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GRAPHICS ARCHITECTURE INCLUDING A NEURAL NETWORK PIPELINE

Resumen de: US2025252650A1

One embodiment provides a graphics processor comprising a block of graphics cores and circuitry including a programmable neural network unit, the programmable neural network unit including one or more neural network hardware blocks, wherein a neural network hardware block includes circuitry to perform neural network operations and activation operations for a layer of a neural network, the programmable neural network unit addressable by cores within the block of graphics cores, wherein the programmable neural network unit is to configure one or more neural network hardware blocks with a meta-shader neural network, the meta-shader neural network to generate a texture for one of multiple types of terrain.

GENERALIST COMBINATIONAL OPTIMIZATION AGENT LEARNER

Resumen de: US2025249583A1

A method for robot navigation includes: receiving a backbone neural network, the backbone neural network trained to solve a set of autonomous navigation tasks including a first autonomous navigation task, where the first autonomous navigation task includes optimizing a first path visiting a first number of first locations when considering first path costs; receiving a second autonomous navigation task, where the second autonomous navigation task includes optimizing a second path visiting a second number of second locations when considering second path costs, where the second locations are locations in an environment of an autonomous machine, where the second number is different from the first number; configuring adapter layers for the backbone neural network for forming a neural network pipeline; and feeding the second locations and the second path costs to the neural network pipeline and determining a path for the autonomous machine based on an output.

COMBINED DENOISING AND UPSCALING NETWORK WITH IMPORTANCE SAMPLING IN A GRAPHICS ENVIRONMENT

Resumen de: US2025252530A1

An apparatus to facilitate combined denoising and upscaling network with importance sampling in a graphics environment is disclosed. The apparatus includes set of processing resources including circuitry configured to: receive, at an input of a density map neural network, a sampled signal of a current frame and a reconstructed sample of the current frame; output, from the density map neural network, a prediction of a density map of samples based on the input of the current frame; provide the density map of samples to a sampler; reproject the density map of samples to a next frame; and apply the reprojected density map of samples to the next frame to generate a next sampled signal.

Control sequence generation system and methods

Resumen de: US2025252313A1

A model receives a target demand curve as an input and outputs an optimized control sequence that allows equipment within a physical space to be run optimally. A thermodynamic model is created that represents equipment within the physical space, with the equipment being laid out as nodes within the model according to the equipment flow in the physical space. The equipment activation functions comprise equations that mimic equipment operation. Values flow between the nodes similarly to how states flow between the actual equipment. The model is run such that a control sequence is used as input into the neural network; the neural network outputs a demand curve which is then checked against the target demand curve. Machine learning methods are then used to determine a new control sequence. The model is run until a goal state is reached.

ROBOT NAVIGATION BASED ON MULTI-TASK NEURAL COMBINATORIAL OPTIMIZATION

Resumen de: EP4597374A1

A method for robot navigation is disclosed, the method comprising receiving a backbone neural network, the backbone neural network having been trained to solve a set of robot navigation tasks comprising a first robot navigation task of optimizing a first path visiting a first number of first locations when considering first path costs. The method further comprises receiving a second robot navigation task, wherein the second robot navigation task comprises optimizing a second path visiting a second number of second locations when considering second path costs. The method further comprises configuring adapter layers for the backbone neural network for forming a neural network pipeline, wherein the adapter layers comprise a node adapter, an edge adapter and an output adapter. The method further comprises feeding the second locations and the second path costs to the neural network pipeline to determine a path for an autonomous machine based on the output.

Systems and methods for optimizing hyperparameters for machine learning models

Resumen de: GB2637695A

A combined hyperparameter and proxy model tuning method is described. The method involves iterations for hyperparameters search 102. In each search iteration, candidate hyperparameters are considered. An initial (‘seed’) hyperparameter is determined by initialization function 110, and used to train (104) one or more first proxy models on a target dataset 101. From the first proxy model(s), one or more first synthetic datasets are sampled using sampling function 108. A first evaluation model is fitted to each first synthetic dataset, for each candidate hyperparameter, by applying fit function 106 enabling each candidate hyperparameter from hyperparameter generator 112 to be scored. Based on the respective scores assigned to the candidate hyperparameters, a candidate hyperparameter is selected and used (103) to train one or more second proxy models on the target dataset. Hyperparameter search may be random, grid and Bayesian. Scores by scoring function 114 can be F1 scores. Uses include generative causal model with neural network architectures.

USER SELECTION PREDICTION BASED ON GRAPH NEURAL NETWORK AND USER SELECTION SEQUENCE

Resumen de: MX2025007777A

A method for predicting a next user selection in an electronic user interface includes receiving a sequence of user selections through the electronic user interface, determining a context embedding vector according to the sequence of user selections, querying a knowledge graph, the knowledge graph respective of a plurality of possible user selections, with the context embeddings vector, to obtain a knowledge-enhanced representation of the sequence, determining, with a graph neural network respective of the knowledge graph, based on the knowledge-enhanced representation, a respective representation of each selection in the sequence of user selections, and determining a predicted next user selection according to the respective representations of the selections in the sequence of user selections.

METHOD AND SYSTEM FOR EMERGENCY MONITORING AND ACTING IN DOMESTIC ENVIRONMENTS

Resumen de: US2025246065A1

A method and system for emergency monitoring and acting in domestic environments of a user (10), wherein a data stream capturing device (310) provides data for local processing (320) in a user end device (300). This involves classifying events using a lightweight neural network and triggering actions if emergency is detected based on the events classification. In case of emergency, an external user (20) is notified through an external user end device (330) to manage the situation by selecting actions from the set of triggered actions and provides a validation (370) on whether the actions were correctly taken once the situation is under control. The causality supervisor (340) reviews the log of events and actions (360) and the validation (370) from the external end device (330) for improvements. The local knowledge database (380) is updated to adapt or reinforce behaviour (350) based on the detected emergencies and triggered actions. Also, the user (10) can provide feedback to adapt the system as the causality supervisor (340), for example, if a false negative happens.

- Inference apparatus and learning method thereof using a forward-forward algorithm based on unsupervised neural network

Resumen de: KR20250115745A

비지도 신경망 기반의 포워드-포워드 알고리즘을 사용하는 추론장치 및 그 학습방법에 관한 기술이 개시된다. 추론장치는 입력된 데이터로부터 잠재벡터를 출력하도록 학습된 신경망을 구비하는 복수의 포워드 레이어와, 복수의 포워드 레이어 간에 잠재벡터를 전달하는 잠재벡터 전달부와, 복수의 포워드 레이어 각각에서 출력되는 잠재벡터들을 결합하여 특징벡터를 생성하는 특징벡터 생성부와, 특징벡터를 이용하여 추론 결과를 출력하도록 학습된 출력 레이어를 포함한다. 복수의 포워드 레이어는 비지도 학습 방법에 의하여 잠재벡터를 출력하도록 학습된 비지도 신경망을 포함하고, 잠재벡터 전달부는 먼저 학습되는 전단 포워드 레이어에서 출력되는 잠재벡터를 전단 포워드 레이어의 다음으로 학습되는 후단 포워드 레이어의 입력으로 전달한다. 본 발명에 의하면 특수한 입력과 손실함수를 사용하지 않고도 안정적으로 포워드-포워드 알고리즘을 적용한 추론장치를 학습시킬 수 있다.

KNOWLEDGE GRAPH CONSTRUCTION VIA GENERATIVE ARTIFICIAL INTELLIGENCE

Resumen de: US2025245226A1

Systems or techniques that facilitate knowledge graph construction via generative artificial intelligence are provided. In various embodiments, a system can access a plurality of electronic documents associated with design or fabrication of a medical imaging scanner. In various aspects, the system can construct a knowledge graph representing the plurality of electronic documents, by iteratively executing a generative text-to-text neural network on a design discovery tree associated with the medical imaging scanner. In various instances, the system can access a natural language query regarding the medical imaging scanner and can convert, via execution of another neural network, the natural language query to a structured query. In various cases, the system can execute the structured query over the knowledge graph, thereby yielding an electronic answer to the natural language query.

ELECTRONIC DEVICE AND METHOD FOR CONTROLLING SAME

Resumen de: US2025245119A1

An example method for controlling an electronic device includes detecting at least one user and acquiring user information of the detected at least one user; determining a user mode based on the acquired user information; determining a service to be provided to the detected at least one user, by inputting the user information and the determined user mode as input data to a model learned by an artificial intelligence algorithm; and providing the determined service corresponding to the user mode. A method for providing the service by the electronic device may at least partially use an artificial intelligence model learned according to at least one of machine learning, neural network, and deep learning algorithms.

DEEP NEURAL NETWORK BASED IMAGE COMPRESSION USING A LATENT SHIFT BASED ON GRADIENT OF LATENTS ENTROPY

Resumen de: MX2025003035A

A deep neural network based video compression system in which gradients of entropies with respect to side and main latents are used on decoding side to improve compression efficiency.

KNOWLEDGE GRAPH CONSTRUCTION VIA GENERATIVE ARTIFICIAL INTELLIGENCE

Resumen de: EP4592896A1

Systems or techniques that facilitate knowledge graph construction via generative artificial intelligence are provided. In various embodiments, a system can access a plurality of electronic documents (e.g., 106) associated with design or fabrication of a medical imaging scanner (e.g., 104). In various aspects, the system can construct a knowledge graph (e.g., 206) representing the plurality of electronic documents, by iteratively executing a generative text-to-text neural network (e.g., 202) on a design discovery tree (e.g., 204) associated with the medical imaging scanner. In various instances, the system can access a natural language query (e.g., 1602) regarding the medical imaging scanner and can convert, via execution of another neural network (e.g., 1604), the natural language query to a structured query (e.g., 1606). In various cases, the system can execute the structured query over the knowledge graph, thereby yielding an electronic answer (e.g., 1608) to the natural language query.

IMPROVED PEAK DETECTION

Resumen de: EP4592654A1

The invention provides, amongst other aspects, a method for detecting peaks in an optical spectrum, the method comprising the steps of obtaining the optical spectrum from at least one optical spectrometer, the optical spectrum comprising a wavelength range; and applying a trained neural network, NN, on the optical spectrum to detect the peaks in the optical spectrum, wherein the detecting of peaks relates to output nodes of the NN having been trained w.r.t zones corresponding to subranges of the wavelength range. Further provided is a device carrying out the method, the device preferably comprising a memory including the trained NN. Further provided is a trained NN having been trained w.r.t zones corresponding to subranges of the wavelength range.

PARTITIONING-BASED SCALABLE WEIGHTED AGGREGATION COMPOSITION FOR KNOWLEDGE GRAPH EMBEDDING

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.

GENERATIVE PIPELINE USING SINGLE SELFIE INPUT

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.

INTELLIGENT CONTROL WITH HIERARCHICAL STACKED NEURAL NETWORKS

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.

DATA PROCESSING METHOD AND APPARATUS, AND STORAGE MEDIUM

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.

SYSTEMS AND METHODS TO IDENTIFY NEURAL NETWORK BRITTLENESS BASED ON SAMPLE DATA AND SEED GENERATION

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.

INDEPENDENT SPLIT MODEL INFERENCE IN SPLIT NEURAL NETWORK FOR ESTIMATING NETWORK PARAMETERS

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.

ADAPTIVE VISUAL SPEECH RECOGNITION

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.

MULTIOBJECTIVE COEVOLUTION OF DEEP NEURAL NETWORK ARCHITECTURES

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.

MULTIOBJECTIVE COEVOLUTION OF DEEP NEURAL NETWORK ARCHITECTURES

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.

ACCELERATED TRAINING OF A NEURAL NETWORK VIA REGULARIZATION

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.

Sparse Highly Connected Artificial Neural Network Architectures Involving Hybrid Scale Structure

Nº publicación: US2025232176A1 17/07/2025

Solicitante:

DRIBUS BENJAMIN FORREST [US]
Dribus Benjamin Forrest

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.