Search Results for author: Dongyu Liu

Found 12 papers, 6 papers with code

CATP: Context-Aware Trajectory Prediction with Competition Symbiosis

no code implementations10 Jul 2024 Jiang Wu, Dongyu Liu, YuChen Lin, Yingcai Wu

To address this issue, we propose a ``manager-worker'' framework to unleash the full potential of contextual information and construct CATP model, an implementation of the framework for Context-Aware Trajectory Prediction.

Trajectory Prediction

AER: Auto-Encoder with Regression for Time Series Anomaly Detection

3 code implementations27 Dec 2022 Lawrence Wong, Dongyu Liu, Laure Berti-Equille, Sarah Alnegheimish, Kalyan Veeramachaneni

We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method.

Anomaly Detection Benchmarking +3

The Need for Interpretable Features: Motivation and Taxonomy

no code implementations23 Feb 2022 Alexandra Zytek, Ignacio Arnaldo, Dongyu Liu, Laure Berti-Equille, Kalyan Veeramachaneni

Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their features.

Decision Making

VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models

1 code implementation4 Aug 2021 Furui Cheng, Dongyu Liu, Fan Du, Yanna Lin, Alexandra Zytek, Haomin Li, Huamin Qu, Kalyan Veeramachaneni

Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks.

Decision Making

Superresolving second-order correlation imaging using synthesized colored noise speckles

no code implementations11 Feb 2021 Zheng Li, Xiaoyu Nie, Fan Yang, Xiangpei Liu, Dongyu Liu, Xiaolong Dong, Xingchen Zhao, Tao Peng, M. Suhail Zubairy, Marlan O. Scully

We present a novel method to synthesize non-trivial speckles that can enable superresolving second-order correlation imaging.

Optics Image and Video Processing

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks

5 code implementations16 Sep 2020 Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni

However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations.

Benchmarking Time Series +2

DeepTracker: Visualizing the Training Process of Convolutional Neural Networks

no code implementations26 Aug 2018 Dongyu Liu, Weiwei Cui, Kai Jin, YuXiao Guo, Huamin Qu

To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of training log.

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