We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity...

This work represents the first step towards a task library system in the reinforcement learning domain. Task libraries could be useful in speeding up the learning of new tasks through task transfer. Related transfer can increase learning rate and can help prevent convergence to sub-optimal policies in reinforcement learning. Unrelated transfer can be extremely detrimental to the learning rate. ...

In machine learning and data mining, we often encounter situations where we have an insufficient amount of high-quality data in a target domain, but we may have plenty of auxiliary data in related domains. Transfer learning aims to exploit these additional data to improve the learning performance in the target domain. In this talk, I will give an overview on some recent advances in transfer lea...

Transfer learning is one of the most important directions in current machine learning research. In this paper, we propose a new learning framework called Multi-source part-based Transfer Learning (MspbTL), which is one kind of parameter transfer with multiple related source tasks. Dissimilar to many traditional works, we consider how to transfer information from one task to another in the form ...

This study aims to better understand the effect of practice schedule and feedback providing types. In two separate experiments the contextual interference effect in bandwidth and self-control feedback conditions on relative and absolute timing learning was examined. In experiment I, the effect of contextual interference using bandwidth and self-control feedback on absolute timing learning (para...

Negative transfer – a special type of transfer learning – refers to the interference of the previous knowledge with new learning. In this research, through an empirical study, we demonstrate the futile defence to the negative transfer via conventional neural network based transfer techniques, i.e., mid-level feature extraction and knowledge distillation. Under a finer specification of transfer ...

We consider an interesting problem in this paper that uses transfer learning in two directions to compensate missing knowledge from the target domain. Transfer learning tends to be exploited as a powerful tool that mitigates the discrepancy between different databases used for knowledge transfer. It can also be used for knowledge transfer between different modalities within one database. Howeve...