• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    March 16, 2021: Mr. Le Trung Thanh (AVITECH), Adaptive algorithms for tensor tracking

    Tensor decomposition that factorizes a tensor (i.e., multiway arrays) into a sequence of basic components has become a popular analysis tool for processing high-dimensional and multivariate data. In online applications, data acquisition is often a time-varying process in which data are serially collected or changing with time. This has led to defining a variant of tensor decomposition, namely adaptive (online) tensor decomposition or tensor tracking. In this seminar, we introduce two provable adaptive tensor tracking algorithms under the two wellknown tensor formats: Canonical Polyadic (CP) and Tucker. Both algorithms are fast and require a low computational complexity and memory storage. A unified convergence analysis is presented for the proposed algorithms to justify their performance. Experiments indicate that the two proposed algorithms are capable of the adaptive tensor decomposition problem with competitive performance on both synthetic and real data

    Speaker: Mr. Le Trung Thanh, AVITECH

    Time: 15:30, Tuesday, March 16, 2021

    Venue: G2-315, 144 Xuan Thuy, Cau Giay, Hanoi


    Le Trung Thanh received B.Sc. and M.Sc. degrees in Electronics and Telecommunications from the VNU University of Engineering and Technology (VNU-UET), a member of Vietnam National University, Hanoi (VNU) in 2016 and 2018 respectively. He is now pursuing his Ph.D. study at the University of Orleans, France. His research interests include signal processing, subspace tracking, tensor analysis, and system identification.


    February 2, 2024: Dr. Khoa D. Doan (Vin Univeristy) Toward Reliable and Practical Machine Learning Applications

    While Machine Learning (ML) has rapidly transformed several domains and applications with incredible successes, there are also important areas where the progress is significantly slower. Specifically, there exists a widened complexity gap between the methods currently investigated in research and those used in practice in these areas. One reason is that many algorithms, despite achieving […]

    February 2, 2024: Prof. Heng Ji (University of Illinois at Urbana-Champaign), Combating with Misinformation and Cancer: A Unified Multimodal AI Approach to Healthy and Happy Life

    A research overview of ongoing research projects, especially focusing on two that are most related to the VinUni-UIUC Smart Health Center: (1) Misinformation Detection and Trustworthy Large Language Models; (2) Joint Natural Language and Molecule Learning for Drug Discovery. Unsurprisingly these two seemingly different research problems can be tackled with a unified approach based on […]

    January 11, 2024: Dr. Le Duc Trong (FIT-UET) Resilient Multimodal Learning for Multimodal Emotion Recognition in the Presence of Incomplete Modalities

    Multimodal Emotion Recognition in Conversation (Multimodal ERC) is a critical area of research for interpreting human communication in diverse applications. Nevertheless, the persistent issue of uncertain missing modalities poses a major hurdle, hampering the development of robust Multimodal ERC models. Existing approaches face limitations in effectively leveraging a fusion of diverse data modalities encompassing audio, […]