no code implementations • 2 Jun 2022 • Peixian Liang, Yizhe Zhang, Yifan Ding, Jianxu Chen, Chinedu S. Madukoma, Tim Weninger, Joshua D. Shrout, Danny Z. Chen
We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances.
no code implementations • 24 Mar 2022 • Ye Jia, Yifan Ding, Ankur Bapna, Colin Cherry, Yu Zhang, Alexis Conneau, Nobuyuki Morioka
End-to-end speech-to-speech translation (S2ST) without relying on intermediate text representations is a rapidly emerging frontier of research.
1 code implementation • 3 Dec 2021 • Bingbing Rao, Ehsan Kazemi, Yifan Ding, Devu M Shila, Frank M. Tucker, Liqiang Wang
Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial measurement units (IMU) measurements.
1 code implementation • NeurIPS 2021 • Juntang Zhuang, Yifan Ding, Tommy Tang, Nicha Dvornek, Sekhar Tatikonda, James S. Duncan
We demonstrate that ACProp has a convergence rate of $O(\frac{1}{\sqrt{T}})$ for the stochastic non-convex case, which matches the oracle rate and outperforms the $O(\frac{logT}{\sqrt{T}})$ rate of RMSProp and Adam.
1 code implementation • NAACL (DADC) 2022 • Yifan Ding, Nicholas Botzer, Tim Weninger
Metrics used in these evaluations are tied to the availability of well-defined ground truth labels, and these metrics typically do not allow for inexact matches.
no code implementations • 4 Jan 2021 • Nicholas Botzer, Yifan Ding, Tim Weninger
We introduce and make publicly available an entity linking dataset from Reddit that contains 17, 316 linked entities, each annotated by three human annotators and then grouped into Gold, Silver, and Bronze to indicate inter-annotator agreement.
no code implementations • NeurIPS Workshop DL-IG 2020 • Juntang Zhuang, Tommy Tang, Sekhar Tatikonda, Nicha C Dvornek, Yifan Ding, Xenophon Papademetris, James S Duncan
We propose AdaBelief optimizer to simultaneously achieve three goals: fast convergence as in adaptive methods, good generalization as in SGD, and training stability.
8 code implementations • NeurIPS 2020 • Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar Tatikonda, Nicha Dvornek, Xenophon Papademetris, James S. Duncan
Viewing the exponential moving average (EMA) of the noisy gradient as the prediction of the gradient at the next time step, if the observed gradient greatly deviates from the prediction, we distrust the current observation and take a small step; if the observed gradient is close to the prediction, we trust it and take a large step.
1 code implementation • 25 Sep 2020 • Yifan Ding, Nicholas Botzer, Tim Weninger
The present work describes HetSeq, a software package adapted from the popular PyTorch package that provides the capability to train large neural network models on heterogeneous infrastructure.
no code implementations • 13 Feb 2020 • Yifan Ding, Yong Xu, Shi-Xiong Zhang, Yahuan Cong, Liqiang Wang
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems.
no code implementations • 16 Jun 2019 • Yifan Ding, Liqiang Wang, huan zhang, Jin-Feng Yi, Deliang Fan, Boqing Gong
As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world.
no code implementations • 8 May 2019 • Yifan Ding, Chuan Wang, Haibin Huang, Jiaming Liu, Jue Wang, Liqiang Wang
Compared with image inpainting, performing this task on video presents new challenges such as how to preserving temporal consistency and spatial details, as well as how to handle arbitrary input video size and length fast and efficiently.
1 code implementation • 29 Apr 2019 • Jiaming Liu, Chi-Hao Wu, Yuzhi Wang, Qin Xu, Yuqian Zhou, Haibin Huang, Chuan Wang, Shaofan Cai, Yifan Ding, Haoqiang Fan, Jue Wang
In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising.
no code implementations • 8 Feb 2018 • Yifan Ding, Liqiang Wang, Deliang Fan, Boqing Gong
In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability.