There are complaints about current machine learning techniques such as the requirement of
a huge amount of training data and proficient training skills, the difficulty of continual …

We study {\it model reusability evaluation}(MRE) for source pre-trained models: evaluating
their transfer learning performance to new target tasks. In special, we focus on the setting …

The learnware paradigm proposed by Zhou [2016] devotes to constructing a market of
numerous wellperformed models, enabling users to solve problems by reusing existing …

The learnware paradigm was recently proposed by Zhou with the wish of developing the
learnware market to help users build models more efficiently by reusing existing well …

The learnware paradigm aims to construct a market of numerous well-performing machine
learning models, which enables users to leverage these models to accomplish specific tasks …

The learnware paradigm attempts to change the current style of machine learning
deployment, ie, user builds her own machine learning application almost from scratch, to a …

This paper studies multiparty learning, aiming to learn a model using the private data of
different participants. Model reuse is a promising solution for multiparty learning, assuming …

As the ubiquity of deep learning in various machine learning applications has amplified, a
proliferation of neural network models has been trained and shared on public model …

Deep learning has shown significant improvements on various machine learning tasks by
introducing a wide spectrum of neural network models. Yet, for these neural network models …

The learnware paradigm aims to help users leverage numerous existing high-performing
models instead of starting from scratch, where a learnware consists of a well-trained model …