Embodiments of the disclosure involve training machine learned models using DNA-encoded library experimental data outputs and for deploying the trained machine learned models for conducting a virtual compound screen, for performing a hit selection and analysis, or for predicting binding affinities between compounds and targets. Machine learned models are trained using one or more augmentations that selectively expand molecular representations of a training dataset. Furthermore, machine learned models are trained to account for confounding covariates, thereby improving the machine learned models' abilities to conduct a virtual screen, perform a hit selection, and to predict binding affinities.

Embodiments of the disclosure involve training machine learned models using DNA-encoded library experimental data outputs and for deploying the trained machine learned models for conducting a virtual compound screen, for performing a hit selection and analysis, or for predicting binding affinities between compounds and targets. Machine learned models are trained using one or more augmentations that selectively expand molecular representations of a training dataset. Furthermore, machine learned models are trained to account for confounding covariates, thereby improving the machine learned models' abilities to conduct a virtual screen, perform a hit selection, and to predict binding affinities.

Methods, apparatus, and processor-readable storage media for detecting container incidents using machine learning techniques are provided herein. An example method includes generating first and second representations of a telemetry dataset associated with a software container in an edge computing environment, the telemetry dataset including values for a set of parameters for each of a plurality of timestamps; providing the first representation to a predictive model to obtain a predicted remaining lifetime of the software container; providing the second representation of the telemetry dataset to a first machine learning model to obtain a predicted behavior of the software container; determining, using a second machine learning model, whether the predicted behavior of the software container corresponds to a pattern of behavior that is associated with previous container incidents; and triggering an automated action for the software container in response to determining that the predicted behavior corresponds to the pattern of behavior.

Systems, methods, and computer-readable media are disclosed for language-agnostic subtitle drift detection and correction. A method may include determining subtitles and/or captions from media content (e.g., videos), the subtitles and/or captions corresponding to dialog in the media content. The subtitles may be broken up into segments which may be analyzed to determine a likelihood of drift (e.g., a likelihood that the subtitles are out of synchronization with the dialog in the media content) for each segment. For segments with a high likelihood of drift, the subtitles may be incrementally adjusted to determine an adjustment that eliminates and/or reduces the amount of drift and the drift in the segment may be corrected based on the drift amount detected. A linear regression model and/or human blocks determined by human operators may be used to otherwise optimize drift correction.

Trajectory optimization is process of designing a trajectory of operating variables that optimizes measure of performance while satisfying a set of constraints, when the system moves from one state to another. It is very necessary to achieve optimization in real time. A system and method for real-time trajectory optimization has been provided. The trajectory optimization of a process can be performed in any dynamical automated system. The system is configured to optimize the trajectory in both online and offline mode. In the online mode, the system optimizes the trajectory of the process in real-time. The system has the ability to handle both machine learning and deep learning based time series models along with first principles based models represented by ordinary/partial differential equation or differential algebraic equation based dynamic models of the process to estimate process variables given the disturbance profile and the actuation profile of manipulated variables.

Trajectory optimization is process of designing a trajectory of operating variables that optimizes measure of performance while satisfying a set of constraints, when the system moves from one state to another. It is very necessary to achieve optimization in real time. A system and method for real-time trajectory optimization has been provided. The trajectory optimization of a process can be performed in any dynamical automated system. The system is configured to optimize the trajectory in both online and offline mode. In the online mode, the system optimizes the trajectory of the process in real-time. The system has the ability to handle both machine learning and deep learning based time series models along with first principles based models represented by ordinary/partial differential equation or differential algebraic equation based dynamic models of the process to estimate process variables given the disturbance profile and the actuation profile of manipulated variables.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to categorical data objects. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis with respect to categorical data objects by utilizing at least one of predictive feature hierarchies, feature refinement routines, decision subsets of predictive features that are generated based at least in part on predictiveness measures for the predictive features, and/or the like.

Methods and systems for improved analysis of population data and imagery data for a geographical area are provided. In one embodiment, a method is provided that includes receiving or extracting characteristics for a population and/or a geographical data. The characteristics may be combined with a variable of interest to form an input dataset. A first plurality of machine learning models may be trained based on at least a portion of the input dataset and may be used to generate a plurality of prediction surfaces for the variable of interest. A second plurality of machine learning models may be trained based on the plurality of prediction surfaces and at least one of the second plurality of machine learning models may be selected for future predictions of the variable of interest.

This document describes road modeling with ensemble Gaussian processes. A road is modeled at a first time using at least one Gaussian process regression (GPR). A kernel function is determined based on a sample set of detections received from one or more vehicle systems. Based on the kernel function, a respective mean lateral position associated with a particular longitudinal position is determined for each GPR of the at least one GPR. The respective mean lateral position for each of the at least one GPR is aggregated to determine a combined lateral position associated with the particular longitudinal position. A road model is then output including the combined lateral position associated with the particular longitudinal position. In this way, a robust and computationally efficient road model may be determined to aid in vehicle safety and performance.

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to training a learning model based on determined drift. 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 selection component that can select an ensemble of deep learning regressors, and an identification component that can identify drift among the ensemble. An analysis component can analyze uncertainty samplings from the ensemble to determine a time instant when drift occurred. A training component can train one or more deep learning models, such as of the deep learning regressors, based upon the identified drift.