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INFERENCE PROCESSING DEVICE, INFERENCE PROCESSING METHOD AND INFERENCE PROCESSING PROGRAM

Resumen de: US2025390717A1

An inference processing device includes: a division unit that divides a layer of a convolutional neural network into a plurality of sublayers in a channel direction; a convolution unit that executes convolution processing for each of the sublayers to output a convolution result; an addition unit that adds an intermediate value obtained by cumulatively adding convolution results up to a previous sublayer to the convolution result with an adder for adding a bias to the convolution result every time the convolution processing is executed, and outputs an addition result; and an activation unit that inputs, to an activation function, the addition result obtained by adding the convolution result of a last sublayer on which the convolution processing has been executed last.

SELECTING A NEURAL NETWORK ARCHITECTURE FOR A SUPERVISED MACHINE LEARNING PROBLEM

Resumen de: US2025390745A1

Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.

MACHINE LEARNING SPARSE COMPUTATION MECHANISM FOR ARBITRARY NEURAL NETWORKS, ARITHMETIC COMPUTE MICROARCHITECTURE, AND SPARSITY FOR TRAINING MECHANISM

Resumen de: US2025384257A1

An apparatus to facilitate processing of a sparse matrix for arbitrary graph data is disclosed. The apparatus includes a graphics processing unit having a data management unit (DMU) that includes a scheduler for scheduling matrix operations, an active logic for tracking active input operands, and a skip logic for tracking unimportant input operands to be skipped by the scheduler. Processing circuitry is coupled to the DMU. The processing circuitry comprises a plurality of processing elements including logic to read operands and a multiplication unit to multiply two or more operands for the arbitrary graph data and customizable circuitry to provide custom functions.

METHOD AND DEVICE FOR TRAINING A NEURAL NETWORK BASED DECODER

Resumen de: US2025384277A1

A device and a method for training a neural network based decoder. The method includes during the training, quantizing, using a training quantizer, parameters representative of the coefficients of the neural network based decoder. A method and device are also provided for encoding at least parameters representative of the coefficients of a neural network based decoder. Provided also are a method for generating an encoded bitstream including an encoded neural network based decoder, a neural network based encoder and decoder, and a signal encoded using the neural network based encoder.

COMPUTER-IMPLEMENTED METHOD AND ARRANGEMENT FOR OPERATING A RECOMMENDATION SYSTEM, COMPUTER-READABLE DATA CARRIER AND COMPUTER PROGRAM PRODUCT

Resumen de: WO2025257015A1

The invention relates to a computer-implemented method for operating a recommendation system which involves a) converting at least one part of at least one 3D CAD model capable of acquiring so-called "product manufacturing information", PMI, data into a graph representation in such a way that the graph representation comprises existing PMI data of the 3D CAD model, b) training a so-called "graph neural network", GNN, model at least at a first, in particular initial, point in time, the GNN model being trained on the basis of at least the graph representation, c) generating at least one recommendation output concerning at least the part of the 3D CAD model, in particular an output by the recommendation system on the basis of the graph representation and the GNN model. Furthermore, the invention relates to an arrangement for carrying out the method, to a computer-readable data carrier, and to a computer program product.

AUTOMATED CREATION OF DIGITAL TWINS USING GRAPH-BASED INDUSTRIAL DATA

Resumen de: US2025384266A1

A computer-implemented method for automatically creating a digital twin of an industrial system having one or more devices includes accessing a triple store that includes an aggregated ontology of graph-based industrial data synchronized with the one or more devices. The triple store is queried for a specified device to extract, from the graph-based industrial data, structural information of the specified device defined by a tree comprising a hierarchy of nodes. For each node, a neural network element is assigned based on a mapping of node types to pre-defined neural network elements. The assigned neural network elements are combined based on the tree topology to create a digital twin neural network. The triple store is then queried to extract, form the graph-based industrial data, real-time process data gathered from the specified device at runtime and use the extracted real-time process data to tune parameters of the digital twin neural network.

SYSTEM AND METHOD FOR NEURAL NETWORK ORCHESTRATION

Resumen de: US2025384661A1

Methods and systems for training one or more neural networks for transcription and for transcribing a media file using the trained one or more neural networks are provided. One of the methods includes: segmenting the media file into a plurality of segments; inputting each segment, one segment at a time, of the plurality of segments into a first neural network trained to perform speech recognition; extracting outputs, one segment at a time, from one or more layers of the first neural network; and training a second neural network to generate a predicted-WER (word error rate) of a plurality of transcription engines for each segment based at least on outputs from the one or more layers of the first neural network.

OBJECTIVE-CONDITIONED GENERATIVE NEURAL NETWORKS

Resumen de: WO2025260090A1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a generative neural network. In particular, the generative neural network is trained on an objective function that includes multiple different objectives, with two or more of the objectives being reward objectives.

TRAINING ENCODER MODEL AND/OR USING TRAINED ENCODER MODEL TO DETERMINE RESPONSIVE ACTION(S) FOR NATURAL LANGUAGE INPUT

Nº publicación: US2025384350A1 18/12/2025

Solicitante:

GOOGLE LLC [US]
GOOGLE LLC

Resumen de: US2025384350A1

Systems, methods, and computer readable media related to: training an encoder model that can be utilized to determine semantic similarity of a natural language textual string to each of one or more additional natural language textual strings (directly and/or indirectly); and/or using a trained encoder model to determine one or more responsive actions to perform in response to a natural language query. The encoder model is a machine learning model, such as a neural network model. In some implementations of training the encoder model, the encoder model is trained as part of a larger network architecture trained based on one or more tasks that are distinct from a “semantic textual similarity” task for which the encoder model can be used.