MODELING OF LONG-RANGE INTERACTIONS WITH REDUCED FEATURE MATERIALIZATION VIA LAMBDA FUNCTIONS

The present disclosure provides systems, methods, and computer program products for performing modeling of long-range interactions with reduced feature materialization, for example, in machine learning models. A computer-implemented method may include receiving a layer input comprising input data and context data, generating one or more lambda functions based, at least in part, on a content function and a position function for each of a plurality of context elements in the context data, and applying one or more of the generated lambda functions to the input data in association with generating a layer output associated with a respective lambda layer. Experimental results for image classification on ResNet and for object detection with RetinaNet show that examples of the present disclosure significantly outperform convolutional and attentional counterparts while providing increased accuracy and efficiency.