Artificial intelligence is the driving force of change focusing on the needs and demands of
the student. The research explores Artificial Intelligence in Education (AIEd) for building …

Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …

Recently model merging techniques have surfaced as a solution to combine multiple single-
talent models into a single multi-talent model. However previous endeavors in this field have …

In collaborative machine learning (CML), multiple agents pool their resources (eg, data)
together for a common learning task. In realistic CML settings where the agents are self …

The growing literature of Federated Learning (FL) has recently inspired Federated
Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better …

Productionizing machine learning projects is inherently complex, involving a multitude of
interconnected components that are assembled like LEGO blocks and evolve throughout …

Existing meta-learning works assume that each task has available training and testing data.
However, there are many available pre-trained models without accessing their training data …

In collaborative learning with streaming data, nodes (eg, organizations) jointly and
continuously learn a machine learning (ML) model by sharing the latest model updates …

Valuation methods of data and machine learning (ML) models are essential to the
establishment of AI marketplaces. Importantly, certain practical considerations (eg …

This paper investigates the problem of exploiting existing solution models of previous tasks
to address a related target task with limited training data. Existing approaches addressing …