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45
resultados
Última actualización
07/12/2024 [07:32:00]
Resumen de: US2024403660A1
Systems and methods for determining a placement for computational graph across multiple hardware devices. One of the methods includes generating a policy output using a policy neural network and using the policy output to generate a final placement that satisfies one or more constraints.
Resumen de: US2024403382A1
Provided are a method and a device of multivariable time series processing. The method comprises obtaining a time series set comprising a plurality of first time series segments having a same length and being a multivariable time series; inputting the first time series segment into a graph neural network to predict a multivariable reference value corresponding to a first time point that is a next time point adjacent to a latest time point in the first time series segment; determining an optimization function based on multivariable reference values corresponding to a plurality of the first time points and corresponding multivariable series tags; determining values of respective parameters in the causal matrix with an objective of minimizing the optimization function; and determining, based on the values of the respective parameters in the causal matrix, a causal relationship between multiple variables in the multivariable time series.
Resumen de: WO2024248787A1
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing a multi-modal machine learning task on a network input that includes text and an image to generate a network output. One of the systems includes a vision-language model (VLM) neural network. The VLM neural network includes a VLM backbone neural network and an attention-based feature adapter. The VLM neural network has access to an external dataset that stores multiple text items.
Resumen de: AU2023280790A1
A predictive control system includes controllable equipment and a controller. The controller is configured to use a neural network model to predict values of controlled variables predicted to result from operating the controllable equipment in accordance with corresponding values of manipulated variables, use the values of the controlled variables predicted by the neural network model to evaluate an objective function that defines a control objective as a function of at least the controlled variables, perform a predictive optimization process to generate optimal values of the manipulated variables for a plurality of time steps in an optimization period using the neural network model and the objective function, and operate the controllable equipment by providing the controllable equipment with control signals based on the optimal values of the manipulated variables generated by performing the predictive optimization process.
Resumen de: US2024403620A1
An apparatus to facilitate acceleration of machine learning operations is disclosed. The apparatus comprises at least one processor to perform operations to implement a neural network and accelerator logic to perform communicatively coupled to the processor to perform compute operations for the neural network.
Resumen de: EP4471670A2
A method (600) for detecting a hotword includes receiving a sequence of input frames (210) that characterize streaming audio (118) captured by a user device (102) and generating a probability score (350) indicating a presence of the hotword in the streaming audio using a memorized neural network (300). The network includes sequentially-stacked single value decomposition filter (SVDF) layers (302) and each SVDF layer includes at least one neuron (312). Each neuron includes a respective memory component (330), a first stage (320) configured to perform filtering on audio features (410) of each input frame individually and output to the memory component, and a second stage (340) configured to perform filtering on all the filtered audio features residing in the respective memory component. The method also includes determining whether the probability score satisfies a hotword detection threshold and initiating a wake-up process on the user device for processing additional terms.
Resumen de: EP4471668A1
Provided is an electronic device including a memory storing a state inference model, and at least one instruction; a transceiver; and at least one processor configured to execute the at least one instruction to: obtain, via the transceiver, first state information of each of a plurality of devices at a first time point, obtain second state information of each of the plurality of devices at a second time point that is a preset time interval after the first time point, by inputting the first state information to the state inference model, and determine an inference distribution ratio of the artificial neural network of each of the plurality of devices, based on the second state information of each of the plurality of devices, where the electronic device is determined among the plurality of devices, based on network states of the plurality of devices.
Resumen de: WO2024239499A1
The present application relates to the field of intelligent measurement. Particularly disclosed are a purification method and system for electronic-grade lithium hexafluorophosphate. By means of a deep learning-based neural network model, the present application mines the contextual implicit association relationship between the drying temperature and the vacuum degree during purification, so as to achieve automation control of the drying temperature, thus effectively avoiding product decomposition, loss of crystal water and other problems, and improving the purity and quality of electronic-grade lithium hexafluorophosphate products.
Resumen de: US2024395245A1
A system for classifying words in a batch of words can include at least one memory device storing instructions for causing at least one processor to create dictionary vectors for each of a plurality of dictionary words using a neural network (NN), store each dictionary vector along with a classification indicator corresponding to the associated dictionary word, and create word vectors for each word in a batch of words for classification using the NN. The closest matching dictionary vectors are found for each word vector and the classification indicators of the closest matching dictionary vector for each word vector in the batch is reported.
Resumen de: US2024394981A1
Systems, methods, and computer-readable media for generating 3D models are disclosed. A method includes accessing an image depicting a dental arch of a user. The method further includes identifying, from the image, a set of features of the dental arch. The method further includes generating, using a convolutional neural network, a first voxel grid based on the set of features identified from the image, wherein a shape of the first voxel grid is generated based on a camera projection matrix accounting for a depth of the dental arch depicted in the image. The method further includes generating a 3D model including a 3D surface of the dental arch of the user based on a voxel grid from combining at least a portion of the first voxel grid and a portion of a second voxel grid.
Resumen de: US2024394540A1
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scalable continual learning using neural networks. One of the methods includes receiving new training data for a new machine learning task; training an active subnetwork on the new training data to determine trained values of the active network parameters from initial values of the active network parameters while holding current values of the knowledge parameters fixed; and training a knowledge subnetwork on the new training data to determine updated values of the knowledge parameters from the current values of the knowledge parameters by training the knowledge subnetwork to generate knowledge outputs for the new training inputs that match active outputs generated by the trained active subnetwork for the new training inputs.
Resumen de: US2024394507A1
The disclosure relates to a method, apparatus, system, medium and electronic device for graph neural network generation. The method includes: obtaining a subgraph structure, the subgraph structure being configured to reflect a graph structure of a corresponding subgraph, and the subgraph comprising a plurality of nodes and edges between the plurality of nodes; obtaining, based on the subgraph structure and according to a predetermined priority, node features of the plurality of nodes and edge features of the edges from a plurality of memories; the predetermined priority being obtained by sorting the plurality of memories in accordance with memory size in an ascending order; fusing, based on the subgraph structure, the node features of the plurality of nodes and the edge features of the edges to obtain subgraph data; and training, based on the subgraph data, the graph neural network.
Resumen de: WO2024240424A1
According to the invention, a plurality of objects (O1, O2) are supplied to the machine (M) in a real or dynamically simulated process. Additionally, it is continuously detected which of the objects (O1, O2) currently adjoin one another. Furthermore, each of the objects (O1, O2) is assigned a respective control agent, (P1, P2) into which current state data (S1, S2) of the respective object (O1, O2) is fed. Furthermore, neurons of a neural network (GNN) are continuously linked according to the currently detected proximities of the objects (O1, O2). The transmission of transfer data sets (TD) between the control agents (P1, P2) is controlled by means of the neural network (GNN). Furthermore, a manipulation of the objects (O1, O2) is controlled in a real manner or in a simulated manner by means of the control agents (P1, P2) using the fed state data (S1, S2) and the transmitted transfer data sets (TD), and the performance (RET) of the machine (M) relating thereto is ascertained. The neural network (GNN) is thus trained to optimize the performance (RET). Finally, the machine (M) is controlled using the trained neural network (GNN) and the control agents (P1, P2).
Resumen de: US2024396647A1
Methods and an expert system for processing a plurality of inputs collected from sensors in an industrial environment are disclosed. A modular neural network, where the expert system uses one type of neural network for recognizing a pattern relating to at least one of: the sensors, components of the industrial environment and a different neural network for self-organizing a data collection activity in the industrial environment is disclosed. A data communication network configured to communicate at least a portion of the plurality of inputs collected from the sensors to storage device is also disclosed.
Resumen de: US2024394019A1
A method for computation with recurrent neural networks includes receiving an input drive and a recurrent drive, producing at least one modulatory response; computing at least one output response, each output response including a sum of: (1) the input drive multiplied by a function of at least one of the at least one modulatory response, each input drive including a function of at least one input, and (2) the recurrent drive multiplied by a function of at least one of the at least one modulatory response, each recurrent drive including a function of the at least one output response, each modulatory response including a function of at least one of (i) the at least one input, (ii) the at least one output response, or (iii) at least one first offset, and computing a readout of the at least one output response.
Resumen de: US2024394236A1
A system and method to generate search results in response to a search query based on comparisons of embedding vectors. The system and method receive, from an end user system, a search query including a set of keywords associated with the entity. Using a neural network, an embedding vector is identified based on the set of keywords of the search query. The system and method compares the embedding vector associated with the search query to a set of embedding vectors associated with a set of structured data elements relating to the entity. Based on the comparison, a set of matching structured data elements is identified. The system and method generate a search result in response to the search query, wherein the search result includes at least a portion of the set of matching structured data elements. The search result is displayed via an interface of the end user system.
Resumen de: US2024394776A1
Disclosed are systems and methods utilizing neural contextual bandit for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to make item recommendations using latent relations and latent representations, which can improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods use neural network modeling in automatic selection of a number of items for recommendation to a user and using feedback in connection with the recommendation for further training of the model(s).
Resumen de: US2024394531A1
Embodiments of this disclosure provide a method and apparatus for compiling a neural network model, and a method and an apparatus for training an optimization model. The method includes: obtaining a to-be-compiled neural network model; determining an intermediate instruction sequence corresponding to the to-be-compiled neural network model based on the to-be-compiled neural network model; processing the intermediate instruction sequence by using a pre-trained instruction sequence optimization model, to obtain a target optimization parameter corresponding to the intermediate instruction sequence; determining an optimization instruction sequence corresponding to the intermediate instruction sequence based on the target optimization parameter; and converting the optimization instruction sequence into an executable instruction sequence, to obtain a target instruction sequence that is executable by a neural network processor corresponding to the to-be-compiled neural network model. According to the embodiments of this disclosure, compilation time can be greatly reduced, thereby effectively improving compilation efficiency.
Resumen de: EP4468202A1
Embodiments of this disclosure provide a method and apparatus for compiling a neural network model, and a method and an apparatus for training an optimization model. The method includes: obtaining a to-be-compiled neural network model; determining an intermediate instruction sequence corresponding to the to-be-compiled neural network model based on the to-be-compiled neural network model; processing the intermediate instruction sequence by using a pre-trained instruction sequence optimization model, to obtain a target optimization parameter corresponding to the intermediate instruction sequence; determining an optimization instruction sequence corresponding to the intermediate instruction sequence based on the target optimization parameter; and converting the optimization instruction sequence into an executable instruction sequence, to obtain a target instruction sequence that is executable by a neural network processor corresponding to the to-be-compiled neural network model. According to the embodiments of this disclosure, compilation time can be greatly reduced, thereby effectively improving compilation efficiency.
Resumen de: GB2630419A
A conversational open-domain question answering (OpenQA) method based on curriculum learning. As well as a retriever model, and reader model the OpenQA method introduces a sorter module to re-sort the top K articles retrieved by a retriever, such that more relevant articles are ranked in the top T positions. Performing joint training of the models using a semi-automatic curriculum learning strategy having two core components: an automatic difficulty measure and a discrete training scheduler. The semi-automatic curriculum learning strategy reduces the probability of manually adding a golden paragraph/positive sample article. The automatic difficulty measurer determining if a difficult training mode is, or a simple training mode is available. If difficult mode is available the discrete training scheduler automatically introduces the positive sample article into a retrieval result. If a simple mode is available the discrete scheduler skips adding the positive sample article. The method then using a sum of scores from the sorter and the reader as a predicted score outputs an answer span with a highest total score as a final answer. A subsequent neural network may be added after the retriever to establish a sorter enabling it to learn a high-level feature representation of an article.
Resumen de: US2024386283A1
A method allows configuration of the weights of neural network models of nodes from a set of nodes of a communication network, the neural networks all having a model with the same structure. The method includes partitioning the set of nodes into a cluster of nodes and sending, to a node belonging to the cluster, an item of information according to which the node should act as an aggregation node in the cluster and identifiers of the nodes of the cluster. The method also includes sending, to the aggregation node of the cluster, a request for learning the weights of the node models of the cluster with the weights of a global model for the set of nodes, receiving, from the aggregation node of the cluster, the weights of an aggregated model of the cluster resulting from the training, and updating the weights of the global model by aggregating the weights received from the aggregated model of the cluster.
Resumen de: WO2024238024A1
A processor-implemented method for generating grounded rationales for visual reasoning tasks includes receiving, by a first artificial neural network (ANN), an interleaved sequence of images and textual information. The first ANN extracts grid features of the images of the interleaved sequence of the images and the textual information to generate a representation of the interleaved sequence of the images and the textual information based on the grid features. A second ANN maps the grid features to a textual domain. The second ANN extracts visual information of the interleaved sequence of the images and the textual information based on the grid features in the textual domain. The second ANN determines a rationale based on the visual information. The visual information comprises one or more lower-level surrogate tasks.
Resumen de: WO2024237500A1
The present invention relates to a multi-task real-time inference scheduling system and real-time inference scheduling method of a machine tool, wherein a central control unit is connected to each of one or more individual control units through a network, receives a use context of each machine tool through each individual control unit, generates a multi-task learning model through a neural network, infers multiple tasks required to be performed by the individual control unit of each machine tool through machine learning by using real-time use contexts collected during operation of the machine tool by a use scenario, and schedules the multiple tasks of the machine tool through machine learning.
Resumen de: WO2023169771A1
The invention discloses a computer-implemented method for accelerating deep learning inference of a neural network with layers, whereby a line-wise image consisting of pixels is generated by a line-camera (1) scanning an object (3), whereby: for each new pixel-line added to the image, results of previously calculations for pixels of the current layer are used instead of repeated calculations to calculate the value of a pixel in the next layer. A corresponding arrangement comprising a neural network is disclosed as well.
Nº publicación: US2024378137A1 14/11/2024
Solicitante:
BAYERISCHE MOTOREN WERKE AG [DE]
Bayerische Motoren Werke Aktiengesellschaft
Resumen de: US2024378137A1
Systems, methods, and apparatuses are provided for learning-based anomaly detection to determine a software error in a networked vehicle. Trace lines are translated using a controller of the vehicle. A node list with weighted links is input into a graph neural network. Similarities and dependencies of each node are output as embedded features in a floating-point format with respect to other nodes of the node list in an embedded representation and for each node of the node list. Embedded features of nodes are sorted into a temporal sequence based on a timestamp of each translated trace line. The embedded features of the nodes are augmented with similar embedded features of nodes determined using a distance metric. Similar embedded features are input into a deep neural network together with the embedded features. A time of an error probability and/or error class of an anomaly is output to determine the software error.
BOPI
Sede Electrónica
