no code implementations • 3 Mar 2024 • Matthew Dowling, Yuan Zhao, Il Memming Park
We introduce an amortized variational inference algorithm and structured variational approximation for state-space models with nonlinear dynamics driven by Gaussian noise.
no code implementations • 25 Jan 2024 • Patrick Lee, Alain Chirino Trujillo, Diana Cuevas Plancarte, Olumide Ebenezer Ojo, Xinyi Liu, Iyanuoluwa Shode, Yuan Zhao, Jing Peng, Anna Feldman
This study investigates the computational processing of euphemisms, a universal linguistic phenomenon, across multiple languages.
no code implementations • 1 Jun 2023 • Matthew Dowling, Yuan Zhao, Il Memming Park
In this work, we propose cvHM, a general inference framework for latent GP models leveraging Hida-Mat\'ern kernels and conjugate computation variational inference (CVI).
no code implementations • 31 May 2023 • Patrick Lee, Iyanuoluwa Shode, Alain Chirino Trujillo, Yuan Zhao, Olumide Ebenezer Ojo, Diana Cuevas Plancarte, Anna Feldman, Jing Peng
Transformers have been shown to work well for the task of English euphemism disambiguation, in which a potentially euphemistic term (PET) is classified as euphemistic or non-euphemistic in a particular context.
no code implementations • 18 May 2023 • Matthew Dowling, Yuan Zhao, Il Memming Park
Latent variable models have become instrumental in computational neuroscience for reasoning about neural computation.
no code implementations • 23 Mar 2023 • Huajie Chen, Tianqing Zhu, Yuan Zhao, Bo Liu, Xin Yu, Wanlei Zhou
By avoiding high-frequency artifacts and manipulating the frequency distribution of the embedded feature map, LIDS achieves improved robustness against attacks that distort the high-frequency components of container images.
no code implementations • 13 Jan 2023 • Xiaomeng Chu, Jiajun Deng, Yuan Zhao, Jianmin Ji, Yu Zhang, Houqiang Li, Yanyong Zhang
To this end, we propose OA-BEV, a network that can be plugged into the BEV-based 3D object detection framework to bring out the objects by incorporating object-aware pseudo-3D features and depth features.
no code implementations • 28 Mar 2021 • Xiguo Yuan, Yuan Zhao, Yang Guo, Linmei Ge, Wei Liu, Shiyu Wen, Qi Li, Zhangbo Wan, Peina Zheng, Tao Guo, Zhida Li, Martin Peifer, Yupeng Cun
In the past decade, a variety of methods have been developed for subclonal reconstruction using bulk tumor sequencing data.
no code implementations • 18 Jan 2021 • Jiale Zhao, Zijing Zhang, Yiming Li, Longzhu Cen, Yuan Zhao
Recognition of OAM modes unlimited by distance and sign of TC achieved by MIADLFR method can make optical communication and detection by OAM light much more attractive.
Optics Image and Video Processing Medical Physics
no code implementations • 26 Oct 2020 • Luanxuan Hou, Jie Cao, Yuan Zhao, Haifeng Shen, Jian Tang, Ran He
We propose a refinement stage for the pyramid features to further boost the accuracy of our network.
1 code implementation • NeurIPS 2020 • Diego M. Arribas, Yuan Zhao, Il Memming Park
The standard approach to fitting an autoregressive spike train model is to maximize the likelihood for one-step prediction.
no code implementations • 2 Sep 2020 • Matthew Dowling, Yuan Zhao, Il Memming Park
However, obtaining a satisfactory fit often requires burdensome model selection and fine tuning the form of the basis functions and their temporal span.
no code implementations • 21 Jul 2020 • Vishwali Mhasawade, Yuan Zhao, Rumi Chunara
Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individuals, but communities as a whole.
no code implementations • 17 Mar 2020 • Luanxuan Hou, Jie Cao, Yuan Zhao, Haifeng Shen, Yiping Meng, Ran He, Jieping Ye
At last, we proposed a differentiable auto data augmentation method to further improve estimation accuracy.
no code implementations • 23 Feb 2020 • Yuan Zhao, Jiasi Chen, Samet Oymak
We demonstrate that this leads to heterogenous confidence/accuracy behavior in the test data and is poorly handled by the standard calibration algorithms.
1 code implementation • 4 Jun 2019 • Yuan Zhao, Josue Nassar, Ian Jordan, Mónica Bugallo, Il Memming Park
Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series.
no code implementations • 19 Apr 2019 • Siyang Sun, Yingjie Yin, Xingang Wang, De Xu, Yuan Zhao, Haifeng Shen
To address this problem, we propose a multiple receptive field and small-object-focusing weakly-supervised segmentation network (MRFSWSnet) to achieve fast object detection.
no code implementations • 24 Feb 2019 • Yiwei Zhang, Chunbiao Zhu, Ge Li, Yuan Zhao, Haifeng Shen
A fast and effective motion deblurring method has great application values in real life.
no code implementations • 20 Nov 2018 • Wanxin Tian, Zixuan Wang, Haifeng Shen, Weihong Deng, Yiping Meng, Binghui Chen, Xiubao Zhang, Yuan Zhao, Xiehe Huang
We assume that problems inside are inadequate use of supervision information and imbalance between semantics and details at all level feature maps in CNN even with Feature Pyramid Networks (FPN).
no code implementations • 27 Sep 2018 • Guoqing Chao, Chengsheng Mao, Fei Wang, Yuan Zhao, Yuan Luo
We used the simulation data to verify the effectiveness of this method, and then we applied it to ICU mortality risk prediction and demonstrated its superiority over other conventional supervised NMF methods.
1 code implementation • 27 Jul 2017 • Yuan Zhao, Il Memming Park
It brings the challenge of learning both latent neural state and the underlying dynamical system because neither is known for neural systems a priori.
1 code implementation • 11 Apr 2016 • Yuan Zhao, Il Memming Park
In the V1 dataset, we find that vLGP achieves substantially higher performance than previous methods for predicting omitted spike trains, as well as capturing both the toroidal topology of visual stimuli space, and the noise-correlation.