The accuracy of existing machine learning models, software technologies, and computers are improved by using one or more machine learning models to predict a type of data that one or more numerical characters and/or one or more natural language word characters of a document correspond to. For instance, a Question Answering systems can be used to predict that a particular number value corresponds to a date, a billing amount, a page number, or the like.

A system and method of combining and improving sets of diverse prescriptors for Evolutionary Surrogate-assisted Prescription (ESP) model is described. The prescriptors are distilled into neural networks and evolved further using ESP. The system and method can handle diverse sets of prescriptors in that it makes no assumptions about the form of the input (i.e., contexts) of the initial prescriptors; it relies only on the prescriptions made in order to distill each prescriptor to a neural network with a fixed form. The resulting set of high performing prescriptors provides a practical way for ESP to incorporate external human and machine knowledge and generate more accurate and fitting set of solutions.

Disclosed herein include systems, devices, and methods for detecting embryo polarization from a 2D image generated from a 3D image of an embryo that is not fluorescently labeled using a convolutional neural network (CNN), e.g., deep CNN.

A method of generating a simulation model based on simulation data and measurement data of a target includes classifying weight parameters, included in a pre-learning model learned based on the simulation data, as a first weight group and a second weight group based on a degree of significance, retraining the first weight group of the pre-learning model based on the simulation data, and training the second weight group of a transfer learning model based on the measurement data, wherein the transfer learning model includes the first weight group of the pre-learning model retrained based on the simulation data.

Embodiments disclosed herein include a method, an electronic device, and a computer program product for data processing. The method includes determining a first set of feature vectors representing samples in a data set. The method also includes generating a second set of feature vectors by performing a first transformation on the first set of feature vectors, wherein distribution skewness of the second set of feature vectors in a feature space is smaller than that of the first set of feature vectors. The method also includes generating a third set of feature vectors by performing a second transformation on the second set of feature vectors, wherein the third set of feature vectors and the second set of feature vectors have different distances between vectors. The method also includes selecting target samples as representatives from the samples based on a distribution of the third set of feature vectors in the feature space.

The present disclosure describes techniques for identifying music attributes. The described techniques comprises receiving audio data of a piece of music; determining at least one attribute of the piece of music based on the audio data of the piece of music using a model; the model comprising a convolutional neural network and a transformer; the model being pre-trained using training data, wherein the training data comprise labelled data associated with a first plurality of music samples and unlabelled data associated with a second plurality of music samples, the labelled data comprise audio data of the first plurality of music samples and label information indicative of attributes of the first plurality of music samples, and the unlabelled data comprise audio data of the second plurality of music samples.

Systems, methods, and protocols for developing invasive brain computer interface (iBCI) decoders non-invasively by using emulated brain data are provided. A human operator can interact in real-time with control algorithms designed for iBCI. An operator can provide input to one or more computer models (e.g., via body gestures), and this process can generate emulated brain signals that would otherwise require invasive brain electrodes to obtain.

The present disclosure discloses a method and system for training a neural network for determining severity, and more particularly, a method and system which may effectively learn a neural network performing patch unit severity diagnosis using a pathological slide image to which a severity indication (label) is given.

Embodiments of the present disclosure relate to a model optimization method, an electronic device, and a computer program product. This method includes: determining an initial learning rate combination for a deep learning model, wherein the initial learning rate combination includes a plurality of learning rates, each learning rate being determined for one of a plurality of layers of the deep learning model, and the plurality of learning rates including static learning rates and dynamic learning rates; and adjusting the initial learning rate combination to obtain a target learning rate combination, wherein an accuracy rate achieved when the target learning rate combination is used to train the deep learning model is higher than or equal to a first threshold accuracy rate. With the technical solution of the present disclosure, a deep learning model can be optimized by setting learning rates for each layer of the deep learning model.

Some disclosed systems are configured to obtain a knowledge module configured to receive one or more knowledge inputs corresponding to one or more different modalities and generate a set of knowledge embeddings to be integrated with a set of multi-modal embeddings generated by a multi-modal main model. The systems receive a knowledge input at the knowledge module, identify a knowledge type associated with the knowledge input, and extract a knowledge unit from the knowledge input. The systems select a representation model that corresponds to the knowledge type and select a grounding type configured to ground the at least one knowledge unit into the representation model. The systems then ground the knowledge unit into the representation model according to the grounding type.