Search Results for author: Yifan Ding

Found 14 papers, 6 papers with code

H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation

no code implementations2 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.

Image Segmentation Instance Segmentation +1

CTIN: Robust Contextual Transformer Network for Inertial Navigation

1 code implementation3 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.

Multi-Task Learning

Momentum Centering and Asynchronous Update for Adaptive Gradient Methods

2 code implementations 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.

Image Classification

Posthoc Verification and the Fallibility of the Ground Truth

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.

Entity Linking

Reddit Entity Linking Dataset

no code implementations4 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.

Entity Linking

AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

7 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.

Image Classification Language Modelling

HetSeq: Distributed GPU Training on Heterogeneous Infrastructure

1 code implementation25 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.

Image Classification Language Modelling +1

Self-supervised learning for audio-visual speaker diarization

no code implementations13 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.

Self-Supervised Learning speaker-diarization +2

Defending Against Adversarial Attacks Using Random Forests

no code implementations16 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.

Frame-Recurrent Video Inpainting by Robust Optical Flow Inference

no code implementations8 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.

Image Inpainting Optical Flow Estimation +1

A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

no code implementations8 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.

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