Abstract: Systems and methods for spatial graph convolutions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting characteristics for molecules, wherein the method includes performing a first set of graph convolutions with a spatial graph representation of a set of molecules, wherein the first set of graph convolutions are based on bonds between the set of molecules, performing a second set of graph convolutions with the spatial graph representation, wherein the second set of graph convolutions are based on at least a distance between each atom and other atoms of the set of molecules, performing a graph gather with the spatial graph representation to produce a feature vector, and predicting a set of one or more characteristics for the set of molecules based on the feature vector.

Abstract: Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.

Abstract: Systems and methods for spatial graph convolutions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting characteristics for molecules, wherein the method includes performing a first set of graph convolutions with a spatial graph representation of a set of molecules, wherein the first set of graph convolutions are based on bonds between the set of molecules, performing a second set of graph convolutions with the spatial graph representation, wherein the second set of graph convolutions are based on at least a distance between each atom and other atoms of the set of molecules, performing a graph gather with the spatial graph representation to produce a feature vector, and predicting a set of one or more characteristics for the set of molecules based on the feature vector.

Abstract: Systems and methods for spatial graph convolutions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting characteristics for molecules, wherein the method includes performing a first set of graph convolutions with a spatial graph representation of a set of molecules, wherein the first set of graph convolutions are based on bonds between the set of molecules, performing a second set of graph convolutions with the spatial graph representation, wherein the second set of graph convolutions are based on at least a distance between each atom and other atoms of the set of molecules, performing a graph gather with the spatial graph representation to produce a feature vector, and predicting a set of one or more characteristics for the set of molecules based on the feature vector.

Abstract: An automated machine learning (“ML”) method may include training a first machine learning model using a first machine learning algorithm and a training data set; validating the first machine learning model using a validation data set, wherein validating the first machine learning model comprises generating an error data set; training a second machine learning model to predict a suitability of the first machine learning model for analyzing an inference data set, wherein the second machine learning model is trained using a second machine learning algorithm and the error data set; and triggering a remedial action associated with the first or second machine learning model in response to a predicted suitability of the first machine learning model for analyzing the inference data set not satisfying a suitability threshold.

Abstract: Apparatuses, systems, computer program products, and methods are disclosed for foundation model based fluid simulations. An apparatus includes a processor and a memory that stores code executable by the processor to receive a fluid foundation model that is pretrained on fluid data, deploy the received fluid foundation model into a downstream machine learning pipeline for a fluid dynamics application, reconfigure the fluid foundation model for the fluid dynamics application, and output results from the machine learning pipeline for the fluid dynamics application based on the reconfigured fluid foundation model.

Abstract: Apparatuses, systems, computer program products, and methods are disclosed for differentiable machines for physical systems. A hardware server device is configured to determine a plurality of differentiable models each representing a component of a physical system. A hardware server device is configured to combine a plurality of differentiable models using an integration layer so that the integration layer and the combined differentiable models form a differentiable machine representing a physical system. A hardware server device is configured to deploy a differentiable machine for an instance of a physical system.

Abstract: Apparatuses, systems, program products, and methods are disclosed for interpretability-based machine learning adjustment during production. An apparatus includes a first results module that is configured to receive a first set of inference results of a first machine learning algorithm during inference of a production data set. An apparatus includes a second results module that is configured to receive a second set of inference results of a second machine learning algorithm during inference of a production data set. An apparatus includes an action module that is configured to trigger one or more actions that are related to a first machine learning algorithm in response to a comparison of first and second sets of inference results not satisfying explainability criteria.

Abstract: Apparatuses, systems, program products, and methods are disclosed for determining validity of machine learning algorithms for datasets. An apparatus includes a primary training module that is configured to train a first machine learning model for a first machine learning algorithm. An apparatus includes a primary validation module that is configured to validate a first machine learning model to generate an error data set. An apparatus includes a secondary training module that is configured to train a second machine learning model for a second machine learning algorithm using an error data set. A second machine learning algorithm may be configured to predict a suitability of a first machine learning model for analyzing an inference data set. An apparatus includes an action module that is configured to trigger an action in response to a predicted suitability of the first machine learning model not satisfying a predetermined suitability threshold.

Abstract: Apparatuses, systems, program products, and method are disclosed for detecting suitability of machine learning models for datasets. An apparatus includes a training evaluation module configured to calculate a first statistical data signature for a training data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes an inference evaluation module configured to calculate a second statistical data signature for an inference data set of a machine learning system using one or more predefined statistical algorithms. An apparatus includes a score module configured to calculate a suitability score describing the suitability of a training data set to an inference data set as a function of a first and a second statistical data signature. An apparatus includes an action module configured to perform an action related to a machine learning system in response to a suitability score satisfying an unsuitability threshold.

Abstract: Systems and methods for spatial graph convolutions in accordance with embodiments of the invention are illustrated. One embodiment includes a method for predicting characteristics for molecules, wherein the method includes performing a first set of graph convolutions with a spatial graph representation of a set of molecules, wherein the first set of graph convolutions are based on bonds between the set of molecules, performing a second set of graph convolutions with the spatial graph representation, wherein the second set of graph convolutions are based on at least a distance between each atom and other atoms of the set of molecules, performing a graph gather with the spatial graph representation to produce a feature vector, and predicting a set of one or more characteristics for the set of molecules based on the feature vector.

Abstract: Apparatuses, systems, program products, and method are disclosed for machine learning abstraction. An apparatus includes an objective module configured to receive an objective to be analyzed using machine learning. An apparatus includes a grouping module configured to select a logical grouping of one or more machine learning pipelines to analyze a received objective. An apparatus includes an adjustment module configured to dynamically adjust one or more machine learning settings for a logical grouping of one or more machine learning pipelines based on feedback generated in response to analyzing a received objective.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for conditional iteration. A method includes receiving a request comprising a condition. A method includes checking an address mapping structure for entries satisfying a condition for a request. A method includes providing a result for a request based on one or more entries satisfying a condition for a request.

Abstract: Apparatuses, systems, and methods are disclosed for a key-value store. A method includes encoding a key of a key-value pair into a logical address of a sparse logical address space for a non-volatile medium. A method includes mapping a logical address to a physical location in the non-volatile medium. A method includes storing a value of a key-value pair at a physical location.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for conditional iteration. A method includes receiving a request comprising a condition. A method includes checking an address mapping structure for entries satisfying a condition for a request. A method includes providing a result for a request based on one or more entries satisfying a condition for a request.

Abstract: Apparatuses, systems, and methods are disclosed for a key-value store. A method includes encoding a key of a key-value pair into a logical address of a sparse logical address space for a non-volatile medium. A method includes mapping a logical address to a physical location in the non-volatile medium. A method includes storing a value of a key-value pair at a physical location.