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.

An apparatus for super resolution imaging includes a convolutional neural network (104) to receive a low resolution frame (102) and generate a high resolution illuminance component frame. The apparatus also includes a hardware scaler (106) to receive the low resolution frame (102) and generate a second high resolution chrominance component frame. The apparatus further includes a combiner (108) to combine the high resolution illuminance component frame and the high resolution chrominance component frame to generate a high resolution frame (110).

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 method of training a deep learning model, a method of processing a natural language, an electronic device, and a storage medium are provided, which relate to a field of artificial intelligence, in particular to deep learning technology and natural language processing technology. The method includes: inputting first sample data into a first deep learning model to obtain a first output result; training the first deep learning model according to the first output result and a first target output result, the first target output result is obtained by processing the first sample data using a reference deep learning model; inputting second sample data into a second deep learning model to obtain a second output result; and training the second deep learning model according to the second output result and a second target output result, to obtain a trained second deep learning model.

Systems and methods for implementing federated learning engine for integration of vertical and horizontal AI are disclosed herein. A method can include receiving a global model from a central aggregator communicatingly connected with a plurality of user environments, which global model including a plurality of layers. The method can include training a mini model on top of the global model with data gathered within the user environment, uploading the at least a portion of the mini model to the central aggregator, receiving a plurality of mini models, and creating a fusion model based on the received plurality of mini models.

System and method for using and managing artificial intelligence (AI) inference platform (AIP) and/or model orchestrators according to certain embodiments. For example, a method includes receiving sensor data via a data interface of a model orchestrator, the model orchestrator including an indication of a model pipeline, the model pipeline including a plurality of models; loading the plurality of models according to the model pipeline; applying the model pipeline to the received sensor data; receiving a model output from the model pipeline via a model interface of the model orchestrator; and generating an insight based at least in part on the model output.

Machine learning techniques are used, in one embodiment, to mitigate multipath in an L5 GNSS receiver. In one embodiment, training data is generated to provide ground truth data for excess path length (EPL) corrections for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the ground truth data to train a set of one or more neural networks that can produce EPL corrections for pseudorange measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct pseudorange measurements using EPL corrections provided by the trained set of neural networks.

An audio sample including speech and ambient sounds is transmitted to a vehicle computer. Recorded audio is received from the vehicle computer, the recorded audio including the audio sample broadcast by the vehicle computer and recorded by the vehicle computer and recognized speech from the recorded audio. The recognized speech and text of the speech are input to a machine learning program that outputs whether the recognized speech matches the text. When the output from the machine learning program indicates that the recognized speech does not match the text, the recognized speech and the text are included in a training dataset for the machine learning program.