Abstract: Robustness of a machine learning model can be characterized by receiving a file with a known, first classification by the machine learning model. Thereafter, a selection is made as to which of a plurality of perturbation algorithms to use to modify the file. The perturbation algorithm is selected as to provide a shortest sequence of actions to cause the machine learning model to provide a desired classification. Subsequently, the received file is iteratively modified using the selected perturbation algorithm and inputting the corresponding modified file into the machine learning model until the machine learning model outputs a known, second classification. Related apparatus, systems, techniques and articles are also described.

Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.

Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.

Abstract: An image with a known, first classification by the machine learning model is received. This image is then iteratively modified using at least one perturbation algorithm and such modified images are input into the machine learning model until such time as the machine learning model outputs a second classification different from the first classification. Data characterizing the modifications to the image that resulted in the second classification can be provided (e.g., displayed in a GUI, loaded into memory, stored in physical persistence, transmitted to a remote computing device). Related apparatus, systems, techniques and articles are also described.

Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.

Abstract: An image with a known, first classification by the machine learning model is received. This image is then iteratively modified using at least one perturbation algorithm and such modified images are input into the machine learning model until such time as the machine learning model outputs a second classification different from the first classification. Data characterizing the modifications to the image that resulted in the second classification can be provided (e.g., displayed in a GUI, loaded into memory, stored in physical persistence, transmitted to a remote computing device). Related apparatus, systems, techniques and articles are also described.

Abstract: Robustness of a machine learning model can be characterized by receiving a file with a known, first classification by the machine learning model. Thereafter, a selection is made as to which of a plurality of perturbation algorithms to use to modify the file. The perturbation algorithm is selected as to provide a shortest sequence of actions to cause the machine learning model to provide a desired classification. Subsequently, the received file is iteratively modified using the selected perturbation algorithm and inputting the corresponding modified file into the machine learning model until the machine learning model outputs a known, second classification. Related apparatus, systems, techniques and articles are also described.

Abstract: An image with a known, first classification by the machine learning model is received. This image is then iteratively modified using at least one perturbation algorithm and such modified images are input into the machine learning model until such time as the machine learning model outputs a second classification different from the first classification. Data characterizing the modifications to the image that resulted in the second classification can be provided (e.g., displayed in a GUI, loaded into memory, stored in physical persistence, transmitted to a remote computing device). Related apparatus, systems, techniques and articles are also described.

Abstract: Described is a system for learning and predicting key phrases. The system learns based on a dataset of historical forecasting questions, their associated time-series data for a quantity of interest, and associated keyword sets. The system learns the optimal policy of actions to take given the associated keyword sets and the optimal set of keywords which are predictive of the quantity of interest. Given a new forecasting question, the system extracts an initial keyword set from a new forecasting question, which are perturbed to generate an optimal predictive key-phrase set. Key-phrase time-series data are extracted for the optimal predictive key-phrase set, which are used to generate a forecast of future values for a value of interest. The forecast can be used for a variety of purposes, such as advertising online.