6 results about "Matrix addition" patented technology

The application is suitable for the technical field of computers, and provides a named entity recognition method, including: acquiring a to-be-recognized text, and converting the to-be-recognized text into a first matrix of n*k dimensions; performing multi-layer convolution layer convolution on the first matrix, wherein the last convolution layer of a convolution kernel in the multi-layer convolution layer has a channel number of m, four convolution operations are performed on the last convolution layer, and four parallel second matrices of n*m dimensions are obtained; performing attention weight self-adaption on three second matrices of the four second matrices to obtain a third matrix of n*m dimensions, performing matrix addition on the third matrix and the remaining one second matrix, and outputting a fourth matrix of n*m dimensions; performing classification on the fourth matrix, and outputting an entity label corresponding to the to-be-recognized text; and outputting a named entity corresponding to the to-be-recognized text according to the entity label. By introducing an attention mechanism in the convolution layer, data redundancy is effectively reduced, the number of model parameters is reduced, and the recognition speed of the named entity recognition model is accelerated.

Aspects of the present invention are directed to computer-implemented techniques for improving the training of artificial neural networks using a reduced precision (e.g., float16) data format. Embodiments of the present invention rescale tensor values prior to performing matrix operations (such as matrix multiplication or matrix addition) to prevent overflow and underflow. To preserve accuracy throughout the performance of the matrix operations, the scale factors are defined using a novel data format to represent tensors, wherein a matrix is represented by the tuple X, where X=(a, v[.]), wherein a is a float scale factor and v[.] are scaled values stored in the float16 format. The value of any element X[i] according to this data format would be equal to a*v[i].

The invention discloses a decision-level multi-model dynamic fusion classification method, which comprises the following steps of: establishing each base model, obtaining a data set containing a sample and a real classification category, predicting through the base model to obtain a predicted classification category, calculating a classification accuracy rate in combination with the real category, and constructing a matrix; converting the prediction category into a one-hot coding form to obtain a voting matrix; after filtering the constructed matrix, calculating the contribution weight of each base model by adopting an approximate ideal solution sorting method; and performing scalar multiplication on the voting matrix and the weight to obtain an effective category voting weight matrix, performing Hadamard product on the voting matrix and the weight element by element, performing matrix addition to generate a voting fusion category two-dimensional matrix of all samples, and finally processing the two-dimensional matrix to obtain a fusion classification category. Aiming at the problem that the performance of the existing fusion classification method depends on the sample data quality, more accurate fusion classification is realized by dynamically selecting the dominant basis model and the category which is good at prediction and dynamically endowing the weight.

The application discloses a decision-level multi-model dynamic fusion classification method, and steps are as follows: establishing each base model and obtaining a data set containing samples and real classification categories, obtaining a predicted classification category through base model prediction, calculating classification accuracy in combination with the real categories and constructing a matrix; converting the predicted classification category into a one-hot encoding form to obtain a voting matrix; after filtering the constructed matrix, the contribution weight of each base model is calculated by using the TOPSIS method; the voting matrix and the weight are multiplied by a scalar to obtain an effective category voting weight matrix, and after element-wise Hadamard product, a two-dimensional matrix of the voting fusion classification category of all samples is generated through matrix addition, and finally the two-dimensional matrix is processed to obtain the fusion classification category. The application aims at the problem that the performance of the existing fusion classification method depends on the sample data quality, dynamically selects the advantage base model and the category which is good at prediction, and dynamically assigns the weight, so that more accurate fusion classification is realized.

The invention relates to a storage and calculation integrated peripheral circuit device based on a 3D VRRAM and a matrix calculation method, belongs to the technical field of memories, and solves the problem that the structure of an existing two-dimensional resistive random access memory array is not suitable for a neural network with a high calculation power demand. The device comprises a matrix multiplier for multiplying an input data matrix by a weight matrix; the input ends of the analog-to-digital converters are connected to the output ends of the column control switches so as to convert the analog product result into a digital product result; the plurality of samplers are connected with the output end of the analog-to-digital converter so as to sample the digital product result; the output ends of the plurality of samplers are connected with the input end of the matrix adder through the matrix adder so as to realize digital product result shifting, then shifting data are accumulated, and an accumulation result is stored in a temporary register; and the updating register adds the accumulation result and the data in the updating register to obtain sum data, and updates the data in the updating register by using the sum data. And high-storage and high-computing-power-density operation is realized so as to be suitable for a neural network.

The application discloses a sintering endpoint prediction method, device, equipment and readable storage medium, obtains to-be-processed data, divides the to-be-processed data into t moments, obtains a feature vector based on the to-be-processed data of the current moment, obtains a factor attention result based on the feature vector and a hidden vector of the previous moment, determines a time attention result, calculates a new feature vector through matrix addition of the feature vector and the time attention result, obtains the hidden vector of the current moment and a prediction result of the current moment based on the new feature vector, judges whether the current moment is the t moment, if not, the next moment is a new current moment, and the step of constructing an adjacency matrix based on the to-be-processed data is returned to be executed, and if yes, a final prediction result is determined based on the t prediction results. In the application, the factor time attention is focused, the contribution of each variable and each time to the prediction is captured, and the accuracy of predicting the sintering endpoint is improved to a certain extent.