no code implementations • 21 Feb 2024 • Yuchen Liang, Peizhong Ju, Yingbin Liang, Ness Shroff
In this paper, we establish the convergence guarantee for substantially larger classes of distributions under discrete-time diffusion models and further improve the convergence rate for distributions with bounded support.
4 code implementations • NeurIPS 2023 • Benjamin Hoover, Yuchen Liang, Bao Pham, Rameswar Panda, Hendrik Strobelt, Duen Horng Chau, Mohammed J. Zaki, Dmitry Krotov
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory.
no code implementations • 1 Nov 2022 • Yuchen Liang, Venugopal V. Veeravalli
The problem of quickest change detection in a sequence of independent observations is considered.
no code implementations • 30 Aug 2022 • Yuchen Liang, Dmitry Krotov, Mohammed J. Zaki
The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information.
no code implementations • 13 Nov 2021 • Yuchen Liang, Mohammed J. Zaki
Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document.
no code implementations • 4 Oct 2021 • Yuchen Liang, Alexander G. Tartakovsky, Venugopal V. Veeravalli
For the case where the post-change distributions have parametric uncertainty, a window-limited (WL) generalized likelihood-ratio (GLR) CuSum procedure is developed and is shown to achieve the universal lower bound asymptotically.
no code implementations • 25 Aug 2021 • Yuchen Liang, Venugopal V. Veeravalli
For the case where the pre-change distribution is known, a test is derived that asymptotically minimizes the worst-case detection delay over all possible post-change distributions, as the false alarm rate goes to zero.
2 code implementations • ICLR 2021 • Yuchen Liang, Chaitanya K. Ryali, Benjamin Hoover, Leopold Grinberg, Saket Navlakha, Mohammed J. Zaki, Dmitry Krotov
In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task.
no code implementations • 14 Jan 2021 • Yuchen Liang, Venugopal V. Veeravalli
We study the problem of quickest detection of a change in the mean of an observation sequence, under the assumption that both the pre- and post-change distributions have bounded support.
no code implementations • 6 Sep 2018 • Ning Xu, Linjie Yang, Yuchen Fan, Dingcheng Yue, Yuchen Liang, Jianchao Yang, Thomas Huang
End-to-end sequential learning to explore spatialtemporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.
4 code implementations • ECCV 2018 • Ning Xu, Linjie Yang, Yuchen Fan, Jianchao Yang, Dingcheng Yue, Yuchen Liang, Brian Price, Scott Cohen, Thomas Huang
End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.
Ranked #12 on Video Object Segmentation on YouTube-VOS 2018 (F-Measure (Unseen) metric)