An information processing apparatus (1) includes a learning unit (32), a calculation unit (33), and a presentation unit (34). The learning unit (32) learns the first model based on predetermined new data acquired from a terminal device (100) possessed by the user and the second model based on joined data obtained by joining shared data stored in advance in the storage unit (4) as additional data with the new data. The calculation unit (33) calculates the improvement degree indicating the degree of improvement in the output precision of the second model to the output of the first model. The presentation unit (34) generates predetermined presentation information based on the improvement degree calculated by the calculation unit (33).

Examples of methods for predicting object deformations are described herein. In some examples, a method includes predicting a point cloud. In some examples, the predicted point cloud indicates a predicted object deformation. In some examples, the point cloud may be predicted using a machine learning model and edges determined from an input point cloud.

Examples of neural networks trained for multiple tasks are described herein. In some examples, a method may include determining a feature vector using a first portion of a neural network. In some examples, the neural network is trained for multiple tasks. Some examples of the method may include transmitting the feature vector to a remote device. In some examples, the remote device is to perform one of the multiple tasks using a second portion of the neural network.

Examples of neural networks trained for multiple tasks are described herein. In some examples, a method may include determining a feature vector using a first portion of a neural network. In some examples, the neural network is trained for multiple tasks. Some examples of the method may include transmitting the feature vector to a remote device. In some examples, the remote device is to perform one of the multiple tasks using a second portion of the neural network.

Examples of methods for controlling machine learning model structures are described herein. In some examples, a method includes controlling a machine learning model structure. In some examples, the machine learning model structure may be controlled based on an environmental condition. In some examples, the machine learning model structure may be controlled to control apparatus power consumption associated with a processing load of the machine learning model structure.

Examples of methods for controlling machine learning model structures are described herein. In some examples, a method includes controlling a machine learning model structure. In some examples, the machine learning model structure may be controlled based on an environmental condition. In some examples, the machine learning model structure may be controlled to control apparatus power consumption associated with a processing load of the machine learning model structure.

An initially trained machine learning model is used by an active learning module to generate candidate triples, which are fed into an expert system for verification. As a result, the expert system outputs novel facts that are used for retraining the machine learning model. This approach consolidates expert systems with machine learning through iterations of an active learning loop, by bringing the two paradigms together, which is in general difficult because training of a neural network (machine learning) requires differentiable functions and rules (used by expert systems) tend not to be differentiable. The method and system provide a data augmentation strategy where the expert system acts as an oracle and outputs the novel facts, which provide labels for the candidate triples. The novel facts provide critical information from the oracle that is injected into the machine learning model at the retraining stage, thus allowing to increase its generalization performance.

A system and method for generating libraries of chemical compounds having desired and specific properties by formulating a reaction-based mechanism that may be powered by several algorithms including but not limited to genetic algorithm, expert iteration algorithms, planning methods, reinforcement learning and machine learning algorithms. The system and method may also provide the process steps by which these optimized products S′ may be synthesized from the reactants R1,R2 and further enables a rapid and efficient search of the synthetically accessible chemical space.

A computer-implemented method for predicting properties of a plurality of objects in a vicinity of a vehicle includes multiple steps that can be carried out by computer hardware components. The method includes determining a grid map representation of road-users perception data, with the road-users perception data including tracked perception results and/or untracked sensor intermediate detections. The method also includes determining a grid map representation of static environment data based on data obtained from a perception system and/or a pre-determined map. The method further includes determining the properties of the plurality of objects based on the grid map representation of road-users perception data and the grid map representation of static environment data.

A blood flow field estimation apparatus is provided, including an estimation unit that uses a learned model obtained in advance by performing machine learning to learn a relationship between organ tissue three-dimensional structure data including image data of a plurality of organ cross-sectional images serving as cross-sectional images of an organ and having each pixel provided with two or more bit depths and image position information serving as information indicating a position of an image reflected on each of the organ cross-sectional images in the organ, and a blood flow field in the organ, and estimates the blood flow field in the organ of an estimation target, based on the organ tissue three-dimensional structure data of the organ of the estimation target, and an output unit that outputs an estimation result of the estimation unit.