Abstract: A method comprises receiving an intermediate representation structure that represents a target artificial intelligence model, with a node corresponding to an atomic operator independent of a framework and a module dependent on a target framework of the target artificial intelligence model, wherein the intermediate representation structure has a hierarchical structure in which one or more nodes constitute the module, receiving target platform information for identifying a target platform on which the target artificial intelligence model is to be executed and input data to be used in a simulation process of the target artificial intelligence model and generating an expected inference simulation result for a case where the target artificial intelligence model with the input data is to be executed on the target platform, without executing the target artificial intelligence model on the target platform, by using the intermediate representation structure, the target platform information, and the input data.

Abstract: A method for converting a source artificial intelligence model to a target artificial intelligence model for a framework transformation is disclosed.

Abstract: A method for transforming performance of an artificial intelligence model is disclosed. The method comprises: receiving an intermediate representation structure that represents a target artificial intelligence model which is an object of performance transformation, with a node corresponding to an atomic operator independent of a framework and a module dependent on a target framework of the target artificial intelligence model, wherein the intermediate representation structure has a hierarchical structure in which one or more nodes constitute the module, changing at least a part of the intermediate representation structure by applying a model optimization tool to the intermediate representation structure and outputting a target artificial intelligence model to which a performance transformation has been applied, by using the changed intermediate representation structure.

Abstract: Disclosed is an automatic lightweight method and apparatus for information flow-based neural network compression model that may preserve a performance. An automatic lightweight method for a neural network model may include receiving a first model, generating a second model by injecting trainable bottleneck parameters into the first model, training the bottleneck parameters of the second model using training data, determining an optimal threshold for the trained bottleneck parameters, and pruning the second model based on the trained bottleneck parameters and the determined optimal threshold.

Abstract: A computer-implemented method for training a neural network to perform a data processing task includes: for each data sample of a set of labeled data samples: by a first loss function for the data processing task, computing a first loss for that data sample; and by a second loss function, automatically computing a weight value for the data sample based on the first loss, the weight value indicative of a reliability of a label of the data sample predicted by the neural network for the data sample and dictating the extent to which that data sample impacts training of the neural network; and training the neural network with the set of labelled data samples according to their respective weight value.