Aldaketak

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.

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.

ROBOT NAVIGATION BASED ON MULTI-TASK NEURAL COMBINATORIAL OPTIMIZATION

Nº publicación: EP4597374A1 06/08/2025

Solicitante:

NAVER CORP [KR]
Naver Corporation

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.