The Toronto-dominion Bank

Kin Kwan Leung

Maksims Volkovs

Barum Rho

Yaqiao Luo

Paige Elyse Dickie

Tomi Johan Poutanen

Jesse Cole Cresswell

Hamed Sadeghi

Atiyeh Ashari Ghomi

Kai Wang

Caitlin Messick

Lu Shu

Valentin Tsatskin

Kyle William Hall

Derek Cheung

Mathieu Jean Remi Ravaut

A model visualization system analyzes model behavior to identify clusters of data instances with similar behavior. For a selected feature, data instances are modified to set the selected feature to different values evaluated by a model to determine corresponding model outputs. The feature values and outputs may be visualized in an instance-feature variation plot. The instance-feature variation plots for the different data instances may be clustered to identify latent differences in behavior of the model with respect to different data instances when varying the selected feature. The number of clusters for the clustering may be automatically determined, and the clusters may be further explored by identifying another feature which may explain the different behavior of the model for the clusters, or by identifying outlier data instances in the clusters.

Visualizing feature variation effects on computer model prediction

The disclosed embodiments include computer-implemented processes that generate adaptive textual explanations of output using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to the input dataset, generate output data representative of a predicted likelihood of an occurrence of an event during a second temporal interval. Further, and based on an application of a trained explainability process to the input dataset, the apparatus may generate an element of textual content that characterizes an outcome associated with the predicted likelihood of the occurrence of the event, where the element of textual content is associated with a feature value of the input dataset. The apparatus may also transmit a portion of the output data and the element of textual content to a computing system.

Generating adaptive textual explanations of output predicted by trained artificial-intelligence processes

The disclosed embodiments include computer-implemented processes that flexibly and dynamically analyze a machine learning process, and that generate analytical output characterizing an operation of the machine learning process across multiple analytical periods. For example, an apparatus may receive an identifier of a dataset associated with the machine learning process and feature data that specifies an input feature of the machine learning process. The apparatus may access at least a portion of the dataset based on the received identifier, and obtain, from the accessed portion of the dataset, a feature vector associated with the machine learning process. The apparatus may generate a plurality of modified feature vectors based on the obtained feature vector, and based on an application of the machine learning process to the obtained and modified feature vectors, generate and transmit, to a device, first explainability data associated with the specified input feature for presentation within a digital interface.

Dynamic analysis and monitoring of machine learning processes

A modeling system trains a recurrent machine-learned model by determining a latent distribution and a prior distribution for a latent state. The parameters of the model are trained based on a divergence loss that penalizes significant deviations between the latent distribution the prior distribution. The latent distribution for a current observation is a distribution for the latent state given a value of the current observation and the latent state for the previous observation. The prior distribution for a current observation is a distribution for the latent state given the latent state for the previous observation independent of the value of the current observation, and represents a belief about the latent state before input evidence is taken into account.

Regularization of recurrent machine-learned architectures with encoder, decoder, and prior distribution

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Toronto, Canada

Kin Kwan Leung