Alerta

ROBOT NAVIGATION BASED ON MULTI-TASK NEURAL COMBINATORIAL OPTIMIZATION

Absstract of: 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

Absstract of: 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.

KNOWLEDGE GRAPH CONSTRUCTION VIA GENERATIVE ARTIFICIAL INTELLIGENCE

Absstract of: 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

Absstract of: 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.

METHOD AND SYSTEM FOR EMERGENCY MONITORING AND ACTING IN DOMESTIC ENVIRONMENTS

Absstract of: 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

Absstract of: 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

Absstract of: 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.

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

Absstract of: 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.

IMPROVED PEAK DETECTION

Absstract of: 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.

INTELLIGENT CONTROL WITH HIERARCHICAL STACKED NEURAL NETWORKS

Absstract of: 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.

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

Absstract of: 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.

DATA PROCESSING METHOD AND APPARATUS, AND STORAGE MEDIUM

Absstract of: 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.

GENERATIVE PIPELINE USING SINGLE SELFIE INPUT

Absstract of: 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.

PARTITIONING-BASED SCALABLE WEIGHTED AGGREGATION COMPOSITION FOR KNOWLEDGE GRAPH EMBEDDING

Absstract of: 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.

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

Absstract of: 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.

MULTIOBJECTIVE COEVOLUTION OF DEEP NEURAL NETWORK ARCHITECTURES

Absstract of: 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.

ADAPTIVE VISUAL SPEECH RECOGNITION

Absstract of: 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.

LANGUAGE MODEL NEURAL NETWORKS FOR DOMAIN-SPECIFIC CONVERSATIONAL AGENTS

Absstract of: 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.

ACCELERATED TRAINING OF A NEURAL NETWORK VIA REGULARIZATION

Absstract of: 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.

ELECTRONIC DEVICE FOR ACQUIRING IMAGE AND OPERATION METHOD THEREFOR

Absstract of: 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.

MULTIOBJECTIVE COEVOLUTION OF DEEP NEURAL NETWORK ARCHITECTURES

Absstract of: 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.

Sparse Highly Connected Artificial Neural Network Architectures Involving Hybrid Scale Structure

Absstract of: 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.

USING A RECURRENT NEURAL NETWORK AND A CLASSIFICATION MACHINE LEARNING MODEL TO PREDICT ACTIONS IN SOFTWARE APPLICATIONS

Absstract of: US2025232178A1

Aspects of the present disclosure provide techniques for machine learning based action prediction. Embodiments include providing, as inputs to a recurrent neural network (RNN), an ordered sequence of strings representing actions performed by a user within a software application, the RNN having been trained through a supervised learning process to generate embeddings of the ordered sequence of strings and generate a numerical score relating to a target action based on the embeddings and an order of the ordered sequence of strings. Embodiments include providing, as respective inputs to a tree-based classification machine learning model, the numerical score and an additional feature relating to the user and receiving, as a respective output from the tree-based classification machine learning model in response to the respective inputs, a propensity score indicating a likelihood of the user to perform the target action.

INFERENCE METHOD AND APPARATUS FOR NEURAL NETWORK MODEL, AND RELATED DEVICE

Absstract of: 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.

DATA-DRIVEN STATE ESTIMATION AND SYSTEM CONTROL UNDER UNCERTAINTY

Nº publicación: WO2025146824A1 10/07/2025

Applicant:

MITSUBISHI ELECTRIC CORP [JP]
MITSUBISHI ELECTRIC CORPORATION

Absstract of: 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.