Machine learning models with efficient feature learning

A system comprising:

• a processor;
• and
• a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform operations comprising:
• applying a risk prediction model to time-series data associated with a target entity to generate a risk indicator for the target entity, wherein the risk prediction model comprises a feature learning model configured to generate features from the time-series data and a risk classification model configured to generate the risk indicator based on the features generated by the feature learning model, the feature learning model comprising a plurality of filters, and wherein the risk prediction model is trainable via a training process comprising:
• adjusting parameters of the risk prediction model to minimize a loss function defined based on risk indicators generated for training time-series data and training risk indicators corresponding to the training time-series data;
• and
• generating an updated risk prediction model by removing a filter from an original set of filters for the feature learning model based on influencing scores determined for the original set of filters, the original set of filters organized as blocks of filters and comprising the plurality of filters in the trained feature learning model, the influencing scores for the original set of filters determined based on the adjusted parameters, wherein removing the filter from the original set of filters further comprises determining an influencing score for a block of filters of the blocks of filters by computing a sum of the L-2 norm of weights applied to subsets of the features that are generated via the block of filters;
• and
• transmitting, to a remote computing device, a responsive message including at least the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.

A non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising:

• applying a risk prediction model to time-series data associated with a target entity to generate a risk indicator for the target entity, wherein the risk prediction model comprises a feature learning model configured to generate features from the time-series data and a risk classification model configured to generate the risk indicator based on the features generated by the feature learning model, the feature learning model comprising a plurality of filters, and wherein the risk prediction model is trainable via a training process comprising:
• adjusting parameters of the risk prediction model to minimize a loss function defined based on risk indicators generated for training time-series data and training risk indicators corresponding to the training time-series data;
• and
• generating an updated risk prediction model by removing a filter from an original set of filters for the feature learning model based on influencing scores determined for the original set of filters, the original set of filters organized as blocks of filters and comprising the plurality of filters in the trained feature learning model, the influencing scores for the original set of filters determined based on the adjusted parameters, wherein removing the filter from the original set of filters further comprises determining an influencing score for a block of filters of the blocks of filters by computing a sum of the L-2 norm of weights applied to subsets of the features that are generated via the block of filters;
• and
• transmitting, to a remote computing device, a responsive message including at least the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.