Search Results for author: Ting Yu

Found 14 papers, 5 papers with code

Intelligent Computing: The Latest Advances, Challenges and Future

no code implementations21 Nov 2022 Shiqiang Zhu, Ting Yu, Tao Xu, Hongyang Chen, Schahram Dustdar, Sylvain Gigan, Deniz Gunduz, Ekram Hossain, Yaochu Jin, Feng Lin, Bo Liu, Zhiguo Wan, Ji Zhang, Zhifeng Zhao, Wentao Zhu, Zuoning Chen, Tariq Durrani, Huaimin Wang, Jiangxing Wu, Tongyi Zhang, Yunhe Pan

In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications.

Ten Years after ImageNet: A 360° Perspective on AI

no code implementations1 Oct 2022 Sanjay Chawla, Preslav Nakov, Ahmed Ali, Wendy Hall, Issa Khalil, Xiaosong Ma, Husrev Taha Sencar, Ingmar Weber, Michael Wooldridge, Ting Yu

The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has widened the application space of AI.

Decision Making Fairness +1

Finding MNEMON: Reviving Memories of Node Embeddings

no code implementations14 Apr 2022 Yun Shen, Yufei Han, Zhikun Zhang, Min Chen, Ting Yu, Michael Backes, Yang Zhang, Gianluca Stringhini

Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of graph neural networks.

Graph Embedding

Permutation-Invariant Subgraph Discovery

1 code implementation2 Apr 2021 Raghvendra Mall, Shameem A. Parambath, Han Yufei, Ting Yu, Sanjay Chawla

PSPI can be viewed as a robust formulation of the permutation inference or graph matching, where the objective is to find a permutation between two graphs under the assumption that a set of edges may have undergone a perturbation due to an underlying cause.

Graph Matching

Time-Window Group-Correlation Support vs. Individual Features: A Detection of Abnormal Users

1 code implementation27 Dec 2020 Lun-Pin Yuan, Euijin Choo, Ting Yu, Issa Khalil, Sencun Zhu

Autoencoder-based anomaly detection methods have been used in identifying anomalous users from large-scale enterprise logs with the assumption that adversarial activities do not follow past habitual patterns.

Anomaly Detection

Spatial-Temporal Alignment Network for Action Recognition and Detection

no code implementations4 Dec 2020 Junwei Liang, Liangliang Cao, Xuehan Xiong, Ting Yu, Alexander Hauptmann

The experimental results show that the STAN model can consistently improve the state of the arts in both action detection and action recognition tasks.

Action Detection Action Recognition

Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention

no code implementations26 Oct 2020 Linlin Hou, Ji Zhang, Ou wu, Ting Yu, Zhen Wang, Zhao Li, Jianliang Gao, Yingchun Ye, Rujing Yao

We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.

Data Augmentation

Macroscopic nonlocal entanglement generation of two hybrid massive magnon systems

no code implementations11 Jun 2020 Da-Wei Luo, Xiao-Feng Qian, Ting Yu

We investigate dynamical generation of macroscopic nonlocal entanglements between two remote massive magnon-superconducting-circuit hybrid systems.

Quantum Physics

The Garden of Forking Paths: Towards Multi-Future Trajectory Prediction

1 code implementation CVPR 2020 Junwei Liang, Lu Jiang, Kevin Murphy, Ting Yu, Alexander Hauptmann

The first contribution is a new dataset, created in a realistic 3D simulator, which is based on real world trajectory data, and then extrapolated by human annotators to achieve different latent goals.

Autonomous Driving Human motion prediction +5

Quantum Unsupervised and Supervised Learning on Superconducting Processors

1 code implementation10 Sep 2019 Abhijat Sarma, Rupak Chatterjee, Kaitlin Gili, Ting Yu

Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility.

BIG-bench Machine Learning Clustering

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