Abstract: Features extracted from one or more layers of a trained deep neural network (DNN) are used to detect out-of-distribution (OOD) data, such as anomalies. An OOD detection process includes transforming a feature output from a layer of the DNN from a relatively high-dimensional feature space to a lower-dimensional space, and then performing a reverse transformation back to the higher-dimensional feature space, resulting in a reconstructed feature. A feature reconstruction error is calculated based on a difference between the reconstructed feature and the original feature output from the DNN. The OOD detection process may further include calculating a score based on the feature reconstruction error and generating a visual representation of the feature reconstruction error.

Abstract: Apparatus and method for uncertainty-aware code generation using LLMs. For example, one embodiment of a method comprises: generating, by a large language model (LLM) code generator, a plurality of RTL code blocks based on a design prompt; determining syntactical similarities and semantic similarities between pairs of the RTL code blocks; arranging the RTL code blocks into a plurality of clusters based on a combination of the syntactical similarities and the semantic similarities; generating uncertainty estimates indicating levels of uncertainty associated with one or more clusters of the plurality of clusters; and determining whether to synthesize an RTL output using one or more of the RTL code blocks based on the uncertainty estimates.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate continuous learning. An example apparatus includes a trainer to train a first Bayesian neural network (BNN) and a second BNN, the first BNN associated with a first weight distribution and the second BNN associated with a second weight distribution. The example apparatus includes a weight determiner to determine a first sampling weight associated with the first BNN and a second sampling weight associated with the second BNN. The example apparatus includes a network sampler to sample at least one of the first weight distribution or the second weight distribution based on a pseudo-random number, the first sampling weight, and the second sampling weight. The example apparatus includes an inference controller to generate an ensemble weight distribution based on the sample.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate knowledge sharing among neural networks. An example apparatus includes a trainer to train, at a first computing system, a first Bayesian Neural Network (BNN) on a first subset of training data to generate a first weight distribution, and train, at a second computing system, a second BNN on a second subset of the training data to generate a second weight distribution, the second subset of the training data different from the first subset of training data. The example apparatus includes a knowledge sharing controller to generate a third BNN based on the first weight distribution and the second weight distribution.

Abstract: Methods, apparatus, systems, and articles of manufacture providing a Bayesian compute unit with reconfigurable sampler and methods and apparatus to operate the same are disclosed. An example apparatus includes a number generator to generate a sequence of numbers; a multiplier to generate a plurality of products by multiplying respective numbers of the sequence of the numbers by a variance value; and an adder to generate a plurality of weights by adding a mean value to the plurality of products, the plurality of weights corresponding to a single probability distribution.

Abstract: Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.

Abstract: A method for authenticating features reported by a vehicle includes receiving, from a network, a map of an area with confidence weights corresponding to each feature on the map and/or a list of trusted users; upon the vehicle entering the area, checking whether the vehicle is on the list of trusted users; and checking features reported from the vehicle and matching the features to the map of the area.

Abstract: A communication device for a vehicle to communicate features about the vehicle's environment includes one or more processors configured to receive a communication from another device, wherein the communication includes a global reference coordinate system for an area covered by the other device and a number of allowed transmissions to be sent from the vehicle; transform stored data about the vehicle's environment based on the global reference coordinate system; divide the transformed stored data into a plurality of subsets of data; and select one or more subsets of data from the plurality of subsets for transmission according to the number of allowed transmissions.

Abstract: Features extracted from one or more layers of a trained deep neural network (DNN) are used to detect out-of-distribution (OOD) data, such as anomalies. An OOD detection process includes transforming a feature output from a layer of the DNN from a relatively high-dimensional feature space to a lower-dimensional space, and then performing a reverse transformation back to the higher-dimensional feature space, resulting in a reconstructed feature. A feature reconstruction error is calculated based on a difference between the reconstructed feature and the original feature output from the DNN. The OOD detection process may further include calculating a score based on the feature reconstruction error and generating a visual representation of the feature reconstruction error.

Abstract: Disclosed is an example solution to perform source-free active adaptation to distributional shifts for machine learning. The example solution includes: interface circuitry; programmable circuitry; and instructions to cause the programmable circuitry to: perform a first training of a neural network on a baseline data set associated with a first data distribution; compare data of a shifted data set to a threshold uncertainty value, wherein the threshold uncertainty value is associated with a distributional shift between the baseline data set and the shifted data set; generate a shifted data subset including items of the shifted dataset that satisfy the threshold uncertainty value; and perform a second training of the neural network based on the shifted data subset.

Abstract: Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that calibrate error aligned uncertainty for regression and continuous structured prediction tasks/optimizations. An example apparatus includes a prediction model, at least one memory, instructions, and processor circuitry to at least one of execute or instantiate the instructions to calculate a count of samples corresponding to an accuracy-certainty classification category, calculate a trainable uncertainty calibration loss value based on the calculated count, calculate a final differentiable loss value based on the trainable uncertainty calibration loss value, and calibrate the prediction model with the final differentiable loss value.

Abstract: Methods, apparatus, systems, and articles of manufacture providing a Bayesian compute unit with reconfigurable sampler and methods and apparatus to operate the same are disclosed. An example apparatus includes a number generator to generate a sequence of numbers; a multiplier to generate a plurality of products by multiplying respective numbers of the sequence of the numbers by a variance value; and an adder to generate a plurality of weights by adding a mean value to the plurality of products, the plurality of weights corresponding to a single probability distribution.

Abstract: A communication device for a vehicle to communicate features about the vehicle's environment includes one or more processors configured to receive a communication from another device, wherein the communication includes a global reference coordinate system for an area covered by the other device and a number of allowed transmissions to be sent from the vehicle; transform stored data about the vehicle's environment based on the global reference coordinate system; divide the transformed stored data into a plurality of subsets of data; and select one or more subsets of data from the plurality of subsets for transmission according to the number of allowed transmissions.

Abstract: Vehicle navigation control systems in autonomous driving rely on the accuracy of maps which include features about a vehicle's environment so that a vehicle may safely navigate through its surrounding area. Accordingly, this disclosure provides methods and devices which implement mechanisms for communicating features observed about a vehicle's environment for use in updating maps so as to provide vehicles with accurate and “real-time” features of its surroundings while taking network resources, such as available frequency-time resources, into consideration.

Abstract: A safety system for a vehicle may include one or more processors configured to determine uncertainty data indicating uncertainty in one or more predictions from a driving model during operation of a vehicle; change or update one or more of the driving model parameters to one or more changed or updated driving model parameters based on the determined uncertainty data; and provide the one or more changed or updated driving model parameters to a control system of the vehicle for controlling the vehicle to operate in accordance with the driving model including the one or more changed or updated driving model parameters.

Abstract: Methods, apparatus, systems, and articles of manufacture providing a Bayesian compute unit with reconfigurable sampler and methods and apparatus to operate the same are disclosed. An example apparatus includes a processor element to generate (a) a first element by applying a mean value to an activation and (b) a second element by applying a variance value to a square of the activation, the mean value and the variance value corresponding to a single probability distribution; a programmable sampling unit to: generate a pseudo random number; and generate an output based on the pseudo random number, the first element, and the second element, wherein the output corresponds to the single probability distribution; and output memory to store the output.

Abstract: A human-robot collaboration system, including at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to: predict a human atomic action based on a probability density function of possible human atomic actions for performing a predefined task; and plan a motion of the robot based on the predicted human atomic action.

Abstract: Methods, systems, and apparatus to obtain well-calibrated uncertainty in probabilistic deep neural networks are disclosed. An example apparatus includes a loss function determiner to determine a differentiable accuracy versus uncertainty loss function for a machine learning model, a training controller to train the machine learning model, the training including performing an uncertainty calibration of the machine learning model using the loss function, and a post-hoc calibrator to optimize the loss function using temperature scaling to improve the uncertainty calibration of the trained machine learning model under distributional shift.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate continuous learning. An example apparatus includes a trainer to train a first Bayesian neural network (BNN) and a second BNN, the first BNN associated with a first weight distribution and the second BNN associated with a second weight distribution. The example apparatus includes a weight determiner to determine a first sampling weight associated with the first BNN and a second sampling weight associated with the second BNN. The example apparatus includes a network sampler to sample at least one of the first weight distribution or the second weight distribution based on a pseudo-random number, the first sampling weight, and the second sampling weight. The example apparatus includes an inference controller to generate an ensemble weight distribution based on the sample.