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35
resultados
Última actualización
27/07/2025 [07:20:00]
Resumen de: WO2025151950A1
The disclosure relates to methods and systems of partitioning-based scalable weighted aggregation composition for embeddings learned from knowledge graphs for training neural networks to perform downstream machine-learning tasks. For example, a system may access a knowledge graph comprising a plurality of nodes and partition the knowledge graph into a plurality of partitions based on edge densities between nodes of the knowledge graph. The system may perform partition-wise encoding using compositional message passing between nodes that enables learning from neighboring nodes. The system may generate an embedding for each node and each relation type in each partition based on the partition-wise encoding using compositional message passing. The system may concatenate the generated embeddings from the plurality of partitions. The system may train a global neural network for a downstream prediction task based on the concatenated embeddings using one or more weight matrices.
Resumen de: US2025238673A1
A system and method of detecting an aberrant message is provided. An ordered set of words within the message is detected. The set of words found within the message is linked to a corresponding set of expected words, the set of expected words having semantic attributes. A set of grammatical structures represented in the message is detected, based on the ordered set of words and the semantic attributes of the corresponding set of expected words. A cognitive noise vector comprising a quantitative measure of a deviation between grammatical structures represented in the message and an expected measure of grammatical structures for a message of the type is then determined. The cognitive noise vector may be processed by higher levels of the neural network and/or an external processor.
Resumen de: US2025238288A1
Systems and methods for determining neural network brittleness are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a modeling request comprising a preliminary model and a dataset. The operations may include determining a preliminary brittleness score of the preliminary model. The operations may include identifying a reference model and determining a reference brittleness score of the reference model. The operations may include comparing the preliminary brittleness score to the reference brittleness score and generating a preferred model based on the comparison. The operations may include providing the preferred model.
Resumen de: US2025238658A1
This application relates to a data processing method and apparatus, and a storage medium. The method includes: extracting a feature sequence of target data, where the feature sequence includes T input features, T is a positive integer, and t∈1, T; obtaining T hidden state vectors based on a recurrent neural network, where a tth hidden state vector is determined based on a (t−1)th input feature, a (t−1)th hidden state vector, and a (t−1)th extended state vector, and the (t−1)th extended state vector is obtained by performing lightweight processing based on the (t−1)th hidden state vector; and obtaining a processing result of the target data based on the T hidden state vectors by using a downstream task network.
Resumen de: US2025238902A1
The subject technology receives an input image, the input image comprising a selfie. The subject technology transforms, using a neural network, the input image to a latent representation of an identity. The subject technology transforms, using a diffusion model, a text condition to a second latent representation compatible with the latent representation of the identity. The subject technology transforms a pose template to a set of latent features for the diffusion model. The subject technology generates an intermediate image based on the latent representation of the identity, the second latent representation, and the set of latent features. The subject technology modifies, using a face enhancement network, the intermediate image based on the input image. The subject technology generates, using a face restoration network, a final output image based on the modified intermediate image. The subject technology provides for display the final output image on a display of a client device.
Resumen de: WO2024056547A1
A split neural network includes a tail network model (706) that receives a first plurality of activations and a second plurality of activations at a cut layer of the split neural network, and that generates a model output in response to the first plurality of activations and the second plurality of activations; a head network model (704) that receives a plurality of input feature values and generates the first plurality of activations in response to the plurality of input feature values and provides the first plurality of activations to the tail network model at the cut layer; and a translator model (708) that receives the first plurality of activations, that generates estimated values of the second plurality of activations in response to the first plurality of activations, and that provides the estimated values of the second plurality of activations to the tail network model at the cut layer.
Resumen de: US2025232762A1
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing video data using an adaptive visual speech recognition model. One of the methods includes receiving a video that includes a plurality of video frames that depict a first speaker; obtaining a first embedding characterizing the first speaker; and processing a first input comprising (i) the video and (ii) the first embedding using a visual speech recognition neural network having a plurality of parameters, wherein the visual speech recognition neural network is configured to process the video and the first embedding in accordance with trained values of the parameters to generate a speech recognition output that defines a sequence of one or more words being spoken by the first speaker in the video.
Resumen de: US2025232173A1
An evolutionary AutoML framework called LEAF optimizes hyperparameters, network architectures and the size of the network. LEAF makes use of both evolutionary algorithms (EAs) and distributed computing frameworks. A multiobjective evolutionary algorithm is used to maximize the performance and minimize the complexity of the evolved networks simultaneously by calculating the Pareto front given a group of individuals that have been evaluated for multiple objectives.
Resumen de: US2025232174A1
An evolutionary AutoML framework called LEAF optimizes hyperparameters, network architectures and the size of the network. LEAF makes use of both evolutionary algorithms (EAs) and distributed computing frameworks. A multiobjective evolutionary algorithm is used to maximize the performance and minimize the complexity of the evolved networks simultaneously by calculating the Pareto front given a group of individuals that have been evaluated for multiple objectives.
Resumen de: US2025232175A1
A system and method for controlling a nodal network. The method includes estimating an effect on the objective caused by the existence or non-existence of a direct connection between a pair of nodes and changing a structure of the nodal network based at least in part on the estimate of the effect. A nodal network includes a strict partially ordered set, a weighted directed acyclic graph, an artificial neural network, and/or a layered feed-forward neural network.
Resumen de: US2025232176A1
A method of constructing geometry-induced sparse hybrid highly connected artificial neural network architectures comprising, selecting a geometry defined in terms of a manifold, selecting a direction of data flow in the geometry, selecting a node set as a finite subset of the geometry, partitioning the node set into layers with respect to the geometry and the direction of data flow, selecting an edge set consisting of edges between each node in each non-input layer of the layers and nodes in preceding layers of the layers, selecting one or more subgraphs of the resulting digraph, where each subgraph defines an individual geometry-induced sparse hybrid highly connected artificial neural network architecture with a hierarchy of edge length scales, implementing the sparse hybrid highly connected artificial neural network architectures with hierarchies of edge length scales concretely, and training the sparse hybrid highly connected artificial neural network architectures.
Resumen de: WO2025150734A1
In an embodiment, an electronic device may comprise at least one processor, and a memory for storing one or more instructions. The memory may further store a first sub-neural network model and a second sub-neural network model. The one or more instructions may be executed by the at least one processor so as to cause the electronic device to: acquire N image frames; acquire feature information for each of the image frames by inputting the image frames one by one to the first sub-neural network model; and acquire an enhanced image by inputting, to the second sub-neural network model, integrated feature information obtained by merging the feature information for each of the image frames. The first sub-neural network model may be configured to receive one image frame and output feature information. The second sub-neural network model may be configured to output an enhanced image on the basis of input feature information.
Resumen de: WO2025151795A1
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and use of a language model neural network as a domain-specific conversational agent for a particular domain.
Resumen de: EP4586109A1
This application provides an inference method for a neural network model. The method is applied to a computing cluster. The computing cluster includes a plurality of inference servers and a memory pool. Each inference server includes at least one inference card and a local memory. The method includes: A first inference card of a first inference server in the computing cluster receives an inference task. If a parameter for executing the inference task is not hit in the first inference card, the first inference card obtains the parameter from a local memory of the first server. If the parameter is not hit in the local memory of the first server, the first inference card obtains the parameter from the memory pool. The first inference card can execute the inference task based on all obtained parameters. A speed of obtaining the parameter by the first inference card can be improved based on a high-speed read/write capability of the local memory of the first inference server, to reduce a delay of obtaining the parameter by the first inference card, and meet a requirement for a low delay of executing the inference task. In addition, this application further provides a corresponding apparatus, computing cluster, and storage medium.
Resumen de: WO2025146824A1
A control method for controlling an electro-mechanical system according to a task estimates the state of the system using an adaptive surrogate model of the system to produce an estimation of the state of the system. The adaptive surrogate model includes a neural network employing a weighted combination of neural ODEs of dynamics of the system in latent space, such that weights of the weighted combination of neural ODEs represent the uncertainty. The method controls the system according to the task based on the estimation of the state of the system and tunes the weights of the weighted combination of neural ODEs based on the controlling.
Resumen de: US2025225614A1
Remote distribution of multiple neural network models to various client devices over a network can be implemented by identifying a native neural network and remotely converting the native neural network to a target neural network based on a given client device operating environment. The native neural network can be configured for execution using efficient parameters, and the target neural network can use less efficient but more precise parameters.
Resumen de: US2025225384A1
Systems and methods are disclosed herein for training a model with a learned loss function. In an example system, a first trained neural network is generated based on application of a first loss function, such as a predefined loss function. A set of values is extracted from one or more of the layers of the neural network model, such as the weights of one of the layers. A separate machine learning model is trained using the set of values and a set of labels (e.g., ground truth annotations for a set of data). The machine learning model outputs a symbolic equation based on the training. The symbolic equation is applied to the first trained neural network to generate a second trained neural network. In this manner, a learned loss function can be generated and used to train a neural network, resulting in improved performance of the neural network.
Resumen de: US2025225419A1
A method is applied to a computing cluster. The computing cluster includes a plurality of inference servers and a memory pool. Each inference server includes at least one inference card and a local memory. The method includes: a first inference card of a first inference server in the computing cluster receives an inference task. If a parameter for executing the inference task is not hit in the first inference card, the first inference card obtains the parameter from a local memory of the first server. If the parameter is not hit in the local memory of the first server, the first inference card obtains the parameter from the memory pool. The first inference card can execute the inference task based on all obtained parameters.
Resumen de: US2025224724A1
A method of operating an apparatus using a control system that includes at least one neural network. The method includes receiving an input value captured by the apparatus, processing the input value using the at least one neural network of the control system implemented on first one or more solid-state chips, and obtaining an output from the at least one neural network resulting from processing the input value. The method may also include processing the output with another neural network implemented on solid-state chips to determine whether the output breaches a predetermined condition that is unchangeable after an initial installation onto the control system. The aforementioned another neural network is prevented from being retrained. The method may also include the step of using the output from the at least one neural network to control the apparatus unless the output breaches the predetermined condition. Similar corresponding apparatuses are described.
Resumen de: US2025225386A1
Systems, devices, methods, and computer-readable media for reflexive model generation and inference. A method includes receiving probabilistic rules of a reflexive model that correlate evidence with existence of an event of interest, training, based on ground truth examples of evidence and respective labels indicating whether the event of interest is/was/will be present or not, a neural network (NN) to encode the probabilistic rules and learn respective probabilities for the probabilistic rules, and providing, by the NN and responsive to new evidence, an output indicating a likelihood the event of interest exists.
Resumen de: US2025225798A1
Methods and systems are described for classifying unfertilized eggs. For example, using control circuitry, first images of fertilized eggs can be received, and the first images can be labeled with known classifications. Using the control circuitry, an artificial neural network can be trained to detect the known classifications based on the first images of the fertilized eggs and a second image can be received of an unfertilized egg with an unknown classification. Using the control circuitry, the second image can be input into the trained artificial neural network and a prediction from the trained artificial neural network can be received that the second image corresponds to one or more of the known classifications.
Resumen de: US2025225183A1
A computerized method is disclosed that includes operations of receiving a plurality of alerts, generating a graph-based dense representation of each alert of the plurality of alerts including processing of each alert with a neural network, wherein a result of processing an individual alert by the neural network is a graph-based dense representation of the individual alert, computing relatedness scores between at least a subset of the plurality of alerts, and generating a graphical user interface illustrating a listing of at least a subset of the plurality of alerts, wherein the graphical user interface is configured to receive user input corresponding to selection of a first alert, wherein the graphical user interface is rendered on a display screen. Additionally, an additional operation may include training the neural network to produce graph-based dense representations, wherein the training is performed on a corpus of metapaths.
Resumen de: EP4583004A1
Systems and methods are disclosed herein for training a model with a learned loss function. In an example system, a first trained neural network is generated based on application of a first loss function, such as a predefined loss function. A set of values is extracted from one or more of the layers of the neural network model, such as the weights of one of the layers. A separate machine learning model is trained using the set of values and a set of labels (e.g., ground truth annotations for a set of data). The machine learning model outputs a symbolic equation based on the training. The symbolic equation is applied to the first trained neural network to generate a second trained neural network. In this manner, a learned loss function can be generated and used to train a neural network, resulting in improved performance of the neural network.
Resumen de: EP4583099A1
Though several data augmentation techniques have been explored in the signal or feature space, very few studies have explored augmentation in the embedding space for Automatic Speech Recognition (ASR). The outputs of the hidden layers of a neural network can be seen as different representations or projections of the features. The augmentations performed on the features may not necessarily translate into augmentation of the different projections of the features as obtained from the output of the hidden layers. To overcome the challenges of the conventional approaches, embodiments herein provide a method and system for augmented speech embeddings based automatic speech recognition. The present disclosure provides an augmentation scheme which works on the speech embeddings. The augmentation works by replacing a set of randomly selected embeddings by noise during training. It does not require additional data, works online during training, and adds very little to the overall computational cost.
Nº publicación: KR20250102720A 07/07/2025
Solicitante:
성균관대학교산학협력단
Resumen de: WO2025143765A1
A deep neural network inference method based on an idle CPU resource according to the present specification may comprise the steps of: when a training task is reserved and all GPUs are used, classifying a CPU core into a training task-specific group, and classifying an unallocated idle CPU core into an unallocated group (U group); executing a training task by using a CPU core of the training task-specific group and, when there is a request for an online inference task, executing the online inference task by using the idle CPU core of the U group; and when there is a request for a batch inference task, additionally executing the batch inference task by using at least one of the idle CPU core of the U group and an idle CPU core of the training task-specific group.
BOPI
Sede Electrónica
