Abstract: A method of deep neural network (DNN) modularization for optimal loading includes receiving, by an electronic device, a DNN model for execution, obtaining, by the electronic device, a plurality of parameters associated with the electronic device and a plurality of parameters associated with the DNN model, determining, by the electronic device, a number of sub-models of the DNN model and a splitting index, based on the obtained plurality of parameters associated with the electronic device and the obtained plurality of parameters associated with the DNN model, and splitting, by the electronic device, the received DNN model into a plurality of sub-models, based on the determined number of sub-models of the DNN model and the determined splitting index.

Abstract: An electronic device for generation of recommendations using trust-based embeddings is provided. The electronic device determines first correlation information of a first set of users associated with a first domain. The electronic device generates, based on the first correlation information, a first vector indicating a trust embedding of a first user with respect to the first set of users, in the first domain. The electronic device receives second correlation information associated with a second set of users associated with a second domain. The electronic device utilizes a Graph Attention Network model for the second correlation information to generate a second vector indicative of a trust embedding of the first user, with respect to the second set of users, in the second domain. The electronic device applies a recommendation model on the first vector and the second vector to recommend one or more items to the first user.

Abstract: An electronic device and a method for implementation of hypergraph-based collaborative filtering recommendations. The electronic device receives a collaborative filtering graph corresponding to a set of users and a set of items. The electronic device determines a first set of user embeddings and a first set of item embeddings. The electronic device applies a semantic clustering model to determine a second set of user embeddings and a second set of item embeddings. The electronic device constructs a hypergraph to determine a third set of user embeddings and a third set of item embeddings. The electronic device determines a first contrastive loss and a second contrastive loss to determine a collaborative filtering score. The electronic device determines a recommendation of an item for a user based on the determined collaborative filtering score. The electronic device renders the determined recommended item on a display device.

Abstract: An electronic device for generation of recommendations using trust-based embeddings is provided. The electronic device determines first correlation information of a first set of users associated with a first domain. The electronic device generates, based on the first correlation information, a first vector indicating a trust embedding of a first user with respect to the first set of users, in the first domain. The electronic device receives second correlation information associated with a second set of users associated with a second domain. The electronic device utilizes a Graph Attention Network model for the second correlation information to generate a second vector indicative of a trust embedding of the first user, with respect to the second set of users, in the second domain. The electronic device applies a recommendation model on the first vector and the second vector to recommend one or more items to the first user.

Abstract: An electronic device and a method for implementation for machine learning model update based on dataset or feature unlearning are disclosed. The electronic device receives a data subset of a first dataset associated with a user. A first machine learning model is trained based on the first dataset. The electronic device trains a second machine learning model based on the received data subset. The electronic device applies a transformation function on the trained first machine learning model based on the trained second machine learning model. The electronic device updates the trained first machine learning model, based on the application of the transformation function. The update of the trained first machine learning model corresponds to an unlearning of at least one of the received data subset or a set of features associated with the second machine learning model.

Abstract: A method of deep neural network (DNN) modularization for optimal loading includes receiving, by an electronic device, a DNN model for execution, obtaining, by the electronic device, a plurality of parameters associated with the electronic device and a plurality of parameters associated with the DNN model, determining, by the electronic device, a number of sub-models of the DNN model and a splitting index, based on the obtained plurality of parameters associated with the electronic device and the obtained plurality of parameters associated with the DNN model, and splitting, by the electronic device, the received DNN model into a plurality of sub-models, based on the determined number of sub-models of the DNN model and the determined splitting index.

Abstract: A method for generating an optimal neural network (NN) model may include determining intermediate outputs of the NN model by passing an input dataset through each intermediate exit gate of the plurality of intermediate exit gates, determining an accuracy score for each intermediate exit gate of the plurality of intermediate exit gates based on a comparison of the final output of the NN model with the intermediate output, identifying an earliest intermediate exit gate that produces the intermediate output closer to the final output based on the accuracy score, and generating the optimal NN model by removing remaining layers of the plurality of layers and remaining intermediate exit gates of the plurality of intermediate exit gates located after the determined earliest intermediate exit gate.

Abstract: Embodiments herein provide a NAS method of generating an optimized DNN model for executing a task in an electronic device. The method includes identifying the task to be executed in the electronic device. The method includes estimating a performance parameter to be achieved while executing the task. The method includes determining hardware parameters of the electronic device required to execute the task based on the performance parameter and the task, and determining optimal neural blocks from a plurality of neural blocks based on the performance parameter and the hardware parameter of the electronic device. The method includes generating the optimized DNN model for executing the task based on the optimal neural blocks, and executing the task using the optimized DNN model.