830 results about "Training phase" patented technology

Systems and methods are provided for obtaining, from a database, a set of unlabeled data comprising data entries for a plurality of clients relating to eligibility for particular category of product, service or resource, e.g. discounted rates, applying a selection model to the set of unlabeled data to obtain a subset of unlabeled data, sending, to the communication interface, the subset of unlabeled data for oracle review by an oracle, receiving, from the communication interface, a subset of labeled data comprising a label from the oracle regarding eligibility for discounted rates of each of the plurality of clients in the subset of unlabeled data, applying, in a training phase, the subset of labeled client to a prediction model to adjust parameters of the prediction model, the prediction model for predicting whether each of the plurality of clients in the unlabeled data is eligible for discounted rates.

Techniques for learning to personalize vision-language models through meta-personalization are described. In one embodiment, one or more processing devices lock a pre-trained vision-language model (VLM) during a training phase. The processing devices train the pre-trained VLM to augment a text encoder of the pre-trained VLM with a set of general named video instances to form a meta-personalized VLM, the meta-personalized VLM to include global category features. The processing devices test the meta-personalized VLM to adapt the text encoder with a set of personal named video instances to form a personal VLM, the personal VLM comprising the global category features personalized with a set of personal instance weights to form a personal instance token associated with the user. Other embodiments are described and claimed.

The invention relates to the technical field of spatial-temporal trajectory data anomaly detection, in particular to a trajectory anomaly route detection method and system based on self-supervised trajectory representation learning. The method comprises the following steps: preprocessing acquired trajectory data; track double-view comparison representation learning is carried out based on a double-view-angle synchronous mask strategy; coding time dynamic based on a space-time fusion mechanism and fusing the time dynamic with spatial features; learning essential representation from the fused spatio-temporal features to perform trajectory reconstruction; and error checking is carried out based on the reconstructed trajectory, and an abnormal trajectory is judged through the reconstructed error. The invention provides a brand new trajectory anomaly detection model. According to the model, a GPS track and a grid-based track feature are fused, so that track representation is enriched; and meanwhile, a double-view synchronous mask mechanism is designed, so that the model can sense local disturbance of space and time dimensions at the same time in a training stage, and thus the sensitivity to local anomaly is improved.

The invention discloses a three-dimensional scene reconstruction method and device based on large model geometric prior, and a medium, and aims to solve the problems that a conventional 3DGS is liable to have artifacts and detail loss in geometric discontinuity, data redundancy and illumination variation scenes, and predicts a dense depth map and a normal map from a monocular image by using a pre-trained large model. The position and form of the Gaussian kernel are constrained as additional geometric priori; a primitive adjustment strategy based on kernel density estimation is introduced in the training stage, small Gaussian primitives with similar structures and adjacent spaces are combined into a large Gaussian primitive, the rendering quality is kept, redundancy is reduced, and the volume of the model is reduced; an exposure coefficient is adaptively estimated for each input image, an exposure compensation image loss function is constructed, and floating artifacts caused by illumination differences at shooting moments are eliminated. Experiments show that compared with the prior art, the method improves the three-dimensional reconstruction precision and real-time rendering quality of complex illumination and less-texture areas in a public data set and an unmanned aerial vehicle aerial photography scene.

An estimation system automatically estimates a severity of ulcerative colitis (UC) based on an endoscopic video. During a training phase, a training system trains one or more machine-learned models based on a set of training videos each annotated with a single video-level UC severity score representing an aggregate UC severity observed in the whole video. The one or more machine-learned models are capable of estimating UC severity depicted in an individual endoscopic video frame. Applying the one or more machine-learned models to an endoscopic test video of unknown UC severity enables estimation of frame-level UC severity scores for each frame of the test video. The frame-level UC severity scores may be represented on a continuous severity scale or may be mapped to discrete values on a predefined baseline severity scale such as a Mayo Endoscopic Subscore (MES) scale.

The invention relates to the technical field of computer-generated holographic three-dimensional display and deep learning, in particular to an unsupervised deep learning method for realizing three-dimensional holographic display. The method comprises the steps of generating a depth map corresponding to a two-dimensional image; the depth image and the two-dimensional image are spliced in the channel dimension to serve as input of a hologram encoder, and a double-U-Net cascade neural network architecture serves as the hologram encoder; angular spectrum diffraction back propagation is carried out through the generated pure phase hologram to realize three-dimensional scene discretization reconstruction, a reconstructed image with a specified depth is obtained, a depth map is uniformly quantized to obtain a plurality of binary masks, loss calculation is carried out on the reconstructed image and a target image superposed with the corresponding depth binary masks, network parameter optimization is carried out, and a target image with the depth corresponding to the target image is obtained. And when the training of the double U-Net cascade neural network architecture is converged, the training stage is ended. According to the method, high-quality three-dimensional hologram reconstruction is realized through layered angular spectrum propagation, and the method has relatively high precision and detail reduction capability.

The invention discloses a power grid safety enhanced intelligent scheduling method and device based on twinborn adversarial imitation learning and a storage medium, and belongs to the technical field of power scheduling. The method comprises the following steps: acquiring current real-time state information of a power grid system; inputting the real-time state information into a pre-trained scheduling strategy agent to generate a corresponding real-time scheduling action; and judging whether the real-time scheduling action in the current state is an expert-level action or not based on a state action data pair pool formed by the scheduling strategy agent in a training stage, and outputting a final scheduling strategy to a real environment according to a judgment result. Through real-time data analysis, intelligent decision and expert verification, the method can automatically adapt to the complexity and dynamic nature of the power grid, not only realizes efficient, safe and stable operation of the power grid, but also provides powerful support for intelligent management of the power grid.

The embodiment of the invention discloses a flight path data analysis method based on a depth auto-encoder and a generative adversarial network, relates to the technical field of air traffic management, and adopts a cubic spline interpolation method to enable each flight path to have the same length; a deep auto-encoder (DAE) and a generative adversarial network (GAN) are used as a basic framework to establish a flight trajectory data analysis framework, and two tasks of trajectory anomaly detection and flow pattern recognition are synchronously executed; in the pre-training stage, on the basis of DAE, a discriminator is integrated to establish an abnormal trajectory detection task, and an abnormal score function is formulated in combination with reconstruction loss and discriminator loss; in the fine tuning stage, a Gaussian mixture model (GMM) is adopted for low-dimensional representation of the trajectory in a potential space output by an encoder to establish a flow pattern recognition task, and in order to enhance clustering, a loss function is constructed in combination with clustering distribution enhancement. The method is suitable for civil aviation air traffic management.

The invention relates to the technical field of computer vision, in particular to a reloading pedestrian re-identification method based on a visual language pre-training model. The method comprises the following steps: a training stage: inputting an image to obtain a clothing mask image, and generating clothing irrelevant / relevant prompts; text encoder parameters are fixed, prompt weights are optimized, text prompts are input into an encoder to obtain features, and a classifier is constructed to achieve image-text alignment through cross entropy loss; using a visual encoder to extract mask pattern features, and constraining a class center Euclidean distance to realize image-image alignment; stripping clothes characteristics: extracting clothes area characteristics and corresponding text characteristics, optimizing by a classifier, and introducing orthogonal loss to decouple clothes correlation; in the reasoning stage, a query image is input into a trained image encoder to extract features, and cosine similarity ranking and result returning are calculated according to the features of the image library. According to the technical scheme, the recognition accuracy of the pedestrian re-recognition method under the condition of pedestrian clothing change can be improved.

The invention discloses a complex ship license plate identification method, system and device based on mask reconstruction and semantic reinforcement learning. According to the method, through two innovative modes of ship board mask reconstruction pre-training and vision-language cross-modal semantic enhancement, the complex spatial position relation of ship board characters can be effectively learned, and information supplementation can be carried out on ship boards with visual signal loss through semantic information. Meanwhile, the two innovations serve as a ship license plate recognition pre-training mode, configuration is only needed in the training stage of the ship license plate recognition deep neural network model, configuration is not needed in the online testing stage once training of the model is completed, and therefore extra ship license plate recognition calculated amount cannot be brought. The method has good coping and recognition capabilities for complex conditions such as multi-line texts, fuzzy texts, shielding texts, vertical texts and inverted-sequence texts in the ship sign.

Systems and methods for natural language processing are described. One or more aspects of a method, apparatus, and non-transitory computer readable medium include receiving a text phrase; encoding the text phrase using an encoder to obtain a hidden representation of the text phrase, wherein the encoder is trained during a first training phrase using self-supervised learning based on a first contrastive loss and during a second training phrase using supervised learning based on a second contrastive learning loss; identifying an intent of the text phrase from a predetermined set of intent labels using a classification network, wherein the classification network is jointly trained with the encoder in the second training phase; and generating a response to the text phrase based on the intent.

The invention provides a bidirectional DC / DC converter parameter identification method based on physical information machine learning, and relates to the technical field of parameter identification. The method comprises the steps of firstly completing establishment of a system dynamic model of the bidirectional DC / DC converter, secondly utilizing an implicit Runge-Kutta method to construct a relationship between a data intermediate state and an initial state as well as a relationship between the data intermediate state and an end state, applying the relationship to the system dynamic model, then inputting historical training data into a deep neural network to obtain the intermediate state of the data, and finally establishing a system dynamic model of the bidirectional DC / DC converter. And substituting the physical model as an input to realize seamless coupling of the physical model into training of the deep neural network. And then the trained PINN is used to realize online parameter identification. The hybrid design can seamlessly combine the data of the BDDC physical model, and effectively modulate the training stage, so that the training stage is forced to be executed to the underlying physical principle and constraint of the converter, and the required training data is greatly reduced.

The invention relates to the field of intelligent automatic driving, in particular to a multi-vehicle cooperative controllable confrontation test method based on a diffusion model. Comprising the following steps: S1, a diffusion model training stage: training a diffusion model based on real driving data, and learning vehicle behavior distribution through forward noise addition and reverse denoising to obtain fixed model parameters; s2, scene and multi-vehicle space-time modeling: fusing the road map, the lane topology and the vehicle state information, and constructing a space-time scene representation of multi-vehicle interaction as input of diffusion generation; s3, a diffusion generation mechanism based on adversarial guidance; s4, performing partial diffusion control and diversified confrontation generation; and S5, evaluating the authenticity and controllability of the multi-agent confrontation scene. Compared with the prior art, the method has the advantage that the authenticity, controllability and closed-loop consistency of multi-vehicle cooperative confrontation scene generation are obviously improved.

The invention provides a snakelike robot control method based on hierarchical reinforcement learning in a highly limited environment, and the method specifically comprises the steps: building a highly limited complex terrain simulation scene, and determining a task to be executed; a snake-shaped robot simulation model is established, and modeling is conducted on the structure, joints and kinetic parameters of the snake-shaped robot; a hierarchical control structure comprising a high-layer strategy network and a low-layer strategy network is designed based on a multi-layer perceptron and is respectively used for generating a global navigation sub-target and controlling specific actions; defining a state space, an action space and a reward function; in the training stage, a PPO algorithm is adopted to optimize the high-layer strategy and the low-layer strategy respectively, repeated tests are conducted through a simulation environment, strategy convergence is guided through a reward function, and finally the robot completes path planning and motion control only by depending on the motion state of the robot and obtaining the target position. Autonomous navigation and obstacle avoidance of the snakelike robot are realized, and the environmental adaptability of the snakelike robot is enhanced.

The invention provides an unsupervised industrial anomaly detection method based on an improved full-convolution cross-scale flow network. The method comprises the steps of performing data preprocessing on an industrial image data set; industrial image data is used as input, a pre-trained visual backbone network is used for extracting multi-scale features, a resolution-perceived channel attention module is used for enhancing expression of the multi-scale features, and an enhanced multi-scale feature map is obtained; performing multilayer reversible transformation by taking the enhanced multi-scale feature map as input and taking an ICSF-Net model based on hierarchical attention and expansion convolution as a cross-scale normalized flow network, and modeling multi-scale feature distribution; in the training stage, optimizing and updating ICSF-Net model parameters by maximizing the log likelihood of a normal sample under potential Gaussian distribution based on multi-scale feature distribution and combining an LSGR mechanism; in the reasoning stage, probability density estimation is carried out based on multi-scale feature distribution, an abnormal score graph is generated, and defect detection and positioning are achieved. According to the invention, the accuracy, robustness and pixel-level positioning precision of industrial product defect detection are improved.

Disclosed herein are systems, devices, and methods for improving balancing in neural networks with at least one sparse mixture of experts (sMoE) layer. In some aspects, such improvements can be made during the pre-training phase of the neural network. For example, a method of pre-training such a neural network can comprise calculating a new non-differentiable component of an auxiliary loss function, multiplying the new non-differentiable component in an element-by-element manner by a differentiable component of the auxiliary loss function and summing over its dimensions to produce a raw penalty score, and multiplying the raw penalty score by a scaling factor to produce a balancing auxiliary loss.

Disclosed in the present invention is a temporal information enhancement-based method for 3D medical image segmentation, belonging to the field of medical image segmentation. The method provides a circle transformer module for extraction and fusion of temporal information, and uses temporal input to improve the training effect of a deep learning model, thereby effectively eliminating interference of similar features and blurred images. In a training phase, an input sample is a temporal sequence, the model training effect is enhanced by extracting temporal information, and segmentation results before and after the combination of temporal information are both constrained, so that the model is no longer temporally dependent. In comparison with a training method in which a single sample is input, the present invention can improve the accuracy of an encoder-decoder structure-based segmentation model without costs. In an application phase, only a single frame of 3D image needs to be input, and no sequence needs to be used as an input, resulting in a more flexible application mode.

The invention provides a storage battery health state assessment method fusing ultrasonic data and hybrid machine learning, and belongs to the technical field of storage battery health state assessment. The method focuses on performance comparison of an ANN model and a GWO-ANN model in storage battery SOH regression prediction, and finds that the GWO-ANN model accurately adjusts and optimizes ANN parameters by means of a grey wolf optimization algorithm, so that the storage battery health state assessment accuracy is improved. Compared with the prior art, the method has the advantages that higher prediction precision and generalization ability are shown, the R2 value is close to perfect, the error is extremely low, and the ANN model is remarkably surpassed, the HHT-GWO-ANN model is innovatively introduced, the model integrates the advantages of HHT and GWO, the calculation process is accelerated, the robustness and prediction precision of the model are enhanced, efficient and stable model construction is achieved, and the method is suitable for large-scale popularization and application. And the test performance of the method is consistent with that of the training stage, so that the practical potential of the method in storage battery SOH nondestructive testing is verified. The ultrasonic nondestructive testing method proposed by the invention shows unique advantages in SOH recognition of the storage battery, and overcomes the limitation of a traditional method.

The invention provides a large language model instruction optimization method based on constraint perception self-learning reasoning, which can be applied to the technical field of natural language processing and artificial intelligence. The method comprises the steps that in the cold start training stage, a thinking chain example generated by a pre-training language model is utilized to assist a target large language model in recognizing constraints in a training instruction set, and the constraints in the training instruction set comprise hard constraints and soft constraints; performing supervision and fine tuning on the target large language model by utilizing the thinking chain example and the training annotation data so as to enable a thinking chain generation process of the target large language model to meet a hard constraint and a soft constraint; in the self-learning training stage, a constraint satisfaction evaluation mechanism is constructed by using the hard constraint and the soft constraint, and a thinking chain generation strategy of the target large language model is optimized through a reinforcement learning algorithm based on the constraint satisfaction evaluation mechanism.

The invention discloses a zero-sample industrial defect detection method and device based on visual enhancement and a medium, and the method comprises the steps: collecting an industrial product image and a text semantic tag corresponding to the industrial product image, and constructing a training set and a test set; building a zero-sample industrial defect detection model based on visual enhancement, and training by using the training set; and inputting the test set into the trained model for reasoning and evaluation. The model is composed of a text encoder and an image encoder, and deep interaction and optimization of text and image features are realized by introducing a bidirectional cross attention mechanism, a multi-task fusion module and an image feature refining module. In the training stage, the model learns uniform defect features from part of industrial product categories; in the test stage, defect identification and positioning are carried out on the types of industrial products which are not contacted. According to the method, the dependence on manual labeling is effectively reduced, the adaptability and detection precision of the model in a complex industrial scene are improved, and the method has good generalizability and industrial deployment value.

The invention discloses a deep forged audio detection method based on attention and double features, which comprises a training stage and a testing stage, and is characterized in that training data is acquired and preprocessed in the training stage, and a double-feature deep forged audio detection model is constructed; inputting the manual acoustic features and the original waveform features of the training data into respective detection branches for model training; attention blocks are added to feature extraction modules of the two models respectively, each attention block is composed of spatial local attention and spectrum global attention, the spatial local attention is used for focusing a local sensitive area of the features, the spectrum global attention is used for capturing cross-channel correlation between the features, namely, the long-range dependency relationship of the captured features, and the spatial local attention is used for focusing the local sensitive area of the features. Splicing, fusing and outputting the images, and inputting the images into a classification module for true and false classification; and finally, the detection model is trained in the test stage, authenticity detection of real voice and synthetic voice is completed, and high-quality deep forged audio detection is realized.

The invention provides a VAE-ALSTM-based bridge structure state anomaly detection method and system, and the method comprises the steps: carrying out the standardization and sliding window segmentation of vibration monitoring data, extracting potential features through a variational auto-encoder, and carrying out the time sequence prediction and reconstruction in combination with an attention-enhanced long-short-term memory network, thereby achieving the detection of the abnormal state of a bridge structure. Fusing the prediction error and the reconstruction error to generate an abnormal score; a threshold value is automatically set through quartile distance statistics, and self-adaptive judgment under different bridge types is achieved; when the score crosses the boundary, real-time alarm is triggered; the system correspondingly comprises a data preprocessing and sample construction module, a VAE-ALSTM model construction module, a training stage prediction and reconstruction module, an error calculation and anomaly scoring module, a threshold setting module and an anomaly judgment and alarm module. The method and the system do not need manual threshold parameter adjustment, are high in precision and good in real-time performance, and can be widely applied to the fields of bridge health monitoring, operation and maintenance early warning and the like.

Methods, systems, and computer-readable storage media for providing, for a set of ML models, a set of training metrics determined using test data during a training phase, providing, for a production-use ML model, a set of inference metrics based on predictions generated by the production-use ML model, generating, by a prompt generator, a set of few-shot examples using the set of training metrics and the set of inference metrics, inputting, by the prompt generator, the set of few-shot examples to a LLM as prompts, transmitting, to the LLM a query, displaying, to a user, a recommendation that is received from the LLM and responsive to the query, receiving input from a user indicating a user-selected ML model responsive to the recommendation, and deploying a user-selected ML model to an inference runtime for production use.

The invention discloses a remote sensing image semantic segmentation method, system and device and a medium, and the method comprises the following steps: obtaining remote sensing building data, and inputting the data into a pre-trained semantic segmentation network, so as to achieve the precise segmentation of a building in a remote sensing image. The semantic segmentation network includes an encoder module and a decoder module for adaptive context selection based on structure priori guidance. In the training stage, a special remote sensing building data set is utilized to train the network so as to optimize the segmentation performance of the network. According to the method, by introducing structure prior information and an adaptive context selection mechanism, the accuracy and efficiency of building segmentation in the remote sensing image are effectively improved, and the problem that remote sensing buildings are difficult to distinguish when pixel visual evidences are weak or the remote sensing buildings belong to small space structure objects is solved.

The invention discloses an infrared super-resolution imaging method and an infrared super-resolution imaging system based on nerve compression self-coding, in a training stage, a physical imaging process is modeled into an optical degradation encoder according to a compression sensing theory, and a fuzzy operation is combined by introducing a learnable binarization mask, so that a super-resolution image is obtained. Simulating a sparse coding process of compressed sensing to obtain sparse representation of the input image; restoring the sparse representation into a high-resolution image by using a CNN-based super-resolution decoder; in the reasoning stage, the optical degradation encoder is replaced by a real sampling process, and the optimized binarization mask is used as a mask set in the spatial light modulator and is finally projected to an image collected by a sensor; and the trained CNN model is also stored and is used for carrying out a super-resolution task on a really captured coded image. According to the invention, an efficient and reliable solution is provided for super-resolution reconstruction of the low-resolution infrared image.

The invention discloses a three-dimensional point cloud geometric information compression method based on implicit neural representation, and the method comprises the steps: constructing a trunk structure of an implicit neural network through a plurality of sine representation network layers which are connected in series, and introducing a variable-scale position coding mechanism on this basis, the method enables a network to obtain higher geometric reduction precision while keeping a compression ratio, and comprises the following steps: (1) inputting space coordinates of divided voxels into a position coding module with adjustable scale parameters; (2) feeding a coding result into an implicit neural network constructed by a network layer based on sine representation, and outputting the occupancy probability of the voxel through an activation function; (3) in a training stage, the model continuously optimizes parameters, so that the output probability distribution is highly consistent with a real occupied label; (4) after model training is completed, a method of combining an AdaRound second-order quantization optimization strategy and quantization perception training is introduced, network weight is finely adjusted, and quantization errors are reduced; (5) in a reasoning stage, judging whether the voxel is occupied or not according to a preset threshold value, and when the prediction probability exceeds the threshold value, regarding the voxel as occupied; and (6) all voxels judged to be occupied are aggregated, and reconstruction of the geometric structure of the point cloud is completed.

Embodiments described herein provide systems and methods for a partially supervised training model for online action detection. Specifically, the online action detection framework may include two modules that are trained jointly—a Temporal Proposal Generator (TPG) and an Online Action Recognizer (OAR). In the training phase, OAR performs both online per-frame action recognition and start point detection. At the same time, TPG generates class-wise temporal action proposals serving as noisy supervisions for OAR. TPG is then optimized with the video-level annotations. In this way, the online action detection framework can be trained with video-category labels only without pre-annotated segment-level boundary labels.

A system for training and deploying a sanitizer model of a large language model (LLM) to sanitize the harmful language text. The system generates paired data sets for training using LLM techniques. Training includes a first training phase using a paired dataset to predict the benign language text in the paired dataset based on loss of information computed between the benign language text in the paired dataset and the sanitized language text. Training also includes a second training phase using a harm model that computes a harm score of the LLM prediction by adjusting the trained sanitizing step to reduce the harm score for subsequent LLM responses.

The invention discloses a logging-while-drilling forepoling stratum parameter inversion method based on a deep neural network. A training stage comprises a logging-while-drilling azimuth electromagnetic wave forepoling model construction step, a data set construction step, a data preprocessing step, a resistivity inversion neural network training step and a stratum boundary inversion neural network training step. In the testing stage, the received logging response is input into the trained resistivity inversion neural network and the boundary inversion neural network, the resistivity inversion neural network outputs the predicted resistivity to the boundary inversion neural network, and the boundary inversion neural network outputs the predicted stratum boundary position; and finally, drawing a stratum image according to the predicted resistivity and the stratum boundary position. According to the invention, a formation parameter inversion system composed of the resistivity inversion neural network and the formation boundary inversion neural network based on the resistivity prior is constructed, so that the formation parameters can be inversed in real time with high precision in the logging-while-drilling forepoling process.

A method includes, in a processor, receiving example representations of geometrical shapes of a given type of organ. In a training phase, a neural network model is trained using the example representations. In a modeling phase, the trained neural network model is applied to a set of location measurements acquired in an organ of the given type, to produce a three-dimensional model of the organ.