a first image file corresponding to the first image based on the relevance score reaching a relevance score threshold.

Abstract: An apparatus, method, program product, and system are disclosed for evicting pages from memory using a neural network. One embodiment of a method for evicting pages from memory using a neural network includes determining state information related to evicting pages from memory. The state information may be determined by a dedicated hardware snooping device that snoops a system bus for the state information. The method includes determining an identifier for a page in memory to be evicted using a neural network. The neural network performs machine learning operations on the state information to identify the page in memory to be evicted. The method includes locating the identified page in memory using the identifier determined by the neural network and evicting the identified page from memory.

Abstract: An apparatus, method, program product, and system are disclosed for evicting pages from memory using a neural network. A state module determines state information related to evicting pages from memory. The state information may be determined by a dedicated hardware snooping device that snoops a system bus for the state information. A learning module determines an identifier for a page in memory to be evicted using a neural network. The neural network may perform machine learning operations on the state information to identify the page in memory to be evicted. An eviction module locates the identified page in memory using the identifier determined by the neural network and evicts the identified page from memory.

Abstract: The embodiments relate to classifying and grouping images. Primary images and secondary images are created and stored. A preliminary relationship between the primary and secondary images is established. The primary image is compared with the secondary image to produce a relevance score. The relevance score is converted to a secondary image assignment, which includes selectively assigning the secondary image to a first group based on the relevance score.

Abstract: An approach is provided in which a system analyzes a first subject in a first image taken at a first point in time against a second subject in a second image taken at a second point in time. The first image and the second image are captured at a venue. The system determines, based on a time duration between the first point in time and the second point, a probability that the first subject is at the venue at the second point in time. Next, the system computes, based on the probability, a relevance score that quantifies a relationship between the first subject in the first image and the second subject in the second image. Finally, the system assigns the second image to a first image file corresponding to the first image based on the relevance score reaching a relevance score threshold.

Abstract: The embodiments relate to classifying and grouping images. Primary images and secondary images are created and stored. A preliminary relationship between the primary and secondary images is established. The primary image is compared with the secondary image to produce a relevance score. The relevance score is converted to a secondary image assignment, which includes selectively assigning the secondary image to a first group based on the relevance score.