Transfer Value Iteration Networks

Value iteration networks (VINs) have been demonstrated to have a good generalization ability for reinforcement learning tasks across similar domains. However, based on our experiments, a policy learned by VINs still fail to generalize well on the domain whose action space and feature space are not identical to those in the domain where it is trained. In this paper, we propose a transfer learning approach on top of VINs, termed Transfer VINs (TVINs), such that a learned policy from a source domain can be generalized to a target domain with only limited training data, even if the source domain and the target domain have domain-specific actions and features. We empirically verify that our proposed TVINs outperform VINs when the source and the target domains have similar but not identical action and feature spaces. Furthermore, we show that the performance improvement is consistent across different environments, maze sizes, dataset sizes as well as different values of hyperparameters such as number of iteration and kernel size

From unsupervised to semi-supervised adversarial domain adaptation in EEG-based sleep staging.

OBJECTIVE: The recent breakthrough of wearable sleep monitoring devices results in large amounts of sleep data. However, as limited labels are available, interpreting these data requires automated sleep stage classification methods with a small need for labeled training data. Transfer learning and domain adaptation offer possible solutions by enabling models to learn on a source dataset and adapt to a target dataset. APPROACH: In this paper, we investigate adversarial domain adaptation applied to real use cases with wearable sleep datasets acquired from diseased patient populations. Different practical aspects of the adversarial domain adaptation framework \hl{are examined}, including the added value of (pseudo-)labels from the target dataset and the influence of domain mismatch between the source and target data. The method is also implemented for personalization to specific patients. MAIN RESULTS: The results show that adversarial domain adaptation is effective in the application of sleep staging on wearable data. When compared to a model applied on a target dataset without any adaptation, the domain adaptation method in its simplest form achieves relative gains of 7%-27% in accuracy. The performance on the target domain is further boosted by adding pseudo-labels and real target domain labels when available, and by choosing an appropriate source dataset. Furthermore, unsupervised adversarial domain adaptation can also personalize a model, improving the performance by 1%-2% compared to a non-personal model. SIGNIFICANCE: In conclusion, adversarial domain adaptation provides a flexible framework for semi-supervised and unsupervised transfer learning. This is particularly useful in sleep staging and other wearable EEG applications

Machine learning for targeted display advertising: Transfer learning in action

This paper presents a detailed discussion of problem formulation and data representation issues in the design, deployment, and operation of a massive-scale machine learning system for targeted display advertising. Notably, the machine learning system itself is deployed and has been in continual use for years, for thousands of advertising campaigns (in contrast to simply having the models from the system be deployed). In this application, acquiring sufficient data for training from the ideal sampling distribution is prohibitively expensive. Instead, data are drawn from surrogate domains and learning tasks, and then transferred to the target task. We present the design of this multistage transfer learning system, highlighting the problem formulation aspects. We then present a detailed experimental evaluation, showing that the different transfer stages indeed each add value. We next present production results across a variety of advertising clients from a variety of industries, illustrating the performance of the system in use. We close the paper with a collection of lessons learned from the work over half a decade on this complex, deployed, and broadly used machine learning system.Statistics Working Papers Serie

Relevant framework for social applications of IoT by means of Machine Learning techniques

With the rapid development of Internet of Things (IoT) technology, billions of smart devices are being connected into a whole network and streaming out a huge amount of data every moment. Unimaginable potential value can be mined from these data with the help of "Cloud Computing" and "Machine Learning" techniques. The target of our research is to address the benefits of IoT in social applications, especially in healthcare area, by developing a multilayer framework. Low cost data collection, efficient data transfer, flexible data management and accurate data analysis mechanisms will be included in the framework. A Smart Decision Support System is supposed to be developed on the basis of this framework

PENINGKATAN HASIL BELAJAR PESERTA DIDIK KELAS VII DENGAN MENERAPKAN PEMBELAJARAN INKUIRI TERBIMBING

This study aimed to describe the implementation of learning process while improving learning outcomes and students’ responses using a guided inquiry-based learning model on heat and transfer. This research method uses pre-experimental research with a research design of one group pretest and posttest with a research target of 22 students of class VII SMP Semen Gresik. The techniques of collecting data were observation, tests, and questionnaire and then was analyzed quantitatively and descriptively. The results that were obtained showed that the mode value of the results of the implementation of learning activities at the first and second meetings was included in the criteria for implementing very good learning, while the results of the students’ responses showed a positive response with the acquisition of very good criteria. The result of N-Gain test analysis acquire in the medium category so that students learning outcomes experienced a significant increase. Based on the description above, it can be concluded that there is an effect of guided inquiry-based learning in improving the learning outcomes of Class VII Junior High School students in the matter of heat and its transfer. &nbsp

Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective

The transfer learning and domain adaptation problems originate from a distribution mismatch between the source and target data distribution. The causes of such mismatch are traditionally considered different. Thus, transfer learn-ing and domain adaptation algorithms are designed to ad-dress different issues, and cannot be used in both settings unless substantially modified. Still, one might argue that these problems are just different declinations of learning to learn, i.e. the ability to leverage over prior knowledge when attempting to solve a new task. We propose a learning to learn framework able to lever-age over source data regardless of the origin of the distri-bution mismatch. We consider prior models as experts, and use their output confidence value as features. We use them to build the new target model, combined with the features from the target data through a high-level cue integration scheme. This results in a class of algorithms usable in a plug-and-play fashion over any learning to learn scenario, from binary and multi-class transfer learning to single and multiple source domain adaptation settings. Experiments on several public datasets show that our approach consis-tently achieves the state of the art. 1