110 results about "Model representation" patented technology

Methods and systems enabling multimodal inputs for interacting with a live digital object are provided. The system receives the live digital object which includes a digital artifact extracted from a digital model file through a model representation. The system receives a user's security level and determines the user's access permission and modification permission to access and modify the digital artifact. The system accesses a multimodal interface configured to receive a conversational input and a spatial input, outputs the digital artifact to the multimodal interface based on the access permission, receives from the multimodal interface a conversational input and a spatial input from the user, and generates a modified digital artifact from the digital artifact via the digital model representation, based on the modification permission and on the conversational or spatial input. The multimodal interface may include conventional interfaces (GUIs / APIs), conversational interfaces (text / voice), and spatial computing interfaces (VR / AR / MR / gestural).

Techniques are described for automatically generating visual model representations of buildings based at least in part on captured external imagery of the buildings, and using the generated building visual models to generate and present new images and optionally in additional manners, such as to improve navigation of a building and / or its surroundings. The described techniques may include acquiring building data from a plurality of exterior acquisition locations at multiple heights and view angles of an exterior of a building (e.g., using a flying drone and / or other flying device that captures the data), generating visual model representation(s) of the building (e.g., a 3D Gaussian Splat model), and using the generated visual model representation(s) to generate and present a new image with a view of the building exterior from a particular pose along with associated user-manipulatable controls.

This application discloses a model training method, a power prediction method, and a device. A model includes at least a first model and a second model. The method includes: obtaining a dataset including historical power data and historical meteorological data in preset duration; determining a weight of a loss function of each model based on the dataset; constructing a target loss function, where the first model and the second model may be used to represent input data, and data represented by the first model is not completely the same as data represented by the second model; and finally training the models based on the target loss function. More complete information is obtained from different modal data and different time scales based on a currently available data source, to optimize the learning model and achieve higher prediction precision.

The invention relates to a three-dimensional grid model processing method, computer equipment, a readable storage medium and a program product. The method comprises the steps of performing voxelization on a three-dimensional grid model of a target object to obtain a plurality of voxels corresponding to the target object in a preset reference space; determining the visibility value of the voxel according to the projection information of the voxel under the preset camera visual angle; determining a position relation label of the voxel relative to a reference point according to a communication relation between the voxel and the reference point in a preset reference space; screening out a target voxel from the voxels according to the visibility values and the position relation labels of the voxels; and determining model characterization parameters of the three-dimensional grid model according to the spatial information of the target voxel in the three-dimensional grid model. By adopting the method, the generation precision of the three-dimensional model can be improved.

The invention belongs to the technical field of federated learning, and more specifically relates to a non-data-driven quantum federated learning method based on single communication. The method comprises the following steps: a plurality of clients independently train classical models by depending on respective local data, only perform single communication after training is completed, and upload model representation information to a server; after receiving the model representation information uploaded by each client, the server uniformly fuses the information to construct an integrated teacher model; the server synthesizes a pseudo sample from the random noise by using a pseudo sample generator under the guidance of the integrated teacher model; and migrating the knowledge of the integrated teacher model to the quantum student model through soft label distillation by using the generated pseudo sample, and finally obtaining a unified quantum student model at a server side. The technical problems that in existing quantum federation learning, communication overhead is large, privacy protection and efficiency are difficult to consider, and quantum hardware resources are seriously limited are solved.

The invention discloses a causal decoupling method and device based on multi-scale noise and adversarial supervision, and the method comprises the steps: carrying out the simulation of the causal relationship of variables in a causal graph, so as to generate observation image data, and constructing a training set and a test set according to the observation image data, the corresponding causal label information and the causal graph; constructing a causal decoupling model, and performing adversarial supervision training under multi-scale noise by using the training set; and obtaining anti-fact intervention data by using the test set and the trained causal graph matrix and utilizing the trained observation data coding module and observation data decoding module. According to the method, an auto-encoder and causal acyclic constraints are fully combined, the discrimination module is trained under multi-scale noise, and high-quality adversarial supervision is performed, so that the model representation learning ability is improved, the representation understanding of the model on data with causal relationships is enhanced, the accuracy of implicit causal network prediction is improved, and the prediction efficiency is improved. And the causal decoupling accuracy is improved.

The invention provides a performance optimization method and system for a closed cooling tower, and relates to the technical field of data processing.The method comprises the steps that input parameters describing the closed cooling tower are obtained; determining a cumulative distribution function of each input parameter; discretizing each input parameter to form a discretized simulation sample; performing computational fluid mechanics simulation on the closed cooling tower by utilizing the simulation samples, and determining system response of each simulation sample; performing principal component analysis on system response data through singular value decomposition; establishing an incidence relation between the input variable and the score matrix through Kriging interpolation, and forming a complete preliminary prediction model between the input variable and the system response; through a high-dimensional model representation technology, the high-order interaction effect of the preliminary prediction model is optimized, and a performance prediction model of the closed cooling tower is obtained; in order to improve the cooling efficiency and reduce the construction cost, the design parameters of the closed cooling tower are optimized through a butterfly optimization algorithm.

The invention discloses a visual language action model-oriented target injection type fine tuning method, which comprises the following steps of: firstly, constructing any existing visual language action model, introducing a condition image generation model, and generating a target image with consistent semantics and vision according to an initial observation image and a task target instruction; secondly, target image features are injected into observation input through zero-initialization convolution, parameters are gradually increased from zero, it is ensured that interference noise is not introduced in the initial stage of fine adjustment to destroy a pre-training strategy, and in the training process, along with gradual optimization of the parameters, target image feature information is gradually fused into model representation, and the target image feature information is obtained; therefore, the understanding ability and the execution performance of the task target are improved. According to the method, through a lightweight target image injection mechanism and an efficient fine adjustment process, the performance of the model on various reference tasks can be remarkably improved in few training rounds, and the problem that an existing visual language action model cannot systematically introduce target image guidance is effectively solved.

A computing device includes at least one processor, one or more non-transitory computer-readable storage media, a system for fine-tuning a large language model under low-bit weight-activation quantization. The computing device further comprises a graphics processing unit (GPU), a neural processing unit (NPU), or a tensor processing unit (TPU). The hardware interface module of the system is configured to load a low-bit model representation from the memory module and transmit the model representation to the GPU, NPU, or TPU for inference execution.

Methods and systems for a document review process are provided. The method includes receiving an input digital model representation comprising at least one externally-accessible model endpoint for generating a digital artifact. Then, generating a document splice comprising access to multiple document subunits, with at least one document subunit written in a natural language and comprising the digital artifact; the access to each document subunit is provided through an externally-accessible document endpoint for the subunit. Then, generating a document by combining the document subunits, and generating a view associated with the document, based on an user authorization result including selective access rights to the document subunits. The view comprises access to the digital model representation, the digital artifact, each document subunit, and the document. Finally, receiving a user input and updating, via one of the externally-accessible document endpoints, the document splice based on the user input.

This application discloses a method and system for multilingual enhancement based on a large language model using model ensemble. The system includes: a pre-trained multilingual translation model, a semantic representation mapping module, and a large language model. The multilingual translation model is used for multilingual semantic modeling and language generation, including a multilingual encoder module and a multilingual decoder module. The semantic representation mapping module is used to transform the latent space representations of different models into an interactive unified semantic space based on a cross-model representation mapping mechanism. The output of the multilingual encoder is mapped to the unified semantic representation space of the large language model, and the mapped semantics are input into the large language model to perform language-independent instruction understanding. The intermediate semantic representation output by the large language model is mapped and transformed to a cross-attention representation space, generating the final output text under the target language distribution. The system of this application outperforms existing technologies in terms of efficiency, stability, and generation quality in multilingual capability extension.

The application discloses a kind of based on double model representation's aspheric surface parameter fitting and surface shape deviation measurement method, comprising 1) collection aspheric surface optical element surface several points, processing obtains the three-dimensional data of each sampling point;2) establish initial optical path model and set its wavefront evaluation function;3) input aspheric element's design parameter in turn;4) by the coordinate data of measured XOY plane is fitted to obtain the coordinate of its center point, conversion to the coordinate system of with optical design software coaxial, carries out Zernike fitting and obtains Zernike coefficient;5) in aspheric surface optical path model, using Zernike coefficient in step 4 is represented to be measured aspheric surface, in the same optical design software, constructs aspheric surface using two kinds of representation mode;6) radius of curvature is set as variable, the rest aspheric surface parameters are unchanged, and optimization obtains actual radius of curvature;7) conic coefficient and high order coefficient are set as variable, and optimization obtains aspheric surface parameter and surface shape deviation.

The application discloses a kind of energy storage cluster diversification modeling characterization method, system, equipment and storage medium, the method includes: obtaining the real-time data of each type of energy storage unit in energy storage cluster;Based on the real-time data obtained, different types of energy storage units are modeled using a diversified modeling method, and a diversified model library considering various types of energy storage units is constructed;According to the modeling results of the energy storage unit, the behavior of the energy storage cluster is characterized, the results of different diversified models are output, and the performance of the energy storage cluster is comprehensively evaluated;Using the diversified model constructed, an optimization algorithm is used to optimize the scheduling of the energy storage cluster;Monitor the operating state of the energy storage cluster, and adjust and update the diversified model in real time according to the operating state data.The application can effectively characterize the performance characteristics of various types of energy storage equipment and optimize the overall scheduling efficiency of the energy storage cluster.

The application provides an open vocabulary segmentation method and system for multi-modal model representation optimization, and belongs to the technical field of computer vision. Image data to be segmented is acquired; a pre-trained multi-modal model is used to process the acquired image to obtain a segmentation result. The application better optimizes visual-text representation in a multi-modal task, effectively aligns the same visual-text representation space, proposes a mask-sensitive loss to constrain the classification score and mask quality to be consistent in the parameter fine-tuning process, thereby giving the visual encoder local perception ability and improving the effect of the model in the fine-grained downstream task, introduces the original pre-training feature as a representation compensation to ensure the zero-shot ability of the pre-training visual-language model in the optimization process, and interacts the text representation and the visual representation, so that the text representation can be adaptively enhanced for different input images, and the alignment property of the visual-text in the open vocabulary segmentation can be effectively improved.

The invention discloses an analysis method and system for model training and extraction of case causes, and the method comprises the steps: carrying out the joint coding of a case document through fusion of Transform and a graph neural network (GNN), achieving the dual modeling of the semantic and structural relation of a case text sequence, introducing a knowledge graph to embed and enhance the model representation, and achieving the analysis of the case causes. The method has the advantages that by the aid of the multi-task learning framework, key elements can be identified, legal article quotation can be predicted and the like while action cause classification is completed by the model, the integral understanding depth is increased, the integral process automation degree is high, the accuracy and recall rate of action cause extraction can be greatly increased, and manual annotation dependence is reduced.

This application discloses a method and system for interpreting sparse interaction utility modeled by a black-box artificial intelligence model, relating to the field of machine learning technology. The method and system can automatically analyze the interaction distribution modeled by the model. Implementation of the method and system includes the steps of providing data that is essential for evaluation; predicting the data using a black-box model and obtaining the model's prediction results; modeling the interaction effects between sample input units based on the output of the black-box model, calculating the interaction intensity of the combinations formed between the input units, representing the black-box model as "and additive relationships" and "or additive relationships" between combinations of input units; and optimizing the "and additive relationships" and "or additive relationships" to make them more sparse. The advantage of the present invention is that it provides a quantification method for interpreting interactions modeled by a black-box artificial intelligence model, enabling a more sparse and concise interpretation of interactions compared to conventional studies.

The invention discloses a mechanical arm action generation method based on a self-supervised reconstruction task. And training a mechanical arm imitation learning strategy close to an expert strategy on the basis of the obtained expert motion trail for manually controlling the mechanical arm to complete the specified task. According to the mechanical arm imitation learning strategy, a de-noising diffusion probability model with U-Net as a backbone network is used, down-sampling and up-sampling coding is carried out on an action sequence after Gaussian noise is randomly added, a global condition vector is used in each coding layer to modulate features, the action increment is predicted, the control action of a mechanical arm is output, and the control action of the mechanical arm is obtained. And executing and completing the specified task. In the training process, a self-supervised reconstruction task is introduced, and the difference between the action based on middle-layer feature reconstruction, point cloud features and expert motion trails is compared, so that a denoising network is promoted to carry stronger observation semantics, the characterization ability of a model to a three-dimensional hidden space is enhanced, and the success rate and generalization ability of task execution of the mechanical arm are effectively improved.

The present disclosure relates to computer-aided dental restoration system that is configured to estimate crown pose and representing virtual crown in 3D model. The system obtains 3D model of dentition of the patient. The system segments 3D model to obtain segmented tooth data and generates encoded 3D model representation suitable for processing by trained neural network. The method for generating encoded 3D model representation comprises subsampling point cloud representation based on segmented tooth data, determining surface normal representation, retrieving dental notation of restorative site, encoding each of plurality of points with corresponding surface normal representation and with value relative to restorative site to produce encoded 3D model representation, inputting encoded 3D model representation into trained neural network, and producing output, using trained neural network, wherein output includes prediction of translation of each point of encoded 3D model representation into positions corresponding to crown pose.

Provided is a solution system capable of obtaining the optimal state of individual spins with a small amount of memory. The matrix simplification means 93 changes a matrix used in an energy function in a model representing states of individual spins by a first value or a second value into simplified form. In this case, the matrix simplification means 93 deletes elements that have a value of “0” from the matrix, and also deletes some elements that do not have the value of “0” from the matrix, thereby to change the matrix into the simplified form. The spin state derivation means 94 derives the optimal state of each spin based on the matrix changed into the simplified form.

State of art approaches independently use a Pruning-weight Clustering-Quantization (PCQ) or Knowledge Distillation (KD) for model optimization and require critical manual intervention. Embodiments of the present disclosure provide a method and system for the two-step hierarchical model optimization approach for generating optimized model DL model. The method comprises a AutoPCQ technique followed by conditional application of an automated KD (AKD) technique. The AutoPCQ technique formulates a problem of configuration selection of the DL model as an optimization problem by iteratively applying Bayesian optimization and Reinforcement Learning. Further, the AKD technique formulates automated search of a student model as the optimization problem with the DL model representing a teacher model. A search space for the student model is defined by a restricted Neural Network Architecture Search that restricts the search space. The method automates the model optimization, in time efficient manner without compromising accuracy of the optimized model.

An action redirection method includes: segmenting a target character based on semantics of different parts of the target character, to obtain a plurality of first body segments related to the target character; modeling each first body segment based on a first coordinate system marked on the first body segment, to obtain a plurality of first geometric models, where one first geometric model represents a shape of one body segment; performing collision detection on the plurality of first geometric models; and when first geometric models collide with each other, adjusting a distance between the first geometric models that collide with each other, and adjusting an action sequence of the target character based on an adjusted distance. Thus, a coordinate system is constructed for a body segment of the target character, and a geometric model is output through modeling, so that accurate geometric expressing of the body segment is implemented.

Techniques are described for automatically generating visual model representations of buildings based at least in part on captured external imagery of the buildings, and using the generated building visual models to generate and present new images and optionally in additional manners, such as to improve navigation of a building and / or its surroundings. The described techniques may include acquiring building data from a plurality of exterior acquisition locations at multiple heights and view angles of an exterior of a building (e.g., using a flying drone and / or other flying device that captures the data), generating visual model representation(s) of the building (e.g., a 3D Gaussian Splat model), and using the generated visual model representation(s) to generate and present a new image with a view of the building exterior from a particular pose along with associated user-manipulatable controls.

Methods for obtaining black-box representations of machine learning models are disclosed when information about the models' internal states or parameters is inaccessible. By using the model's outputs instead of its internal states, the black-box representation is model-agnostic and provides a reliable and robust representation of the model through an external lens. The black-box representation is generated using responses from the model to a series of initialization and information-gathering questions, quantifying the model's confidence in the responses it has just returned. The black-box representation is then used as a training dataset for a linear classifier to learn performance metrics about the model.

A machine learning model representation is obtained from a model source and information characterizing the layers of the model representation is extracted to result in extracted model information. This extracted model information can be compared to information characterizing one or more known (i.e., previously characterized) machine learning models in order to determine whether there is a match based on layer information. A match can, in some cases, be used to determine an identity of the underlying machine learning model for the model representation. Information regarding the comparison (i.e., the model matching determination) can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.

Methods and systems for obtaining contextual information about a machine learning model are provided. The method includes: receiving raw data that is usable for training a model; training the by using the raw data; computing a set of common background data based on the raw data; computing a first explanation based on an output of the model and the set of common background data; computing, based on an output of the model, an agnostic model representation of the model; computing, based on the first explanation and the agnostic model representation, a deep, compact, and dense explanation-driven representation of the model; and determining, based on the explanation-driven representation, contextual information that relates to the model.

This invention relates to a knowledge graph-based method for recommending manufacturing resources, comprising the following steps: establishing a supply and demand information model for the manufacturing domain; constructing a manufacturing domain ontology model to represent the concepts and relationships of demand information and manufacturing resources, serving as the schema layer of the manufacturing domain knowledge graph; performing knowledge extraction; utilizing the knowledge graph for visualization; implementing knowledge graph embedding, during training, obtaining erroneous triples (i.e., negative samples) by randomly replacing head entities; continuously optimizing the loss function using stochastic gradient descent to obtain qualified embedding vectors; after training the vector representations of entities using the TransE model, using vector value calculation to measure the degree of conformity of each resource, and employing cosine similarity calculation to calculate the similarity between vectors; and adding resource QoS service matching based on feature matching.

To provide a world model system and keyframe-based compression in an application.SOLUTION: In various examples, a method includes a step of calculating a current keyframe. The current keyframe represents an area around an autonomous vehicle at current time based on map data. The method includes a step of generating a first world model frame by converting a previous keyframe to a coordinate frame of the autonomous vehicle at a first time prior to completing calculation of the current keyframe. The method includes a step of generating a second world model frame by converting the previous keyframe to the coordinate frame of the autonomous vehicle at a second time after the first time and prior to completion of the calculation of the current keyframe.SELECTED DRAWING: Figure 1

The invention discloses a model dyeing method and device, a storage medium, equipment and a program product, and the method comprises the steps: obtaining the updated real-time dyeing parameters of a first model material in response to the dyeing parameter change of the first model material, and enabling the first model material to correspond to a first model; according to the dyeing parameter binding relation between the first model material and the second model material, the real-time dyeing parameter is synchronized to the second model material, the second model material corresponds to a second model, and the first model and the second model are two models of different dimensions of the same virtual object; and performing real-time dyeing processing on the second model based on the synchronized real-time dyeing parameters. According to the scheme provided by the embodiment of the invention, the real-time, accurate and consistent dyeing effect of the same virtual object under different dimension model representation forms is realized.