Abstract: The present disclosure relates to a method comprising at training system iteratively training a machine learning algorithm using current training data. The current training data comprises a local dataset of a current task and a replay dataset and may be updated for a next iteration as follows. A training dataset may be received. If the training dataset is not s shared dataset and its task is different from the current task: information representing the local dataset may be shared with other training systems, the local dataset may be added to the replay dataset, and the received training dataset may be used as the local dataset for a next iteration. In case the task is the current task: the received training dataset may be added to the local dataset. If the training dataset is a shared dataset, the received training dataset may be added to the replay dataset.

Abstract: Predetermined musical data for a song is received. The predetermined musical data includes chords and lyrics and rhythmic structures of the song. Audio data of a band generating music of the song is received. Generating real-time vocal audio that is in rhythm with the audio data and in harmony with the chords. The vocal audio includes the lyrics and is of a predetermined voice.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include procuring a model and obtaining hyperparameters for watermarking the model. The operations may include embedding a watermark in the model using the hyperparameters to achieve a watermarked model and delivering the watermarked model and a watermark verification mechanism to a user.

Abstract: Intelligent classification for product pedigree identification are presented. A transaction agreement request may be received from a user. A revised transaction agreement request may be generated based on one or more user profiles, a multi-party entity feedback loop, one or more constraints relating to the transaction agreement request, and a transaction agreement fulfillment requirements of the entity.

Abstract: A method, computer program, and computer system are provided for predicting and assessing risks on websites. Data corresponding to historical interactions of a user with one or more websites is accessed. A simulation of actions of the user is generated based on the accessed data, and actions of the user are simulated on a pre-defined target website based on the generated simulation of the actions of the user. Risks on the target website are identified based on simulating the actions of the user. The website is updated to mitigate the identified risks.

Abstract: A computer-implemented method, a computer program product, and a computer system for assessing fairness of a deep generative model. A computer system receives a user defined fairness criterion for the deep generative model. A computer system probes the deep generative model to produce samples for a target output. A computer system evaluates the samples for the fairness of the deep generative model, according to the user defined fairness criterion. A computer system produces a set of recommendations for modifying the deep generative model to meet the user defined fairness criterion, in response to determining that the deep generative model does not meet the user defined fairness criterion. In response to determining that the deep generative model is to be modified, a computer system applies at least one subset of the recommendations to the deep generative model. A computer system updates the deep generative model.

Abstract: Embodiments for providing enhanced generative models based assistance for design and creativity in a computing environment by a processor. A partially completed design of an object may be received. A set of recommendations may be generated for completing the partially completed design based on one or more generative models.

Abstract: Embodiments for providing expert-in-the-loop training of machine learning models in a computing environment by a processor. A performance of a machine learning model may be learned. Feedback for the machine learning model may be received based on learning the performance the machine learning model, where the feedback includes domain knowledge provided by a domain expert. The machine learning model may be trained or updated based the feedback of the performance of the machine learning model.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to facilitating a process to compensate a service being provided over a network connection. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a determination component that determines a network connection between a server and a client node, and a predictive component that predicts, employing machine learning, a graphical representation update to a service provided by the server over the network connection. The predictive component can generate the prediction in response to a decrease in bandwidth and/or an increase in latency of a network connection. A training component can train a machine learning model employed by the predictive component based on historical data of the service provided by the server.

Abstract: Various embodiments are provided for securing machine learning models by one or more processors in a computing system. One or more hardened machine learning models that are secured against adversarial attacks are provided by applying one or more of a plurality of combinations of selected preprocessing operations from one or more machine learning models, a data set used for hardening the one or more machine learning models, a list of preprocessors, and a selected number of learners.

Abstract: Adversarial attack detection operations may be applied on one or more deep generative models for defending deep generative models from adversarial attacks. The adversarial attack may be detected on the one or more deep generative models based on the one or more of a plurality of adversarial attack detection operations. The one or more deep generative models may be sanitized based on the adversarial attack.

Abstract: Embodiments for providing enhanced adversarial robustness of machine learning models using certification for federated learning in a computing environment by a processor. Machine learning model updates, a dataset, and a set of hyperparameters may be received. One or more certification parameters and one or more filtered machine learning model updates for a machine learning model may be generated by certifying each of plurality of data points using one or more abstract representations in a machine learning operation and filtering the plurality of machine learning model updates.

Abstract: A computer-implemented method, a computer program product, and a computer system for recommending model contributions based on federated learning lineage. The computer system retrieves information of model checkpoints. The computer system trains data analytic models for monitoring activities of training rounds in a federated learning system, based on the information of the model checkpoints. The computer system sends to a user summary statistics of the model checkpoints. The computer system receives from the user natural language instructions of modifying a federated learning plan for future training rounds in the federated learning system. The computer system translates the natural language instructions into updates for the federated learning system. The computer system forwards the updates to the federated learning system.

Abstract: A computer-implemented method, a computer program product, and a computer system for federated learning model lineage. A model lineage system receives an initial model, from an aggregator in a federated learning system, where the aggregator starts a round of training the initial model. The model lineage system dispatches the initial model to workers in the federated learning system. The model lineage system records the initial model in a lineage database. The model lineage system receives updates from the workers which train the initial model locally. The model lineage system records the updates in the lineage database.

Abstract: Various embodiments are provided for automating decision making for a neural architecture search by one or more processors in a computing system. One or more specifications may be automatically selected for a dataset, tasks, and one or more constraints for a neural architecture search. The neural architecture search may be performed based on the one or more specifications. A deep learning model may be suggested, predicted, and/or configured for the dataset, the tasks, and the one or more constraints based on the neural architecture search.

Abstract: A method for additive manufacturing includes identifying a discrepancy between a three-dimensional model and an object model. The three-dimensional model is a model of a three-dimensional object that is being constructed by an additive manufacturing process, and the three-dimensional object is being constructed based on the object model. The method further includes determining a reconfiguration recommendation based on the identified discrepancy. The method further includes reconfiguring the additive manufacturing process based on the reconfiguration recommendation.

Abstract: A computer receives a dataset and a set of ML pipeline components to generate a preferred ensemble of Machine Learning (ML) pipelines. An Automated Learning (AutoML) tool is applied to generate a plurality of ML pipelines. A performance value is determined for each pipeline, and a set of candidate pipelines is identified based on the performance values. The candidate pipelines are combined into candidate ensembles. A database provides historic performance data for a plurality of historic ensembles applied to a plurality of historic datasets. A metamodel is trained to identify patterns within the historic performance data, and a applies the patterns to generate predicted ensemble performance values for the candidate ensembles. A preferred ensemble is selected based on the predicted performance value rankings.

Abstract: A computing device configured for automatic selection of model parameters includes a processor and a memory coupled to the processor. The memory stores instructions to cause the processor to perform acts including providing an initial set of model parameters and initial condition information to a model based on historical data. A model generates data based on the model parameters and the initial condition information. After determining whether the model-generated data is similar to an observed data, updated model parameters are selected for input to the model based on the determined similarity.

Abstract: A computer-implemented method includes receiving at a threat detection system monitoring data in real-time from online activity in a network, the threat detection system including a machine learning model, and analyzing the monitoring data via the machine learning model to identify one or more anomalies in the monitoring data associated with a security threat to the network, the machine learning model trained to have one or more learning parameters. The method also includes receiving a subset of the monitoring data at a meta-learning module, storing the subset as time-based historical data, inputting the historical data at a meta-learning model, calculating an update policy prescribing a change to the one or more learning parameters based on the historical data, and applying the update policy to the machine learning model.

Abstract: A system, computer program product, and method are presented for administering examinations with adversarial hardening of queries against automated responses. The method include receiving an original query electronically. A response to the original query is to be submitted electronically by a human. The method also includes modifying the original query, thereby generating a modified query. The modified query is configured to be comprehensible by the human, and not properly responded to through electronic means without human support.