Abstract: A method includes receiving, from a first head end system (HES), a first raw file including a first set of events in a first HES specific format, and modifying the first set of events in the first raw file by a first HES adapter to create a first unified formatted file. The first unified formatted file is in a unified format, and the first HES adapter is connected to the first HES. The method further include transforming, by a core HES adapter, the first unified formatted file to a first data management formatted file according to a data management format, and consuming the first data management formatted file.

Abstract: A residual estimation with an I/O kernel (“RIO”) framework provides estimates of predictive uncertainty of neural networks, and reduces their point-prediction errors. The process captures neural network (“NN”) behavior by estimating their residuals with an I/O kernel using a modified Gaussian process (“GP”). RIO is applicable to real-world problems, and, by using a sparse GP approximation, scales well to large datasets. RIO can be applied directly to any pretrained NNs without modifications to model architecture or training pipeline.

Abstract: A residual estimation with an I/O kernel (“RIO”) framework provides estimates of predictive uncertainty of neural networks, and reduces their point-prediction errors. The process captures neural network (“NN”) behavior by estimating their residuals with an I/O kernel using a modified Gaussian process (“GP”). RIO is applicable to real-world problems, and, by using a sparse GP approximation, scales well to large datasets. RIO can be applied directly to any pretrained NNs without modifications to model architecture or training pipeline.

Abstract: A genetic loss function optimization (hereafter “GLO”) process uses a genetic algorithm to construct candidate loss functions as trees. The process takes the best candidate loss functions from this set and optimizes the coefficients thereof using covariance-matrix adaptation evolutionary strategy (hereafter “CMA-ES”), resulting in new loss functions showing substantial improvements in accuracy, convergence speed, and data requirements.

Abstract: A process for discovering optimal Generative Adversarial Networks (GAN) includes jointly optimizing the three functions of a GANs process including (i) a real component of a discriminator network's loss that is a function of D(x), wherein D(x) is the discriminator network's output for a real sample from an input dataset; (ii) a synthetic component of the discriminator network's loss that is a function of D(G(z)), wherein D(G(z)) is the discriminator network's output for a generator network's synthetic samples z from a latent distribution; and (iii) a generator network's loss which is a function of D(G(z)), with the discriminator network's total loss being the sum of components (i) and (ii).

Abstract: The present invention relates to metalearning of deep neural network (DNN) architectures and hyperparameters. Precisely, the present system and method utilizes Evolutionary Population-Based Based Training (EPBT) that interleaves the training of a DNN's weights with the metalearning of loss functions. They are parameterized using multivariate Taylor expansions that EPBT can directly optimize. Further, EPBT based system and method uses a quality-diversity heuristic called Novelty Pulsation as well as knowledge distillation to prevent overfitting during training. The discovered hyperparameters adapt to the training process and serve to regularize the learning task by discouraging overfitting to the labels. EPBT thus demonstrates a practical instantiation of regularization metalearning based on simultaneous training.

Abstract: A method for injecting metadata into an existing artifact is described. The method generates metadata related to an existing artifact having a predetermined structure and encodes the metadata in accordance with the predetermined structure. The encoded metadata is embedded within the existing artifact in accordance with the predetermined structure and is delineated within the predetermined structure as one or more individual records. The artifact, including embedded metadata, is stored within a storage entity and is accessible to processes related to the artifact. Additional records may be generated and embedded over time, thus creating a timeline if event related to the artifact.

Abstract: Roughly described, an evolutionary data mining system includes at least two processing units, each having a pool of candidate individuals in which each candidate individual has a fitness estimate and experience level. A first processing unit tests candidate individuals against training data, updates an individual's experience level, and assigns each candidate to one of multiple layers of the candidate pool based on the individual's experience level. Individuals within the same layer of the same pool compete with each other to remain candidates. The first processing unit selects a set of candidates to retain based on the relative novelty of their responses to the training data. The first processing unit reports successful individuals to the second processing unit, and receives individuals for further testing from the second processing unit. The second processing unit selects individuals to retain based on their fitness estimate.

Abstract: A computer-implemented method optimizing genetic algorithms for finding solutions to a provided problem is described. The method implements a multi-arm bandit algorithm to determine performance scores for candidate individuals from a candidate pool in dependence on successes and failures of the one or more candidates. The method evolves the candidate individuals in the candidate pool by performing evolution steps including: determining a fitness score for each of the candidate individuals in the candidate pool in dependence on the performance scores for the candidate individuals, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool after the discarding of the candidate individuals.

Abstract: A system and method of combining and improving sets of diverse prescriptors for Evolutionary Surrogate-assisted Prescription (ESP) model is described. The prescriptors are distilled into neural networks and evolved further using ESP. The system and method can handle diverse sets of prescriptors in that it makes no assumptions about the form of the input (i.e., contexts) of the initial prescriptors; it relies only on the prescriptions made in order to distill each prescriptor to a neural network with a fixed form. The resulting set of high performing prescriptors provides a practical way for ESP to incorporate external human and machine knowledge and generate more accurate and fitting set of solutions.

Abstract: A composite novelty method approach to deceptive problems where a secondary objective is available to diversify the search is described. In such cases, composite objectives focus the search on the most useful tradeoffs and allow escaping deceptive areas. Novelty-based selection increases exploration in the focus area, leading to better solutions, faster and more consistently and it can be combined with other fitness-based methods.

Abstract: The technology disclosed proposes a novel asynchronous evaluation strategy (AES) that increases throughput of evolutionary algorithms by continuously maintaining a queue of K individuals ready to be sent to the worker nodes for evaluation and evolving the next generation once a fraction Mi of the K individuals have been evaluated by the worker nodes, where Mi<<K. A suitable value for Mi is determined experimentally, balancing diversity and efficiency. The technology disclosed is extended to coevolution of deep neural network supermodules and blueprints in the form of AES for cooperative evolution of deep neural networks (CoDeepNEAT-AES). Applied to image captioning domain, a threefold speedup is observed on 200 graphics processing unit (GPU) worker nodes, demonstrating that the disclosed AES and CoDeepNEAT-AES are promising techniques for evolving complex systems with long and variable evaluation times.

Abstract: The computer system and method herein uses a multi-objective driven evolutionary algorithm that is better able to find optimum solutions to a problem because it balances the use of objectives as composite functions, and relative novelty and diversity in evolutionary optimization. In particular, the system and method herein described herein presents an improved process which introduces novelty pulsation, i.e., a systematic method to alternate between novelty selection and local optimization.

Abstract: Described herein is a process which facilitates segmented security between domain-specific data sets being evaluated as part of a candidate evaluation service and third-party evolution services, wherein the data sets are not transmitted to the evolution service which is evolving candidates for evaluation. This enables customers with secure data sets to use candidate evolution services securely by obtaining a population of potentially optimal candidate models to evaluate and then optimizing on those data sets in their own secure fashion.

Abstract: A method for finding a solution to a problem is provided. The method includes storing candidate individuals in a candidate pool and evolving the candidate individuals by performing steps including (i) testing each of the candidate individuals to obtain test results, (ii) assigning a performance measure to the tested candidate individuals, (iii) discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and (iv) adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool.

Abstract: A computer-implemented method optimizing genetic algorithms for finding solutions to a provided problem is described. The method implements a multi-arm bandit algorithm to determine performance scores for candidate individuals from a candidate pool in dependence on successes and failures of the one or more candidates. The method evolves the candidate individuals in the candidate pool by performing evolution steps including: determining a fitness score for each of the candidate individuals in the candidate pool in dependence on the performance scores for the candidate individuals, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from candidate individuals remaining in the candidate pool after the discarding of the candidate individuals.

Abstract: An artificial intelligence (AI) prediction engine is used to correctly classify an entity based on a predetermined classification taxonomy, e.g., NAICS. The engine and process for using takes as inputs an entity's social presence (e.g., name, web address, etc.) and address. The AI prediction engine employs various machine learning models to make a classification prediction.

Abstract: A general prediction model is based on an observer traveling around a continuous space, measuring values at some locations, and predicting them at others. The observer is completely agnostic about any particular task being solved; it cares only about measurement locations and their values. A machine learning framework in which seemingly unrelated tasks can be solved by a single model is proposed, whereby input and output variables are embedded into a shared space. The approach is shown to (1) recover intuitive locations of variables in space and time, (2) exploit regularities across related datasets with completely disjoint input and output spaces, and (3) exploit regularities across seemingly unrelated tasks, outperforming task-specific single-task models and multi-task learning alternatives.

Abstract: An error detection framework, RED (Residual-based Error Detection), produces reliable confidence scores for detecting misclassification errors. RED calibrates the classifier's inherent confidence indicators and estimates uncertainty of the calibrated confidence scores using Gaussian Processes.

Abstract: Roughly described, anomalous behavior of a machine-learned computer-implemented individual can be detected while operating in a production environment. A population of individuals is represented in a computer storage medium, each individual identifying actions to assert in dependence upon input data. As part of machine learning, the individuals are tested against samples of training data and the actions they assert are recorded in a behavior repository. The behavior of an individual is characterized from the observations recorded during training. In a production environment, the individuals are operated by applying production input data, and the production behavior of the individual is observed and compared to the behavior of the individual represented in the behavior repository. A determination is made from the comparison of whether the individual's production behavior during operation is anomalous.