With transfer learning, one set of tasks is used to bias learning and improve performance on another task. However, transfer learning may actually hinder performance if the tasks are too dissimilar. As described in this paper, one challenge for transfer learning research is to develop approaches that detect and avoid negative transfer using very little data from the target task.
We analyze naturally occurring datasets from student use of educational technologies to explore a lo...
We analyze naturally occurring datasets from student use of educational technologies to explore a lo...
The success of transfer learning on a target task is highly dependent on the selected source data. I...
The traditional machine learning paradigm of training a task-specific model on one single task has l...
Transfer learning is a common technique used in a wide variety of deep learning applications. Transf...
In this paper we examine the relevance of transfer learning in deep learning context, we review diff...
The study of learning transfer yields conflicting patterns of results. While some research shows str...
The study of learning transfer yields conflicting patterns of results. While some research shows str...
Reinforcement Learning has recently emerged as a viable solution for various sequential decision-mak...
72 students solved two problems using a source problem designed to inhibit performance on the target...
Transfer learning is a successful technique that significantly improves machine learning algorithms ...
Transfer learning is a new machine learning and data mining framework that allows the training and t...
The aim of transfer learning is to reduce sample complexity required to solve a learning task by usi...
The aim of transfer learning is to reduce sample complexity required to solve a learning task by usi...
We analyze naturally occurring datasets from student use of educational technologies to explore a lo...
We analyze naturally occurring datasets from student use of educational technologies to explore a lo...
We analyze naturally occurring datasets from student use of educational technologies to explore a lo...
The success of transfer learning on a target task is highly dependent on the selected source data. I...
The traditional machine learning paradigm of training a task-specific model on one single task has l...
Transfer learning is a common technique used in a wide variety of deep learning applications. Transf...
In this paper we examine the relevance of transfer learning in deep learning context, we review diff...
The study of learning transfer yields conflicting patterns of results. While some research shows str...
The study of learning transfer yields conflicting patterns of results. While some research shows str...
Reinforcement Learning has recently emerged as a viable solution for various sequential decision-mak...
72 students solved two problems using a source problem designed to inhibit performance on the target...
Transfer learning is a successful technique that significantly improves machine learning algorithms ...
Transfer learning is a new machine learning and data mining framework that allows the training and t...
The aim of transfer learning is to reduce sample complexity required to solve a learning task by usi...
The aim of transfer learning is to reduce sample complexity required to solve a learning task by usi...
We analyze naturally occurring datasets from student use of educational technologies to explore a lo...
We analyze naturally occurring datasets from student use of educational technologies to explore a lo...
We analyze naturally occurring datasets from student use of educational technologies to explore a lo...
The success of transfer learning on a target task is highly dependent on the selected source data. I...