Welcome to the 4th IEEE BDTL! -- Heterogeneous Representation and Networks Although widely applied on considerable scientific research, conventional statistical machine learning revolves on a simplified assumption that the training data, from which the algorithms learn, are drawn i.i.d. from the same distribution as the test data, to which the learned models are applied. This assumption, being broken down by numerous real-world applications nowadays, especially with the emergence of large-scale data from the private internal data, or the public Internet, has fundamentally restricted the development of practical learning algorithms. More examples include but are not limited to: (1) speech recognition when speakers have strong dialects from different countries, regions, genders, or aging groups; (2) surveillance system where captured suspects' faces are side view, under unknown lighting conditions, in low-resolutions, different from the conditions of the registered faces in the system. We may attribute above issues to heterogeneous feature representation of different data/object types without explicitly modeling the connections between data samples. This is also the research target of recently developed transfer learning (TL) that bridges the gap between the feature space of heterogeneous concepts. When it comes to the structured data, e.g., social networks, academic networks, however, a joint modeling of both data attributes/features, and their interaction represented by networks becomes necessary and feasible. This is exactly the motivation of recent research in the field of heterogeneous information networks (HIN). It is thus our intention to promote conventional transfer learning in vectorized feature space and extend to heterogenous information network, where each object is characterized by both object attributes and link types with other objects. This aligns well with most existing real-world information networks and their learning tasks, and becomes particularly useful under the context of “Big Data”, where knowledge transfer and reuse of well-established or labeled objects could help other linked objects in the variety of machine learning tasks. This half-day workshop (in conjunction with IEEE Big Data 2019) is a continuation of our past Big Data Transfer Learning (BDTL) workshops (1st BDTL, 2nd BDTL, 3rd BDTL) which will provide a focused international forum to bring together researchers and research groups to review the status of transfer learning on both conventional vectorized features and heterogeneous information networks. Specifically, we will discuss the challenges given large-scale networks, new deep computing models, and explore future directions particularly in the unconstrained social environments, such as social media data in the cloud, Facebook and YouTube applications. The workshop will consist of one to two invited talks together with peer-reviewed regular papers (oral and poster). This half-day workshop (in conjunction with IEEE Big Data 2019) is a continuation of our past Big Data Transfer Learning (BDTL) workshops (1st BDTL, 2nd BDTL, 3rd BDTL) which will provide a focused international forum to bring together researchers and research groups to review the status of transfer learning on both conventional vectorized features and heterogeneous information networks. Specifically, we will discuss the challenges given large-scale networks, new deep computing models, and explore future directions particularly in the unconstrained social environments, such as social media data in the cloud, Facebook and YouTube applications. The workshop will consist of one to two invited talks together with peer-reviewed regular papers (oral and poster). Original high-quality papers are solicited on a wide range of topics including:
General Chairs
Workshop Co-Chairs
Tentative Program Committee
Web and Publicity Co-Chairs
Date: Dec 9th, 2019 Location: Mt Washington, The Westin Bonaventure Hotel & Suites, Los Angeles Workshop Schedule:
The first workshop with this name was held in 2016, in conjunction with 2016 IEEE International Conference on Big Data (IEEE BigData 2016). So far, it has been successfully held 3 times. The homepages of previous two BDTL are as follows: |