This application relates to the field of artificial intelligence technologies, and discloses a machine learning model search method, a related apparatus, and a device. In the method, before model search and quantization, a plurality of single bit models are generated based on a to-be-quantized model, and evaluation parameters of layer structures in the plurality of single bit models are obtained. Further, after a candidate model selected from a candidate set is trained and tested, to obtain a target model, a quantization weight of each layer structure in the target model may be determined based on a network structure of the target model and evaluation parameters of all layer structures in the target model, a layer structure with a maximum quantization weight in the target model is quantized, and a model obtained through quantization is added to the candidate set.

An information processing device according to the present disclosure includes: an acquisition unit that acquires a model having a structure of a neural network and input information input to the model; and a generation unit that generates basis information indicating a basis for an output of the model after the input information is input to the model based on state information indicating a state of the model after the input of the input information to the model.

A model compression device includes a compression unit and a determination unit. The compression unit is configured to create a compression model arrived at by compressing a first prediction model created by machine learning. The determination unit is configured to determine whether or not a second prediction model created by re-learning the compression model can be further compressed on the basis of an index related to the performance of the second prediction model.

An artificial intelligence refrigerator according to one embodiment of the present disclosure can comprise: an inner door; an outer door having a transparent display on the front surface thereof; one or more cameras provided to the outer door; a sensor for sensing the opening/closing or opening angle of the outer door; and one or more processors for determining whether the opening angle of the outer door is a preset angle when closing of the outer door is sensed, photographing the inner door through the one or more cameras when the opening angle of the outer door is the preset angle, acquiring, on the basis of a captured image, the storage state of food stored in the inner door, and displaying food management information on the transparent display on the basis of the acquired storage state.

A class label of a three-dimensional point cloud can be identified with high performance. The key point choice unit 22 extracts a key point cloud 35 including three-dimensional points efficiently representing features of an object and a non-key point cloud 37. A inference unit 24 takes, as representative points, a plurality of points selected by down-sampling from each of the key point cloud 35 and the non-key point cloud 37, extracts, with respect to each of the representative points, a feature of each representative point from coordinates and the feature of the representative point and coordinates and features of neighboring points positioned near the representative point. The inference unit 24 extracts features of a plurality of new representative points from the coordinates and the features of the plurality of representative points, coordinates and features of a plurality of three-dimensional points before sampling which are the new representative points, and coordinates and features of neighboring points positioned near the new representative points. The inference unit 24 derives a class label from the coordinates and features of the plurality of representative points, or the coordinates and features of the plurality of new representative points, and outputs the class label.

A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.

Videos captured in low light conditions can be processed in order to identify an activity being performed in the video. The processing may use both the video and audio streams for identifying the activity in the low light video. The video portion is processed to generate a darkness-aware feature which may be used to modulate the features generated from the audio and video features. The audio features may be used to generate a video attention feature and the video features may be used to generate an audio attention feature. The audio and video attention features may also be used in modulating the audio video features. The modulated audio and video features may be used to predict an activity occurring in the video.

The present disclosure relates to communication technology, and provides a resource configuration method and apparatus, a device, and a storage medium. The method includes: receiving first resource configuration information from a network device. The first resource configuration information includes n resource configuration combinations each including first-type resource configuration information and second-type resource configuration information, where n is positive integer. The first-type resource configuration information indicates a radio resource configuration, and the second-type resource configuration information indicates an Artificial Intelligence (AI) resource configuration. The combined configuration solution according to the embodiments of the present disclosure can reduce the number of times the network device performs resource scheduling, and reduce the processing overhead of the network device.

A network quantization method is a network quantization method of quantizing a neural network, and includes a database construction step of constructing a statistical information database on tensors that are handled by neural network, a parameter generation step of generating quantized parameter sets by quantizing values included in each tensor in accordance with the statistical information database and the neural network, and a network construction step of constructing a quantized network by quantizing the neural network with use of the quantized parameter sets. The parameter generation step includes a quantization-type determination step of determining a quantization type for each of a plurality of layers that make up the neural network.

Provided are: a selection information acquiring unit to acquire selection information for identifying a target model to be generated from among a plurality of generable neural network models; a model identification unit to identify the target model on the basis of the selection information acquired by the selection information acquiring unit; a weight acquiring unit to acquire a weight of the target model identified by the model identification unit; and a model generation unit to generate the target model identified by the model identification unit on the basis of the weight acquired by the weight acquiring unit and a weight map in which structure information on a structure of each of the plurality of neural network models and information for mapping a weight in the structure are defined.