Lifelong Machine Learning with Data Efficiency and Knowledge Retention
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Modern machine learning methods and their applications in computer vision are known to crave for large amounts of training data to reach their full potential. Because training data is mostly obtained through humans who manually label samples, it induces a ...
Multimedia databases are growing rapidly in size in the digital age. To increase the value of these data and to enhance the user experience, there is a need to make these videos searchable through automatic indexing. Because people appearing and talking in ...
The very shallow geothermal potential (vSGP) is increasingly recognized as a viable resource for providing clean thermal energy in urban and rural areas. This is primarily due to its reliability, low-cost installation, easy maintenance, and little constrai ...
Learning a discriminative voice embedding allows speaker turns to be compared directly and efficiently, which is crucial for tasks such as diarization and verification. This paper investigates several transfer learning approaches to improve a voice embeddi ...
Depth matching well logs acquired from multiple logging passes in a single well has been a longstanding challenge for the industry. The existing approaches employed in commercial platforms are typically based on classical cross-correlation and covariance m ...
Learning-based algorithms have gained great popularity in communications since they often outperform even carefully engineered solutions by learning from training samples. In this paper, we show that the selection of appropriate training examples can be im ...
Science is in revolution. The formidable scientific and technological developments of the last century have dramatically transformed the way in which we conduct scientific research. The knowledge and applications that science produces has profound conseque ...
In this paper, we explore various approaches for semi-
supervised learning in an end-to-end automatic speech recog-
nition (ASR) framework. The first step in our approach in-
volves training a seed model on the limited amount of labelled
data. Additional u ...
Open-ended learning environments (OELEs) allow students to freely interact with the content and to discover important principles and concepts of the learning domain on their own. However, only some students possess the necessary skills for efficient and ef ...
Short-term field study involves groups of students working in an off-campus (sometimes international) setting, and often involves working on realistic, open-ended problems, in interaction with a host community. Such learning experiences are intended to dev ...