Abstract: The disclosure herein describes generating input key-standard key mappings for a form. A set of input key-value pairs are received, and a subset of candidate form types are determined from a set of form types using the input key-value pairs. A set of standard keys associated with the determined subset of candidate form types are obtained. A set of input key-standard key pairs are generated using the set of input key-value pairs and the obtained set of standard keys and the set of input key-standard key pairs are narrowed using a narrowing rule. Ranking scores for each input key-standard key pair of the narrowed set of input key-standard key pairs are generated. Each input key of the set of input key-vale pairs is mapped to a standard key of the set of standard keys using at least the generated ranking scores of the narrowed set of input key-standard key pairs.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve performance of an artificial intelligence based (AI-based) model on datasets having different distributions. An example apparatus includes interface circuitry to access data, computer readable instructions, and processor circuitry to at least one of instantiate or execute the computer readable instructions to implement adversarial evaluation circuitry, convolution circuitry, and output control circuitry. The example adversarial evaluation circuitry is to determine whether the data is to be processed as adversarial data. The example convolution circuitry is to, based on whether the data is to be processed as the adversarial data, determine a convolution of an input tensor and (1) a parameter tensor corresponding to a layer of the AI-based model or (2) a noisy parameter tensor generated based on the parameter tensor. The example output control circuitry is to output a classification of the data based on the convolution.

Abstract: The present disclosure is related to techniques for optimizing artificial intelligence (AI) and/or machine learning (ML) models to reduce resource consumption while maintaining or improving AI/ML model performance. A sparse distillation framework (SDF) is provided for producing a class of parameter and compute efficient AI/ML models suitable for resource constrained applications. The SDF simultaneously distills knowledge from a compute heavy teacher model while also pruning a student model in a single pass of training, thereby reducing training and tuning times considerably. A self-attention mechanism may also replace CNNs or convolutional layers of a CNN to have better translational equivariance. Other embodiments may be described and/or claimed.

Abstract: The present disclosure is related to machine learning model swap (MLMS) framework for that selects and interchanges machine learning (ML) models in an energy and communication efficient way while adapting the ML models to real time changes in system constraints. The MLMS framework includes an ML model search strategy that can flexibly adapt ML models for a wide variety of compute system and/or environmental changes. Energy and communication efficiency is achieved by using a similarity-based ML model selection process, which selects a replacement ML model that has the most overlap in pre-trained parameters from a currently deployed ML model to minimize memory write operation overhead. Other embodiments may be described and/or claimed.