A method for selectively generating suggested default values for I/O configurations is provided. The method identifies a first selection including a first input value for an I/O configuration. The method determines a set of remaining input options based on the first selection. The method accesses a set of decision trees based on the set of remaining input options and selects a decision tree of the set of decision trees based on the first input value. The method generates a suggested value for a subsequent selection for the I/O configuration and causes presentation of the suggested value and a user interface element representing the subsequent selection.

Techniques for improved neural network modeling are provided. In one embodiment, a system comprises a memory that stores computer-executable components and a processor that executes the components. The computer-executable components can comprise a loss function logic component that determines a penalty based on a training term, the training term being a function of a relationship between an output scalar value of a first neuron of a plurality of neurons of a neural network model, a plurality of input values from the first neuron, and one or more tunable weights of connections between the plurality of neurons; an optimizer component that receives the penalty from the loss function component, and changes one or more of the tunable weights based on the penalty; and an output component that generates one or more output values indicating whether a defined pattern is detected in unprocessed input values received at the neural network evaluation component.

Certain aspects of the present disclosure provide techniques for receiving data defining a neural network; analyzing the data to determine a depth-first cut point for a depth-first traversal portion of an overall network traversal; performing depth-first traversal for the depth-first portion of the overall network traversal; and performing layer-based traversal for a layer-based portion of the overall network traversal.

Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

The present disclosure relates to systems, methods, and non-transitory computer readable media for selecting hyper-parameter sets by utilizing a modified Bayesian optimization approach based on a combination of accuracy and training efficiency metrics of a machine learning model. For example, the disclosed systems can fit accuracy regression and efficiency regression models to observed metrics associated with hyper-parameter sets of a machine learning model. The disclosed systems can also implement a trade-off acquisition function that implements an accuracy-training efficiency balance metric to explore the hyper-parameter feature space and select hyper-parameters for training the machine learning model considering a balance between accuracy and training efficiency.

Systems and methods are described in relation to specific technical improvements adapted for machine learning architectures that conduct classification on numerical and/or unstructured data. In an embodiment, two neural networks are utilized in concert to generate output data sets representative of predicted future states of an entity. A second learning architecture is trained to cluster prior entities based on characteristics converted into the form of features and event occurrence such that a boundary function can be established between the clusters to form a decision boundary between decision regions. These outputs are mapped to a space defined by the boundary function, such that the mapping can be used to determine whether a future state event is likely to occur at a particular time in the future.

A document analysis device that includes a memory operable to store a machine learning model configured to receive a sentence as an input and to output a classification identifier that is associated with a sentence type for the received sentence. The device further includes an artificial intelligence (AI) processing engine configured to receive a document comprising text, to sentences within the document, and to classify the sentences using the machine learning model. The AI processing engine is further configured to identify tagging rules for the document and to annotate one or more sentences from the document with a sentence type that matches a sentence type that is identified by the tagging rules for the document.

The present invention relates to a rock classification method wherein at least one image (IMA) of the rock to be classified is acquired, and wherein a decision tree (ARB) classifying the rocks according to several descriptors is used, as well as a machine learning method (APP) from a rock image database (BIR). Machine learning is applied for each descriptor considered.

Techniques are disclosed for using a genetic algorithm to identify a processing pipeline that transforms spectra into a form usable to generate predicted characteristics of corresponding samples. The genetic algorithm is used to generate and evaluate multiple candidate solutions specifying various pre-processing and machine-learning-processing configurations. The processing pipeline is defined based on the candidate solutions.

A method is provided that includes generating a visual environment for interactive development of a machine learning (ML) model. The method includes accessing observations of data each of which includes values of independent variables and a dependent variable, and performing an interactive exploratory data analysis (EDA) of the values of a set of the independent variables. The method includes performing a feature construction and selection based on the interactive EDA, and in which select independent variables are selected as or transformed into a set of features for use in building a ML model to predict the dependent variable. The method includes building the ML model using a ML algorithm, the set of features, and a training set produced from the set of features and observations of the data. And the method includes outputting the ML model for deployment to predict the dependent variable for additional observations of the data.