While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

We introduce a new measure to evaluate the transferability of representations learned by
classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy …

One approach to deal with the statistical inefficiency of neural networks is to rely on auxiliary
losses that help to build useful representations. However, it is not always trivial to know if an …

Transfer learning across heterogeneous data distributions (aka domains) and distinct tasks
is a more general and challenging problem than conventional transfer learning, where either …

Curriculum learning in reinforcement learning is a training methodology that seeks to speed
up learning of a difficult target task, by first training on a series of simpler tasks and …

Transfer learning is a method where an agent reuses knowledge learned in a source task to
improve learning on a target task. Recent work has shown that transfer learning can be …

With the preponderance of pretrained deep learning models available off-the-shelf from
model banks today, finding the best weights to fine-tune to your use-case can be a daunting …

To date, transfer learning (TL) has been successfully applied for enhancing the learning
performance of reinforcement learning (RL), and many transfer RL (TRL) approaches have …

Heterogeneous wireless sensor networks are a source of large amount of different
information representing environmental aspects such as light, temperature, and humidity. A …

Transfer learning, or inductive transfer, refers to the transfer of knowledge from a source task
to a target task. In the context of convolutional neural networks (CNNs), transfer learning can …