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38
resultados
Última actualización
19/10/2024 [07:24:00]
Resumen de: US2024346655A1
The present system provides methods and systems of detecting lung abnormalities in chest x-ray images using at least two neural networks.
Resumen de: US2024347061A1
Training and/or utilizing a single neural network model to generate, at each of a plurality of assistant turns of a dialog session between a user and an automated assistant, a corresponding automated assistant natural language response and/or a corresponding automated assistant action. For example, at a given assistant turn of a dialog session, both a corresponding natural language response and a corresponding action can be generated jointly and based directly on output generated using the single neural network model. The corresponding response and/or corresponding action can be generated based on processing, using the neural network model, dialog history and a plurality of discrete resources. For example, the neural network model can be used to generate a response and/or action on a token-by-token basis.
Resumen de: US2024347164A1
An estimate of a functional capacity such as VO2Max is made by applying the vital signs of a monitored human to a trained encoding neural network producing a cardio profile vector. The vector is applied to a trained functional capacity (VO2Max) neural network to estimate the functional capacity. Once estimated, an action is taken.
Resumen de: US2024346654A1
The present system provides methods and systems of detecting lung abnormalities in chest x-ray images using at least two neural networks.
Resumen de: US2024346298A1
A parallel convolutional neural network is provided. The CNN is implemented by a plurality of convolutional neural networks each on a respective processing node. Each CNN has a plurality of layers. A subset of the layers are interconnected between processing nodes such that activations are fed forward across nodes. The remaining subset is not so interconnected.
Resumen de: US2024346332A1
Aspects discussed herein may relate to methods and techniques for embedding constrained and unconstrained optimization programs as layers in a neural network architecture. Systems are provided that implement a method of solving a particular optimization problem by a neural network architecture. Prior systems required use of external software to pre-solve optimization programs so that previously determined parameters could be used as fixed input in the neural network architecture. Aspects described herein may transform the structure of common optimization problems/programs into forms suitable for use in a neural network. This transformation may be invertible, allowing the system to learn the solution to the optimization program using gradient descent techniques via backpropagation of errors through the neural network architecture. Thus these optimization layers may be solved via operation of the neural network itself.
Resumen de: EP4446946A2
A method of generating a controller (60) for a continuous process. The method includes receiving from a storage memory (26) off-line stored values of one or more controlled variables and one or more manipulated variables of the continuous process over a plurality of time points. The off-line stored values are used to train a first neural network to operate as a predictor (58) of the controlled variables. Then, the method includes training a second neural network to operate as a controller of the continuous process using the first neural network after it was trained to operate as the predictor for the continuous process and employing the second neural network as a controller of the continuous process.
Resumen de: GB2629048A
A sock sensor obtains physiological and/or motion data of a user wearing a sock 110, sequenced time series data is processed using a machine learning classifier 122 to generate classification data to predict one of a plurality of categories which represent the state of stress of the person. The states of stress of the person may include pain or anxiety. Preferably the physiological sensor senses electrodermal (EDA) activity of the skin and the motion sensor is an accelerometer. Preferably, the processing of the time series data in a sequence generates a 2D image (fig 4) that represents relationships between the data in the sequence, and this is achieved using a convolutional neural network (CNN) to generate the classification data. The classification data may have a score for each category of the plurality of categories. An alert maybe created based on the stress category. Also, a method of monitoring a person via a wearable sensor is disclosed and a computer implemented method to detect whether a person is exhibiting a heightened physiological response.
Resumen de: EP4446941A2
Methods and apparatus for discrimitive semantic transfer and physics-inspired optimization in deep learning are disclosed. A computation training method for a convolutional neural network (CNN) includes receiving a sequence of training images in the CNN of a first stage to describe objects of a cluttered scene as a semantic segmentation mask. The semantic segmentation mask is received in a semantic segmentation network of a second stage to produce semantic features. Using weights from the first stage as feature extractors and weights from the second stage as classifiers, edges of the cluttered scene are identified using the semantic features.
Resumen de: WO2024211290A1
The embodiments are directed to an inferential sensing system, methods and computer program product of an estimator for estimating parameters of complex nonlinear time-varying systems from scarce system output measurements. The estimator comprises a two-step process to accurately estimate the time-varying parameters of the time-varying system based on the input and output sample of the time-varying system. First, multiple filters in the high frequency processing loop, operating independently and concurrently process the input and output samples of the time-varying system to generate a hypersurface comprising time series objects. Each filter is restricted to adapt only a subset of the modeled time-varying parameters. The hypersurface comprising the time series objects is aggregated over several iterations of the high frequency processing loop. Second, the hypersurface is passed through a neural network in the low frequency processing loop to infer estimates of the time-varying system parameters.
Resumen de: AU2024219930A1
Documnt 1-20/09/2024 The present disclosure relates to systems and methods for detecting and detecting anomalies within a tree structure. In one implementation, the system may include 5 one or more memories storing instructions and one or more processors configured to execute the instructions. The instructions may include instructions to receive a new data structure related to an individual, convert the data structure into a Bayesian wavelet, using a tree structure of existing Bayesian wavelets, calculate one or more harmonics, determine a measure of whether the Bayesian wavelet 10 alters the one or more harmonics, and add the Bayesian wavelet to the tree structure when the measure is below a threshold. - 44-
Resumen de: US2024338414A1
This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.
Resumen de: EP4443377A2
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.
Resumen de: WO2024205617A1
System and method of classification of manufacturing defects and anomaly detection based on a neural network model applying physical measurement data. The system includes a graph engine and a graph neural network (GNN). The graph engine generates a graph of measurement points, a measurement matrix, and weight matrix of connected node distances. The GNN model includes an encoder that encodes the measurement matrix and the weight matrix to generate a latent node representation, from which a decoder determines node reconstructions. Unsupervised training converges by minimizing node reconstruction loss, the reconstruction performed using a decoder that decodes the latent node representation. Supervised training converges by minimizing reconstruction of binary labels of annotated measurement inputs, the reconstruction performed by a softmax layer that translates the latent node representation.
Resumen de: US2024333927A1
The present disclosure relates to video encoding or decoding and, more specifically, to an apparatus and a method for applying an artificial neural network (ANN) to video encoding or decoding. The apparatus and the method of the present disclosure are characterized by applying a CNN-based filter to a first picture and at least one of a quantization parameter map and a block partition map to output a second picture.
Resumen de: US2024333924A1
The present disclosure relates to video encoding or decoding and, more specifically, to an apparatus and a method for applying an artificial neural network (ANN) to video encoding or decoding. The apparatus and the method of the present disclosure are characterized by applying a CNN-based filter to a first picture and at least one of a quantization parameter map and a block partition map to output a second picture.
Resumen de: US2024333925A1
The present disclosure relates to video encoding or decoding and, more specifically, to an apparatus and a method for applying an artificial neural network (ANN) to video encoding or decoding. The apparatus and the method of the present disclosure are characterized by applying a CNN-based filter to a first picture and at least one of a quantization parameter map and a block partition map to output a second picture.
Resumen de: US2024331041A1
A user-described virtual environment method, system, and apparatus obtains a representation of an object and receives a natural language-based communication from a user requesting that a computer-implemented system embody the object within a virtual environment that is described by the user. The natural language description of the virtual environment is interpreted by applying a computer-implemented trained neural network A video stream that embodies the object within a computer-generated virtual environment that is in accordance with the user-described virtual environment is generated by applying a trained neural network and then delivered to the user. The user may then describe desired modifications to the virtual environment and a second video stream is generated in accordance with the desired modifications.
Resumen de: US2024331342A1
Systems and methods are provided for determining an occlusion class indicator corresponding to an occlusion image. This can include acquiring the occlusion image of an occlusion of a human subject by an image capture device, applying one or more computer-implemented occlusion classification neural networks to the occlusion image to determine the class indicator of the occlusion of the human subject. The occlusion classification neural networks are trained for classification using an occlusion training dataset including a plurality of occlusion training examples being pre-classified into one three occlusion classes, each class being attributed a numerical value. The occlusion class indicator determined by the occlusion classification neural network includes a numeric value within a continuous range of values that can be bounded by the values corresponding to the second and third occlusion classes.
Resumen de: US2024330645A1
A computer-implemented method includes receiving, at a neural network, input data indicating at least video data and natural language data, in response to meeting a convergence threshold of the neural network utilizing the input data, outputting one or more patterns associated with the input data to a cognitive architecture, wherein the cognitive architecture is in communication with a symbolic framework that includes a knowledge graph database and the symbolic framework is configured to identify contextual information of the one or more patterns received from the neural network utilizing at least the knowledge graph database, in response to the symbolic framework communicating the contextual information with the neural network, embedding the neural network with the contextual information of the knowledge graph dataset and outputting a recommendation indicating information associated with at least the input data utilizing an embedded neural network.
Resumen de: US2024330681A1
A device for optimizing parameters of a deep neural network is included in an integrated circuit apparatus. The integrated circuit apparatus includes a general interconnection interface and other processing apparatus. A computing apparatus interacts with other processing apparatus to jointly complete a computing operation specified by a user. The integrated circuit apparatus further includes a storage apparatus. The storage apparatus is connected to the computing apparatus and other processing apparatus, respectively. The storage apparatus is used for data storage of the computing apparatus and other processing apparatus.
Resumen de: US2024330679A1
A method for making predictions pertaining to entities represented within a heterogeneous graph includes: identifying, for each node in the heterogeneous graph structure, a set of node-target paths that connect the node to a target node; assigning, to each of the node-target paths identified for each node, a path type identifier indicative of a number of edges and corresponding edge types in the associated node-target path; and extracting a semantic tree from the heterogeneous graph structure. The semantic tree includes the target node as a root node and defines a hierarchy of metapaths that each individually correspond to a subset of the node-target paths in the heterogeneous graph structure assigned to a same path type identifier. The semantic tree is encoded, using one or more neural networks by generating a metapath embedding corresponding to each metapath in the semantic tree. Each of the resulting metapath embeddings encodes aggregated feature-label data for nodes in the heterogeneous graph structure corresponding to the path type identifier corresponding to the metapath associated with the metapath embedding. A label is predicted for the target node in the heterogeneous graph structure based on the set of metapath embeddings.
Resumen de: US2024330685A1
The present application relates to a computer-implemented method for an improved technique for optimising the loss function during deep learning. The method includes receiving a training data set comprising a plurality of data items, initialising weights of at least one neural network layer of the ML model, and training, using an iterative process, the at least one neural network layer of the ML model by inputting, into the at least one neural network layer, the plurality of data items, processing the plurality of data items using the at least one neural network layer and the weights, optimising a loss function of the weights by simultaneously minimising a loss value and a loss sharpness using weights that lie in a neighbourhood having a similar low loss value, wherein the neighbourhood is determined by a geometry of a parameter space defined by the weights of the ML model, and updating the weights of the at least one neural network layer using the optimised loss function.
Resumen de: US2024333926A1
The present disclosure relates to video encoding or decoding and, more specifically, to an apparatus and a method for applying an artificial neural network (ANN) to video encoding or decoding. The apparatus and the method of the present disclosure are characterized by applying a CNN-based filter to a first picture and at least one of a quantization parameter map and a block partition map to output a second picture.
Nº publicación: WO2024205909A1 03/10/2024
Solicitante:
MICROSOFT TECH LICENSING LLC [US]
MICROSOFT TECHNOLOGY LICENSING, LLC
Resumen de: WO2024205909A1
A method for making predictions includes identifying, for each node in the heterogeneous graph structure, a set of node-target paths that connect the node to a target node; assigning, to each of the node-target paths, a path type identifier indicative of a number of edges and corresponding edge types in the associated node-target path; and extracting a semantic tree from the heterogeneous graph structure. The semantic tree includes the target node as a root node and defines a hierarchy of metapaths that each correspond to a subset of the node-target paths in the heterogeneous graph structure assigned to a same path type identifier. The semantic tree is encoded by generating a metapath embedding corresponding to each metapath in the semantic tree. A label is predicted for the target node in the heterogeneous graph structure based on the set of metapath embeddings.
BOPI
Sede Electrónica
