Abstract: The various embodiments described herein include methods, systems and/or devices used to visualize data. In one aspect, a method is performed by a computing system having one or more processors and memory. The method includes (1) receiving a request from a user to visualize data, the data stored in a graph dataflow processing system, and (2) in response to the request, invoking an interactive graphical user interface (GUI) for display to the user, the GUI including a first set of visualization data corresponding to a first subset of the data.

Abstract: Technologies pertaining to tuning a hyper-parameter configuration of a learning algorithm are described. The learning algorithm learns parameters of a predictive model based upon the hyper-parameter configuration. Candidate hyper-parameter configurations are identified, and statistical hypothesis tests are undertaken over respective pairs of candidate hyper-parameter configurations to identify, for each pair of candidate hyper-parameter configurations, which of the two configurations is associated with better predictive performance. The technologies described herein take into consideration the stochastic nature of training data, validation data, and evaluation functions.

Abstract: The various embodiments described herein include methods, systems and/or devices used to visualize data. In one aspect, a method is performed by a computing system having one or more processors and memory. The method includes (1) receiving a request from a user to visualize data, the data stored in a graph dataflow processing system, and (2) in response to the request, invoking an interactive graphical user interface (GUI) for display to the user, the GUI including a first set of visualization data corresponding to a first subset of the data.

Abstract: Technologies pertaining to tuning a hyper-parameter configuration of a learning algorithm are described. The learning algorithm learns parameters of a predictive model based upon the hyper-parameter configuration. Candidate hyper-parameter configurations are identified, and statistical hypothesis tests are undertaken over respective pairs of candidate hyper-parameter configurations to identify, for each pair of candidate hyper-parameter configurations, which of the two configurations is associated with better predictive performance. The technologies described herein take into consideration the stochastic nature of training data, validation data, and evaluation functions.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.

Abstract: A system and method for generating a list is provided. The system includes a seed item input subsystem, an item identifying subsystem, a descriptive metadata similarity determining subsystem and a list generating subsystem that builds a list based, at least in part, on similarity processing performed on seed item descriptive metadata and user item descriptive metadata and user selected thresholds applied to such similarity processing. The method includes inexact matching between identifying metadata associated with new user items and identifying metadata stored in a reference metadata database. The method further includes subjecting candidate user items to similarity processing, where the degree to which the candidate user items are similar to the seed item is determined, and placing user items in a list of items based on user selected preferences for (dis)similarity between items in the list and the seed item.