1916 results about "Ground truth" patented technology

The technology disclosed assigns quality scores to bases called by a neural network-based base caller by (i) quantizing classification scores of predicted base calls produced by the neural network-based base caller in response to processing training data during training, (ii) selecting a set of quantized classification scores, (iii) for each quantized classification score in the set, determining a base calling error rate by comparing its predicted base calls to corresponding ground truth base calls, (iv) determining a fit between the quantized classification scores and their base calling error rates, and (v) correlating the quality scores to the quantized classification scores based on the fit.

The present invention provides a system and method for causality-augmented generative intelligence capable of autonomously discovering non-obvious actionable insights from heterogeneous and multimodal data sources. The system integrates a data ingestion unit for semantic and temporal harmonization of structured and unstructured datasets, a causal inference processor for constructing a dynamically evolving directed causal knowledge representation using perturbation-based validation, a latent representation processor that combines multimodal semantic embeddings with causal parameters to generate fused latent vectors, and a generative insight processor utilizing causally constrained generative reasoning to synthesize hypotheses anchored to verified cause-effect dependencies. A validation processor performs counterfactual assessment and observational verification to ensure retention of only those insights that remain consistent with causal ground truth.

Systems and methods for assessing machine learning model fairness are provided. A graphical user interface (GUI) is rendered in a web browser. Input data from the web browser is obtained. A first input produced by a first GUI control is received to identify a prediction column in the input data. A second input produced by a second GUI control is received to identify a ground truth column. A third input produced by a third GUI control is received to identify a sensitive attribute column. A plurality of groups within a plurality of sensitive attribute values in the sensitive attribute column are automatically displayed. The system processes the input data based on the prediction column, the ground truth column and the sensitive attribute column to generate one or more algorithmic fairness analysis, which are used to display a visual report.

Described herein are systems, methods, devices, and other techniques for comprehensive and automated evaluation of digital images generated from artificial intelligence (AI) models in order to promote accurate representations of real-world content. Prompts are received at the system that are then passed to both a search engine and a generative AI model. Synthesized digital images are obtained from the generative AI model. The top-matching image from the search engine is used as a verification of the ground truth of the synthesized digital images. A realism score is generated for each synthesized digital image that characterizes the accuracy of the synthesized digital image with reference to the verification image. The realism score can be used to assist and expedite the image selection process, as well as serve as input to fine-tune the performance of generative models.

The present invention relates to a system and method for cross-domain predictive modeling using Bedrock-based foundation models and blockchain-anchored data. The invention integrates large-scale foundation model reasoning with distributed ledger-based data provenance to enable verifiable, secure, and explainable predictive analytics across heterogeneous domains such as finance, healthcare, logistics, and environmental systems. The system comprises a data ingestion unit for receiving and normalizing multi-domain datasets, a blockchain anchoring unit for generating cryptographic hashes and recording data provenance into a distributed ledger, a cross-domain harmonization processor for aligning heterogeneous feature representations into a unified latent space, a foundation model processor configured to execute Bedrock-based predictive inference with adaptive domain contextualization, a verification processor for validating predictions against blockchain-anchored ground truths, and a governance processor for maintaining immutable audit trails of model evolution.

Systems and methods are disclosed that relate to training a machine learning system using simulated objects. A simulated object may be generated based at least on extracting at least a portion of the simulated object from a simulated textured representation. Further, the simulated anomaly object may be combined with an existing image to generate a training image. One or more parameters of a machine learning model may be updated based at least on the training image and ground truth data corresponding to the training image.

In various examples, systems and methods for pose detection model training for predicting three-dimensional pose estimates using two-dimensional image data are provided. The occupant pose detection model may be trained using multi-view image sensor training data that includes image frames that capture a pose of a training subject within a machine interior using multiple synchronized optical image sensors placed around the machine interior that produce a set of captured image frames of the training subject from different viewpoints. Based on the multi-view image sensor training data, the occupant pose detection model may generate a set of individual, predicted 3D pose estimates for the training subject from a captured image frame from each of the respective optical image sensors. To adjust the occupant pose detection model during training, a loss feedback may be generated that comprises a pose alignment loss, a pose depth loss, and / or a ground truth kinematic loss.

Computerized systems and methods for training a trajectory prediction model for autonomous driving vehicles. The systems and methods receive agent dynamics data, high-dimensional (HD) map data; a training data set; and a plurality of meta paths through the HIN representing lane transitions, each meta path comprising a sequence of lane nodes and edges. Scene encoding is performed on the agent dynamics data and the HD map data to produce a directed Heterogeneous Information Network (HIN) as a graph comprising nodes and edges. The trajectory prediction model is trained based on a comparison between a prediction, by the trajectory prediction model, of a positive or negative presence of the meta paths between nodes and meta path ground truth obtained from the training data set and between motion trajectory predictions, by the trajectory prediction model, and motion trajectory ground truth obtained from the training data set.

In various examples, image-based three-dimensional occupant assessment for in-cabin monitoring systems and applications are provided. An evaluation function may determine a 3D representation of an occupant of a machine by evaluating sensor data comprising an image frame from an optical image sensor. The 3D representation may comprise at least one characteristic representative of a size of the occupant, (e.g., a 3D pose and / or 3D shape), which may be used to derive other characteristics such as, but not limited to weight, height, and / or age. A first processing path may generate a representation of one or more features corresponding to at least a portion of the occupant based on optical image data, and a second processing path may determine a depth corresponding to the one or more features based on depth data derived from the optical image data and ground truth depth data corresponding to the interior of the machine.

A first neural network is trained to generate a ground truth using a small set of example images that illustrate the goal ground truth output images, which can be full-body images of people in an AR style. The first neural network is used to generate ground truth output images from random input images. Example methods of the first neural network include determining poses in input images, changing values of pixels within areas of the input images, inputting the poses, the areas of the changed input images, and a text prompt describing the input images, into a neural network, to generate output images. The methods further include determining losses between the output images and the input images and updating weights of the neural network based on the losses. A second neural network is then trained using the generated ground truth. And, an application is generated that uses the second neural network.

Communication devices and methods for determining channel state information (CSI) report based on artificial intelligence (AI) / machine learning (ML) are provided. The method for determining CSI report based on AI / MI performed by a communication device includes determining, by the communication device, one or more CSI reports according to an AI / ML based CSI feedback, wherein each of the one or more CSI reports contains an output of an auto-encoder, a compression ratio, a rank indicator, quantization levels, a ground truth of an enhanced CSI feedback, and / or an ML model monitoring outcome, and determining, by the communication device, priority rules for the CSI reports according to the AI / ML based CSI feedback.

A method and a Large Language Model (LLM) evaluation system provides an end-to-end evaluation of LLM, which includes evaluating both input prompts and output prompt responses, wherein the evaluation includes assessing a plurality of input and output characteristics that encompasses both quality and quantity. Each of the plurality of input characteristics are assigned with a corresponding normalized score by employing one or more statistical techniques to derive a composite health score for the input prompts. Evaluation further comprises evaluating output prompt responses in both absence and presence of the ground truth. Upon evaluating both input prompts and output prompt responses, a final aggregated health score for the LLM is computed by a scorer module employing threshold based statistical techniques that considers input prompt health and output prompt response health, wherein the aggregated health score is generated based on the granular scores of each characteristic.

Various embodiments include methods for generating image datasets for training an artificial intelligence machine learning (AI / ML) optical character recognition (OCR) model. Image processing may be performed on a plurality of roadway images to identify street signs within the images and generate a dataset of sign images categorized into sign variants of the same shape, color, pictogram, and characters. An OCR model may process sign images to obtain OCR results for images of each sign variant. An aggregation process may be performed on the OCR results for all sign images within each sign variant to identify a ground truth OCR result for each sign variant. The ground truth OCR result may be used to automatically label all sign images of each sign variant to produce an OCR model training dataset. The produced training dataset may then be used to retrain the initial AI / ML OCR model and / or train other AI / ML OCR models.

The described aspects and implementations enable training and deploying of accurate one-shot models capable of predicting trajectories of vehicles and other objects in driving environments. The disclosed techniques include, in one implementation, obtaining training data that includes a training input representative of a driving environment of a vehicle and one or more ground truth trajectories associated with a forecasted motion of the vehicle within the driving environment. The one or more ground truth trajectories are generated by a teacher model using the training input. The techniques further include training, using the training data, a student model to predict one or more trajectories of the vehicle and / or objects in the driving environment of the vehicle.

The present invention provides a method for training a complex machine learning model based on a dataset that comprises datapoints and ground truth labels for at least some of the datapoints of the dataset, wherein the datapoints comprise images, the method comprising:using a reduced-complexity machine learning model to predict pseudo labels for the dataset, andtraining the complex machine learning model using an extended dataset that comprises the ground truth labels and the pseudo labels as output.

Evaluating and improving information retrieval in question-answering systems is an area of importance in machine learning growth. Retrieval components in a retrieval-augmented generation (RAG) question answering system enable machine learning models to provide more accurate and reliable answers to questions. Systems for retriever evaluation involve processing queries in comparison to reference documents. The system first retrieves documents deemed relevant, then generates a first answer based on them. A second answer is generated using a set of documents that includes ground truth documents known to be relevant to the query. By analyzing semantic overlap between these responses, a quantitative evaluation of the retrieval component is obtained. This evaluation then informs automatic modifications to retrieval parameters, enhancing future document selection and response accuracy.

In various examples, circadian rhythm-based data augmentation for drowsiness detection systems and applications are provided. Embodiments described herein may produce an estimated circadian rhythm for a test subject and / or vehicle driver or other machine operator or occupant, and use the pattern of that circadian rhythm to correct, confirm, calibrate, or otherwise augment drowsiness assessments derived from video image data. The position of a person in the context of their process C circadian cycle may be used as indication of their level of drowsiness. An estimated process C circadian cycle may be used to generate more accurate ground truth training data for training machine learning models, and may be used by real-time, in-vehicle drowsiness detection systems that infer driver drowsiness levels based on captured images. In various embodiments, a circadian rhythm drowsiness estimate may be used to correct, calibrate, augment, and / or replace a drowsiness score predicted by a machine learning model.

Generating high-quality images of logic-rich three-dimensional (3D) scenes from natural language text prompts is challenging, because the task involves complex reasoning and spatial understanding. A reinforcement learning framework utilizing a ground truth data set can be implemented to train a policy network. The policy network can learn optimal parameters to refine a text prompt to obtain a modified text prompt. The modified text prompt can be used to obtain a three-dimensional scene, and the three-dimensional scene can be rendered and projected to obtain a rendered image. The framework involves an action agent for text modification, a generation agent to produce rendered images, and a reward agent to evaluate the rendered images. The loss function used in training the policy network optimizes visual accuracy and quality of the rendered images and semantic alignment between the rendered images and the text prompt.

The invention relates to the field of industrial visual inspection, and provides an automatic test performance benchmark construction method which comprises the following steps: dynamically generating ROI (Region of Interest) parameters; performing pixel cutting on the target area of the production line according to the dynamically generated ROI parameters to generate a high-speed image data stream, and aligning the image acquisition moment with the mechanical motion phase of the production line; selecting a matched visual detection model based on target features in the high-speed image data stream, and outputting a defect detection result by the matched visual detection model; according to defect distribution parameters of a preset test scene, the simulation defect features are embedded into the high-speed image data stream, and a mixed test data stream is generated; performing multi-device time sequence alignment on the mixed test data stream and the defect detection result, and packaging into a standardized data set containing a unified time reference; and analyzing the standardized data set, comparing the simulated defect truth value label with a defect detection result, calculating a multi-dimensional performance index of the visual inspection system, and generating a test report containing parameter optimization suggestions.

Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of whether there is a hazard at the 3D location corresponding to each initial transformer query, the shape of the hazard, and / or its class. These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations (e.g., obstacle avoidance, lane keeping, lane changing, merging, splitting, etc.). Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected static scene points, navigable space boundaries, or detected hazard objects. Accordingly, hazards such as road debris and other obstacles may be detected and ground truth data may be generated for a variety of sensing tasks.

A method includes obtaining a set of training queries that each specify a corresponding operation to perform and include a corresponding plurality of speech recognition hypotheses that each represent a corresponding candidate transcription of the training query, and a corresponding ground-truth transcription of the training query. For each training query, the method includes processing, using an encoder of a neural semantic parsing (NSP) model, the corresponding plurality of speech recognition hypotheses to generate a corresponding NSP embedding, processing, using a transcription decoder, the corresponding NSP embedding to generate a corresponding predicted transcription, and determining a corresponding first loss based on the corresponding predicted transcription and the corresponding ground-truth transcription. The method further includes training, based on the first losses determined for the set of training queries, the NSP model to learn how to predict user intents associated with the operations specified by the training queries.

In various examples, head pose prediction for automotive occupant sensing systems and applications is presented. The systems and methods described herein provide for a machine learning model trained using a dataset that comprises ground truth head pose data computed using a registered head model of a training subject. While operating a vehicle, one or more cameras and a depth sensor capture synchronized images of the training subject. To compute a ground truth 3D head pose, angular deviations between a 3D point cloud and the registered head model may be computed to obtain a 3D ground truth head pose measurement. Using an extrinsic calibration transform, the head pose measurement may be mapped into the sensor coordinate frame. Training samples may be produced for training the machine learning model that comprise an optical image frame and the head pose measurement transposed into the frame of reference for that optical image frame.

Techniques for training for and performing abstractive text summarization are disclosed. Such techniques include, in some embodiments, obtaining textual content, and generating a reconstruction of the textual content using a trained language model, the reconstructed textual content comprising an abstractive summary of the textual content generated based on relative importance parameters associated with respective portions of the textual content. In some cases, the trained language model includes a neural network language model that has been trained by identifying a plurality of discrete portions of training textual content, receiving the plurality of discrete portions of the training textual content as input to the language model, and predicting relative importance parameters associated with respective ones of the plurality of discrete portions of the training textual content, the relative importance parameters each being based at least on one or more linguistic similarity measures with respect to a ground truth.

The invention provides an optically guided SAR target detection method based on frequency domain enhancement and dynamic masks, which comprises the following steps: acquiring an SAR image and a corresponding optical image, and taking a pre-trained optical detection model as a teacher model and a to-be-trained SAR model as a student model; optical and SAR images are respectively input into corresponding models to generate feature maps, SAR features are decomposed into low-frequency global and high-frequency detail components through wavelet transform, and noise is suppressed and target features are enhanced through multi-scale convolution and a self-attention mechanism; generating a target area mask through a dynamic mask module based on the ground truth value; inputting the enhanced SAR features and the optical features into an optical detection head of a teacher model, and calculating loss by using a cross-detection-head distillation strategy; and through combination of detection loss and distillation loss, the student model is subjected to back propagation training until convergence, so that the SAR target detection precision and real-time performance in a complex scene can be effectively improved.

Methods and systems for prompting a Large Language Model (LLM) with a set of text data outside pre-inference trained categories and a test prompt for an initial parameter which has a known ground truth, calculating an uncertainty of an LLM's output, selecting another LLM model parameter and calculating the total uncertainty of the LLM's output with the other LLM model parameter. The methods and systems further include prompting the LLM with another test prompt, with the initial LLM parameter and the other LLM parameter, and calculating the total uncertainty of the LLM's output for initial LLM model parameter and the other LLM model parameter, decomposing the total uncertainty of the LLM into Aleatoric Uncertainty (AU) and Epistemic Uncertainty (EU) components, and rating the total uncertainty of the LLM, using the decomposed total uncertainty as a metric.

Embodiments of this disclosure can provide a system and method for training a perception model to perform an autonomous driving task. During operation, the system can obtain labeled training data comprising images captured by multiple cameras mounted at different locations on a vehicle, and the perception model can generate, in parallel, a prediction output associated with the task and a confidence score based on the labeled training data. The confidence score can indicate a level of uncertainty associated with the prediction output. The system can generate an uncertainty-weighted prediction based on ground truth indicated by the labeled training data, the prediction output, and the confidence score; compute a loss function based on the uncertainty-weighted prediction; and update the perception model based on the loss function.

In various examples, to support training a deep neural network (DNN) to predict a dense representation of a 3D surface structure of interest, a training dataset is generated using a simulated environment. For example, a simulation may be run to simulate a virtual world or environment, render frames of virtual sensor data (e.g., images), and generate corresponding depth maps and segmentation masks (identifying a component of the simulated environment such as a road). To generate input training data, 3D structure estimation may be performed on a rendered frame to generate a representation of a 3D surface structure of the road. To generate corresponding ground truth training data, a corresponding depth map and segmentation mask may be used to generate a dense representation of the 3D surface structure.

Techniques described herein relate to training and executing machine learning models configured to determine injury probabilities based on vehicle accident data. In some cases, a decision tree model may be constructed including various branching criteria based on particular vehicle accident data and injury ground truth data, including leaf nodes storing corresponding injury probabilities. A model execution component may execute the trained models to determine the probability of potential injuries associated with vehicle accidents. Based on the potential injury probabilities, a vehicle accident analysis system may identify target computer systems and / or target processes to be initiated on the target systems, based on the injury probabilities. In some example, the model execution architecture may be implemented using an event-driven system and / or cloud-based data storage and services to receive, store, and process data events associated with individual vehicle accidents.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modifies parameters of a fused feature extractor. In particular, the disclosed systems generate inferred masks from digital images and digital text prompts using a fused feature extractor. Furthermore, the disclosed systems identify a subset of the inferred masks that satisfy a validity threshold. Moreover, the disclosed systems generate an augmented training set by combining the subset of the inferred masks with a training set that includes the ground truth masks. Further, the disclosed systems generate object mask predictions from the augmented training set and determine ground truth and pseudo measures of loss by comparing the object mask predictions with the inferred masks and the ground truth masks. From the ground truth and pseudo measures of loss, the disclosed systems modify parameters of the fused feature extractors.

A method and Graphic User Interface (GUI) system of data analysis for training a discriminative machine learning model are provided. One or more sets of data are provided to a large language model (LLM) for a predefined task with or without prompts provided by a user; the results output by the LLM is compared with a set of initial human-supplied ground truth data generated by the user to produce performance metrics, the user may provide or update one or more prompts for the LLM based on the performance metrics till the performance metrics reach a threshold. The set of initial human-supplied ground truth data and the results of the LLM can be used to train a discriminative machine learning model.