NEURONAL NETWORKS

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Solicitudes publicadas en los últimos 30 días / Applications published in the last 30 days



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TYPE INFERENCE IN DYNAMIC LANGUAGES

Publication No.: US2023029250A1 26/01/2023

Applicant:

INT BUSINESS MACHINES CORPORATION [US]

Absstract of: US2023029250A1

To improve the technological process of programming a computer using a dynamic programming language, generate a first portion of training data which maps types in the dynamic programming language to corresponding functions and methods by performing information retrieval on documentation libraries in the dynamic programming language and/or generate a second portion of training data which maps program variables to the corresponding functions and methods by performing data flow analysis on a plurality of pre-existing programs written in the dynamic programming language. Train a neural network on the first and/or second portions of training data to infer unknown types in the dynamic programming language. Carry out inference with the trained neural network to infer the unknown types. Facilitate programming in the dynamic programming language based on the inferred unknown types. Optionally, execute a resulting program.

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COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR LEARNING-BASED ANOMALY DETECTION IN ORDER TO DETERMINE A SOFTWARE ERROR IN A NETWORKED VEHICLE

Publication No.: WO2023001416A1 26/01/2023

Applicant:

BAYERISCHE MOTOREN WERKE AG [DE]

DE_102021118972_PA

Absstract of: WO2023001416A1

The invention relates to a computer-implemented method for learning-based anomaly detection in order to determine a software error in a networked vehicle (1). The method comprises translating (S2) trace lines (3a-3c) of a trace (3), which is present for example in the DLT format, as a file output by a controller (2) of the vehicle (1) and containing a time sequence of function calls by software components of the controller (2), into a graph representation (5) of an undirected graph (5a), wherein provision is made (S3) for a node list (6) containing weighted links (6a) between nodes (K1-K4, K17, K29) that each represent individual data segments (D1-D8) of the translated trace lines (3a-3c), followed by inputting (S3a) the node list (6) into a graph neural network (8), wherein, in an embedded representation (9) for each node (K1-K4, K17, K29) in the node list (6), similarities and dependencies of this node with respect to other nodes in the node list (6) are output (S4) as embedded features (F1-F3, FS17, FS29), for example in floating-point format. This is followed by sorting (S4a) the embedded features (F1-F3, FS17, FS29) of nodes (K1-K4, K17, K29) of multiple temporally successive trace lines (3a-3c) in a time sequence based on a timestamp (D2) of each translated trace line (3a-3c) and enriching (S4b) the embedded features (10) of the nodes of the multiple temporally successive trace lines (3a-3c) with the most similar embedded features (11) of nodes, which are determined by

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METHOD AND DEVICE FOR DETERMINING SATURATION RATIO-BASED QUANTIZATION RANGE FOR QUANTIZATION OF NEURAL NETWORK

Publication No.: WO2023003432A1 26/01/2023

Applicant:

SAPEON KOREA INC [KR]

Absstract of: WO2023003432A1

Disclosed are a method and device for determining a saturation ratio-based quantization range for quantization of a neural network. According to aspects of the present invention, provided are a method and device, the computer-implemented method, for determining a quantization range for tensors of an artificial neural network, comprising the steps of: observing the saturation ratio in current iteration from tensors and the quantization range of the artificial neural network; and controlling the quantization range such that the observed saturation ratio follows a preset target saturation ratio.

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MOTION COMPENSATION FOR NEURAL NETWORK ENHANCED IMAGES

Publication No.: US2023025778A1 26/01/2023

Applicant:

QUALCOMM INCORPORATED [US]

WO_2023004340_PA

Absstract of: US2023025778A1

A device includes a memory and one or more processors. The memory is configured to store instructions. The one or more processors are configured to execute the instructions to apply a neural network to a first image to generate an enhanced image. The one or more processors are also configured to execute the instructions to adjust at least a portion of a high-frequency component of the enhanced image based on a motion compensation operation to generate an adjusted high-frequency image component. The one or more processors are further configured to execute the instructions to combine a low-frequency component of the enhanced image and the adjusted high-frequency image component to generate an adjusted enhanced image.

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INFERENCE METHOD AND DEVICE USING VIDEO COMPRESSION

Publication No.: WO2023003448A1 26/01/2023

Applicant:

INTELLECTUAL DISCOVERY CO LTD [KR]

Absstract of: WO2023003448A1

A neural network-based image processing method and device according to embodiments of the present invention can: perform pre-processing for an input image; obtain a feature map from the pre-processed image by means of a neural network comprising a plurality of neural network layers; perform quantization for the obtained feature map; and perform video conversion with respect to the quantized feature map.

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FEATURE ENGINEERING IN NEURAL NETWORKS OPTIMIZATION

Publication No.: US2023027016A1 26/01/2023

Applicant:

INT BUSINESS MACHINES CORPORATION [US]

JP_2022539090_PA

Absstract of: US2023027016A1

A transitive closure data structure is constructed for a pair of features represented in a vector space corresponding to an input dataset. The data structure includes a set of entries corresponding to a set of all possible paths between a first feature in the pair and a second feature in the pair in a graph of the vector space. The data structure is reduced by removing a subset of the set of entries such that only a single entry corresponding to a single path remains in the transitive closure data structure. A feature cross is formed from a cluster of features remaining in a reduced ontology graph resulting from the reducing the transitive closure data structure. A layer is configured in a neural network to represent the feature cross, which causes the neural network to produce a prediction that is within a defined accuracy relative to the dataset.

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Multi-Turn Dialogue Response Generation Via Mutual Information Maximization

Publication No.: US2023021852A1 26/01/2023

Applicant:

CAPITAL ONE SERVICES LLC [US]

US_2021027023_A1

Absstract of: US2023021852A1

Machine classifiers in accordance with embodiments of the invention capture long-term temporal dependencies in the dialogue data better than the existing recurrent neural network-based architectures. Additionally, machine classifiers may model the joint distribution of the context and response as opposed to the conditional distribution of the response given the context as employed in sequence-to-sequence frameworks. Further, input data may be bidirectionally encoded using both forward and backward separators. The forward and backward representations of the input data may be used to train the machine classifiers using a single generative model and/or shared parameters between the encoder and decoder of the machine classifier. During inference, the backward model may be used to reevaluate previously generated output sequences and the forward model may be used to generate an output sequence based on the previously generated output sequences.

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EXPOSURE DEFECTS CLASSIFICATION OF IMAGES USING A NEURAL NETWORK

Publication No.: US2023024955A1 26/01/2023

Applicant:

ADOBE INC [US]

CN_113808069_PA

Absstract of: US2023024955A1

Embodiments of the present invention provide systems, methods, and computer storage media for detecting and classifying an exposure defect in an image using neural networks trained via a limited amount of labeled training images. An image may be applied to a first neural network to determine whether the images includes an exposure defect. Detected defective image may be applied to a second neural network to determine an exposure defect classification for the image. The exposure defect classification can includes severe underexposure, medium underexposure, mild underexposure, mild overexposure, medium overexposure, severe overexposure, and/or the like. The image may be presented to a user along with the exposure defect classification.

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PARALLEL COMPUTING SCHEME GENERATION FOR NEURAL NETWORKS

Publication No.: US2023024350A1 26/01/2023

Applicant:

HUAWEI TECH CO LTD [CN]

CN_113994350_PA

Absstract of: US2023024350A1

A device receives a computation graph and transforms the computation graph into a dataflow graph comprising recursive subgraphs. Each recursive subgraph comprises a tuple of another recursive subgraph and an operator node, or an empty graph. The device determines a number of partitioning recursions based on a number of parallel computing devices. For each partitioning recursion, the device determines costs corresponding to operator nodes, determines a processing order of the recursive subgraphs, and processes the recursive subgraphs. To process a recursive subgraph, the device selects a partitioning axis for tensors associated with an operator node of the recursive subgraph. The device outputs a partitioning scheme comprising partitioning axes for each tensor associated with the operator nodes.

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INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND STORAGE MEDIUM

Publication No.: US2023016670A1 19/01/2023

Applicant:

KUBOTA NOZOMU [JP]

CN_115618343_PA

Absstract of: US2023016670A1

An information processing apparatus includes a memory and processor. The memory stores a first inference model using a neural network and a plurality of defense algorithms. The at least one processor performs acquisition of prescribed data, input of the prescribed data to the first inference model to perform inference processing, the first inference model being learned using learning data including respective data and respective result data obtained by solving prescribed problems using the respective data, detection of a possibility as to whether a prescribed attack has been made on the prescribed data, specification of, when the possibility of the prescribed attack is detected, a first defense algorithm capable of making a defense against the prescribed attack from among the plurality of defense algorithms on a basis of the prescribed data on which the prescribed attack has been made, and application of the first defense algorithm to the inference processing.

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DEEP NEURAL NETWORK PROCESSING FOR SENSOR BLINDNESS DETECTION IN AUTONOMOUS MACHINE APPLICATIONS

Publication No.: US2023012645A1 19/01/2023

Applicant:

NVIDIA CORP [US]

WO_2020112213_A2

Absstract of: US2023012645A1

In various examples, a deep neural network (DNN) is trained for sensor blindness detection using a region and context-based approach. Using sensor data, the DNN may compute locations of blindness or compromised visibility regions as well as associated blindness classifications and/or blindness attributes associated therewith. In addition, the DNN may predict a usability of each instance of the sensor data for performing one or more operations—such as operations associated with semi-autonomous or autonomous driving. The combination of the outputs of the DNN may be used to filter out instances of the sensor data—or to filter out portions of instances of the sensor data determined to be compromised—that may lead to inaccurate or ineffective results for the one or more operations of the system.

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INFORMATION PROCESSING APPARATUS

Publication No.: US2023012843A1 19/01/2023

Applicant:

HITACHI ASTEMO LTD [JP]

DE_112020005642_T5

Absstract of: US2023012843A1

An autonomous driving system for a vehicle reduces the amount of computations for object extraction carried out by a DNN, using information a traveling environment or the like. An information processing apparatus including a processor, a memory, and an arithmetic unit that executes a computation using an inference model is provided. The information processing apparatus includes a DNN processing unit that receives external information, the DNN processing unit extracting an external object from the external information, using the inference model, and a processing content control unit that controls processing content of the DNN processing unit. The DNN processing unit includes an object extracting unit that executes the inference model in a deep neural network having a plurality of layers of neurons, and the processing content control unit includes an execution layer determining unit that determines the layers used by the object extracting unit.

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A Transferable Neural Architecture for Structured Data Extraction From Web Documents

Publication No.: US2023014465A1 19/01/2023

Applicant:

GOOGLE LLC [US]

CN_115023710_PA

Absstract of: US2023014465A1

Systems and methods for efficiently identifying and extracting machine-actionable structured data from web documents are provided. The technology employs neural network architectures which process the raw HTML content of a set of seed websites to create transferable models regarding information of interest. These models can then be applied to the raw HTML of other websites to identify similar information of interest. Data can thus be extracted across multiple websites in a functional, structured form that allows it to be used further by a processing system.

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TOOL FOR FACILITATING EFFICIENCY IN MACHINE LEARNING

Publication No.: US2023017304A1 19/01/2023

Applicant:

INTEL CORP [US]

US_2018314936_A1

Absstract of: US2023017304A1

A mechanism is described for facilitating smart distribution of resources for deep learning autonomous machines. A method of embodiments, as described herein, includes detecting one or more sets of data from one or more sources over one or more networks, and introducing a library to a neural network application to determine optimal point at which to apply frequency scaling without degrading performance of the neural network application at a computing device.

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Artificial Intelligence System for Sequence-to-Sequence Processing With Attention Adapted for Streaming Applications

Publication No.: US2023017503A1 19/01/2023

Applicant:

MITSUBISHI ELECTRIC RES LABORATORIES INC [US]

WO_2023276251_PA

Absstract of: US2023017503A1

The present disclosure provides an artificial intelligence (AI) system for sequence-to-sequence modeling with attention adapted for streaming applications. The AI system comprises at least one processor; and memory having instructions stored thereon that, when executed by the processor, cause the AI system to process each input frame in a sequence of input frames through layers of a deep neural network (DNN) to produce a sequence of outputs. At least some of the layers of the DNN include a dual self-attention module having a dual non-causal and causal architecture attending to non-causal frames and causal frames. Further, the AI system renders the sequence of outputs.

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MULTI DIMENSIONAL CONVOLUTION IN NEURAL NETWORK PROCESSOR

Publication No.: US2023018248A1 19/01/2023

Applicant:

APPLE INC [US]

KR_20220083820_PA

Absstract of: US2023018248A1

Embodiments of the present disclosure relate to a neural engine of a neural processor circuit having multiple multiply-add circuits and an accumulator circuit coupled to the multiply-add circuits. The multiply-add circuits perform multiply-add operations of a three dimensional convolution on a work unit of input data using a kernel to generate at least a portion of output data in a processing cycle. The accumulator circuit includes multiple batches of accumulators. Each batch of accumulators receives and stores, after the processing cycle, the portion of the output data for each output depth plane of multiple output depth planes. A corresponding batch of accumulators stores, after the processing cycle, the portion of the output data for a subset of the output channels and for each output depth plane.

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A SYSTEM FOR MONITORING AND CONTROLLING A DYNAMIC NETWORK

Publication No.: US2023013006A1 19/01/2023

Applicant:

DUBAI ELECTRICITY & WATER AUTHORITY [AE]

DE_112020004290_T5

Absstract of: US2023013006A1

The invention relates to a system for monitoring and controlling a dynamic network such as an oil, gas, or water pipeline. The system includes a plurality of sensors for measuring aspects of a state of the network with each sensor being associated with a segment of the network and connected to a virtual sensor which accumulates and pre-processes measurements from the sensors for each segment of the network. The system further includes a network topology processor for storing the topology of the network and relating sensors and virtual sensors to segments of the network and neighbouring sensors and virtual sensors in accordance with the topology and a reinforcement learning artificial neural network (ANN) based nonlinear state estimation and predictive control model which uses measurements from the sensors and virtual sensors to model the state of the network and estimate sequential states of the network.

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Systems and methods for automated analysis of medical images

Publication No.: AU2021289232A1 19/01/2023

Applicant:

ANNALISE AI PTY LTD

WO_2021248187_A1

Absstract of: AU2021289232A1

This disclosure relates to detecting visual findings in anatomical images. Methods comprise inputting anatomical images into a neural network to output a feature vector and computing an indication of visual findings being present in the images by a dense layer of the neural network that takes as input the feature vector and outputs an indication of whether each of the visual findings is present in the anatomical images. The neural network is trained on a training dataset including anatomical images, and labels associated with the anatomical images and each of the visual findings. The visual findings may be organised as a hierarchical ontology tree. The neural network may be trained by evaluating the performance of neural networks in detecting the visual findings and a negation pair class which comprises anatomical images where a first visual finding is identified in the absence of a second visual finding.

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HYPERPARAMETER NEURAL NETWORK ENSEMBLES

Publication No.: EP4118584A1 18/01/2023

Applicant:

GOOGLE LLC [US]

CN_115516466_PA

Absstract of: WO2021248140A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an ensemble of neural networks. In particular, the neural networks in the ensemble are trained using different hyperparameters from one another.

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NEURAL NETWORK BASED IDENTIFICATION DOCUMENT PROCESSING SYSTEM

Publication No.: US2023008443A1 12/01/2023

Applicant:

UBER TECHNOLOGIES INC [US]

US_2020311844_A1

Absstract of: US2023008443A1

A system processes images of documents, for example, identification documents. The system transforms an image of a document to generate an image that represent the document in a canonical form. For example, if the input image has a document that is tilted at an angle with respect to the sides of the image, the system modifies the orientation of the document to show the document having sides aligned with the sides of the image. The system stores user accounts that include user information including images. The system generates a graph of nodes that represent user accounts with edges determined based on similarity scores between user accounts. The system determines connected components of user accounts, such that each connected component represents user accounts that have a high likelihood of being duplicates.

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Image Processing Method and Corresponding System

Publication No.: US2023009282A1 12/01/2023

Applicant:

ST MICROELECTRONICS SRL [IT]

US_2020214614_A1

Absstract of: US2023009282A1

A method includes receiving a video signal that comprises a time series of images of a face of a human, wherein the images in the time series of images comprise a set of landmark points in the face, applying tracking processing to the video signal to reveal variations over time of at least one image parameter at the set of landmark points in the human face, generating a set of variation signals indicative of variations revealed at respective landmark points in the set of landmark points, applying processing to the set of variation signals, the processing comprising artificial neural network processing to produce a reconstructed PhotoPletysmoGraphy (PPG) signal, and estimating a heart rate variability of a variable heart rate of the human as a function of the reconstructed PPG signal.

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METHOD FOR PROVING OR IDENTIFYING COUNTER-EXAMPLES IN NEURAL NETWORKS SYSTEMS THAT PROCESS POINT CLOUD DATA

Publication No.: EP4115348A1 11/01/2023

Applicant:

HRL LAB LLC [US]

CN_115210721_PA

Absstract of: WO2021178009A1

Described is a system for proving correctness properties of a neural network for providing estimates for point cloud data. The system receives as input a description of a neural network for generating estimates from a set of point cloud data. The description of the neural network is parsed to obtain a symbolic representation. Based on a combination of the symbolic representation and a set of analysis parameters, the system generates an analysis output indicating whether the neural network satisfies a correctness property in generating the estimates from the set of point cloud data. The analysis output is a mathematical proof artifact proving that the set of analysis parameters is satisfied, a list of one or more point clouds for which the set of analysis parameters is violated, or a report that progress could not be made by the analysis.

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TRAJECTORY OPTIMIZATION USING NEURAL NETWORKS

Publication No.: EP4115347A1 11/01/2023

Applicant:

EMBODIED INTELLIGENCE INC [US]

US_2021276187_A1

Absstract of: WO2021178865A1

Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free, and minimizing the time it takes to complete the task.

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TRAJECTORY OPTIMIZATION USING NEURAL NETWORKS

Publication No.: EP4115340A1 11/01/2023

Applicant:

EMBODIED INTELLIGENCE INC [US]

US_2021276188_A1

Absstract of: WO2021178872A1

Various embodiments of the technology described herein generally relate to systems and methods for trajectory optimization with machine learning techniques. More specifically, certain embodiments relate to using neural networks to quickly predict optimized robotic arm trajectories in a variety of scenarios. Systems and methods described herein use deep neural networks to quickly predict optimized robotic arm trajectories according to certain constraints. Optimization, in accordance with some embodiments of the present technology, may include optimizing trajectory geometry and dynamics while satisfying a number of constraints, including staying collision-free and minimizing the time it takes to complete the task.

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METHOD FOR PROVING OR IDENTIFYING COUNTER-EXAMPLES IN NEURAL NETWORKS SYSTEMS THAT PROCESS POINT CLOUD DATA

Nº publicación: EP4115349A1 11/01/2023

Applicant:

HRL LAB LLC [US]

CN_115210721_PA

Absstract of: WO2021178009A1

Described is a system for proving correctness properties of a neural network for providing estimates for point cloud data. The system receives as input a description of a neural network for generating estimates from a set of point cloud data. The description of the neural network is parsed to obtain a symbolic representation. Based on a combination of the symbolic representation and a set of analysis parameters, the system generates an analysis output indicating whether the neural network satisfies a correctness property in generating the estimates from the set of point cloud data. The analysis output is a mathematical proof artifact proving that the set of analysis parameters is satisfied, a list of one or more point clouds for which the set of analysis parameters is violated, or a report that progress could not be made by the analysis.

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