no code implementations • 18 Apr 2024 • Zixiang Chen, Jun Han, YongQian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu
Electronic health records (EHRs) are a pivotal data source that enables numerous applications in computational medicine, e. g., disease progression prediction, clinical trial design, and health economics and outcomes research.
1 code implementation • 29 May 2023 • Yuhang Zang, Wei Li, Jun Han, Kaiyang Zhou, Chen Change Loy
Moreover, we present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts, so as to locate, identify, and associate visual objects with language inputs for human-AI interaction.
no code implementations • 21 Sep 2022 • Zhaoqiang Liu, Jun Han
We show that when there is no representation error and the sensing vectors are Gaussian, roughly $O(k \log L)$ samples suffice to ensure that a PGD algorithm converges linearly to a point achieving the optimal statistical rate using arbitrary initialization.
no code implementations • 13 Apr 2022 • Chaoli Wang, Jun Han
Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research.
1 code implementation • ICLR 2022 • Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett
We perform experiments on various image datasets for spiked matrix and phase retrieval models, and illustrate performance gains of our method to the classic power method and the truncated power method devised for sparse principal component analysis.
no code implementations • 8 Aug 2021 • Zhaoqiang Liu, Subhroshekhar Ghosh, Jun Han, Jonathan Scarlett
In 1-bit compressive sensing, each measurement is quantized to a single bit, namely the sign of a linear function of an unknown vector, and the goal is to accurately recover the vector.
no code implementations • 6 Aug 2021 • Kai Feng, Weixing Li, Jun Han, Feng Pan, Dongdong Zheng
At present, most of the rotating object detection datasets focus on the field of remote sensing, and these images are usually shot in high-altitude scenes.
no code implementations • 10 Jul 2021 • Hao Zheng, Jun Han, Hongxiao Wang, Lin Yang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen
Unlike the current literature on task-specific self-supervised pretraining followed by supervised fine-tuning, we utilize SSL to learn task-agnostic knowledge from heterogeneous data for various medical image segmentation tasks.
no code implementations • ICLR 2021 • Jun Han, Martin Renqiang Min, Ligong Han, Li Erran Li, Xuan Zhang
Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as images in an unsupervised learning framework.
1 code implementation • IEEE Transactions on Circuits and Systems I: Regular Papers 2020 • Guozhu Xin, Jun Han, Tianyu Yin, Yuchao Zhou, Jianwei Yang, Xu Cheng, Xiaoyang Zeng
In the 5G era, massive devices need to be securely connected to the edge of communication networks, while emerging quantum computers can easily crack the traditional public-key ciphers.
Hardware Architecture
no code implementations • 6 Apr 2020 • Madhumitha Harishankar, Jun Han, Sai Vineeth Kalluru Srinivas, Faisal Alqarni, Shi Su, Shijia Pan, Hae Young Noh, Pei Zhang, Marco Gruteser, Patrick Tague
and yields 100% lane classification accuracy with 200 meters of driving data, achieving over 90% with just 100 m (correspondingly to roughly one minute of driving).
no code implementations • 7 Mar 2020 • Jun Han
Approximate inference in probability models is a fundamental task in machine learning.
no code implementations • 1 Mar 2020 • Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu
In addition, such transform can be straightforwardly employed in gradient-free kernelized Stein discrepancy to perform goodness-of-fit (GOF) test on discrete distributions.
no code implementations • 17 Dec 2019 • Iqbal H. Sarker, Alan Colman, Jun Han, Asif Irshad Khan, Yoosef B. Abushark, Khaled Salah
This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones.
no code implementations • 2 Sep 2019 • Iqbal H. Sarker, Alan Colman, Jun Han, A. S. M. Kayes, Paul Watters
Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar.
no code implementations • NeurIPS 2019 • Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt
The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy.
no code implementations • 27 Sep 2018 • Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt
We propose a variational inference approach to deep probabilistic video compression.
no code implementations • ICML 2018 • Jun Han, Qiang Liu
Stein variational gradient decent (SVGD) has been shown to be a powerful approximate inference algorithm for complex distributions.
no code implementations • 12 Oct 2017 • Iqbal H. Sarker, Muhammad Ashad Kabir, Alan Colman, Jun Han
In order to improve the classification accuracy, we effectively identify noisy instances from the training dataset by analyzing the behavioral patterns of individuals.
no code implementations • 18 Apr 2017 • Jun Han, Qiang Liu
We propose a novel adaptive importance sampling algorithm which incorporates Stein variational gradient decent algorithm (SVGD) with importance sampling (IS).
no code implementations • NeurIPS 2016 • Jun Han, Qiang Liu
In distributed, or privacy-preserving learning, we are often given a set of probabilistic models estimated from different local repositories, and asked to combine them into a single model that gives efficient statistical estimation.