no code implementations • 5 Feb 2024 • Saiful Khan, Scott Jones, Benjamin Bach, Jaehoon Cha, Min Chen, Julie Meikle, Jonathan C Roberts, Jeyan Thiyagalingam, Jo Wood, Panagiotis D. Ritsos
Motivated initially by the need to communicate time series data during the COVID-19 pandemic, we developed a novel computer-assisted method for meta-authoring of stories, which enables the design of storyboards that include feature-action patterns in anticipation of potential features that may appear in dynamically arrived or selected data.
no code implementations • 20 Feb 2022 • Jaehoon Cha, Jeyan Thiyagalingam
Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations in group-theory.
no code implementations • 3 Jun 2019 • Jaehoon Cha, Kyeong Soo Kim, Sanghyuk Lee
Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal(i. e., no hierarchy) and exclusive of one another (i. e., no overlap).
no code implementations • 24 Jan 2019 • Jaehoon Cha, Kyeong Soo Kim, Sanghyuk Lee
Noting the importance of the latent variables in inference and learning, we propose a novel framework for autoencoders based on the homeomorphic transformation of latent variables, which could reduce the distance between vectors in the transformed space, while preserving the topological properties of the original space, and investigate the effect of the latent space transformation on learning generative models and denoising corrupted data.
2 code implementations • 3 Oct 2017 • Kyeong Soo Kim, Ruihao Wang, Zhenghang Zhong, Zikun Tan, Haowei Song, Jaehoon Cha, Sanghyuk Lee
One of key technologies for future large-scale location-aware services in access is a scalable indoor localization technique.
Networking and Internet Architecture C.2.1; I.2.6; I.5.1; I.5.2; I.5.4; I.5.5