Search Results for author: Chang-Tien Lu

Found 35 papers, 14 papers with code

Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges

no code implementations27 Mar 2024 Yanshen Sun, Jianfeng He, Limeng Cui, Shuo Lei, Chang-Tien Lu

Studies highlight the gap in the deceptive power of LLM-generated fake news with and without human assistance, yet the potential of prompting techniques has not been fully explored.

Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices

1 code implementation4 Dec 2023 Shengkun Wang, Yangxiao Bai, Taoran Ji, Kaiqun Fu, Linhan Wang, Chang-Tien Lu

We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.

Stock Market Prediction

ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction

1 code implementation28 Oct 2023 Shengkun Wang, Yangxiao Bai, Kaiqun Fu, Linhan Wang, Chang-Tien Lu, Taoran Ji

For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being.

Sentiment Analysis

Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Network

no code implementations29 Sep 2023 Siji Chen, Yanshen Sun, Peihan Li, Lifeng Zhou, Chang-Tien Lu

However, it has been observed that relying solely on the states of immediate neighbors is insufficient to imitate a centralized control policy.

Imitation Learning

Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation

1 code implementation11 Sep 2023 Linhan Wang, Shuo Lei, Jianfeng He, Shengkun Wang, Min Zhang, Chang-Tien Lu

To tackle these challenges, we propose a Self-Correlation and Cross-Correlation Learning Network for the few-shot remote sensing image semantic segmentation.

Few-Shot Learning Segmentation +1

TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation

1 code implementation3 Jun 2023 Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, Chang-Tien Lu

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance.

Few-Shot Text Classification Meta-Learning +1

Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban Planning

no code implementations8 Apr 2023 Dongjie Wang, Chang-Tien Lu, Yanjie Fu

The two fields of urban planning and artificial intelligence (AI) arose and developed separately.

DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic Networks

1 code implementation21 Mar 2023 Yanshen Sun, Kaiqun Fu, Chang-Tien Lu

Therefore, DG-Trans is equipped with dual abilities that extract spatiotemporal dependency and identify anomaly nodes affected by incidents while removing noise introduced by benign nodes.

Decision Making Graph Learning +1

Memetic algorithms for Spatial Partitioning problems

1 code implementation4 Aug 2022 Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Chang-Tien Lu, Naren Ramakrishnan

However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems.

Sampling-based techniques for designing school boundaries

1 code implementation8 Jun 2022 Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Chang-Tien Lu, Naren Ramakrishnan

Motivated by these recent developments, we develop a set of similar sampling techniques for designing school boundaries based on the flip proposal.

Deep diffusion-based forecasting of COVID-19 by incorporating network-level mobility information

1 code implementation9 Nov 2021 Padmaksha Roy, Shailik Sarkar, Subhodip Biswas, Fanglan Chen, Zhiqian Chen, Naren Ramakrishnan, Chang-Tien Lu

The Gaussian Mixture Model layer is implemented to consider the multimodal nature of the real-time data while learning from multiple related time series.

Time Series Time Series Analysis

Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning

no code implementations22 Sep 2021 Dongjie Wang, Kunpeng Liu, David Mohaisen, Pengyang Wang, Chang-Tien Lu, Yanjie Fu

Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models.

Representation Learning

Semantic Editing On Segmentation Map Via Multi-Expansion Loss

no code implementations16 Oct 2020 Jianfeng He, Xuchao Zhang, Shuo Lei, Shuhui Wang, Qingming Huang, Chang-Tien Lu, Bei Xiao

Each MEx area has the mask area of the generation as the majority and the boundary of original context as the minority.

Image Inpainting Segmentation

Reimagining City Configuration: Automated Urban Planning via Adversarial Learning

no code implementations22 Aug 2020 Dongjie Wang, Yanjie Fu, Pengyang Wang, Bo Huang, Chang-Tien Lu

The objective is then to propose an adversarial learning framework that can automatically generate such tensor for an unplanned area.

Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations

no code implementations3 Jul 2020 Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, Chang-Tien Lu

Despite the great progress made by deep neural networks in the semantic segmentation task, traditional neural-networkbased methods typically suffer from a shortage of large amounts of pixel-level annotations.

Few-Shot Semantic Segmentation Segmentation +1

Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

no code implementations27 Feb 2020 Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu

Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.

Image Classification Natural Language Understanding +1

TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction

no code implementations20 Nov 2019 Kaiqun Fu, Taoran Ji, Liang Zhao, Chang-Tien Lu

In this paper, we propose a traffic incident duration prediction model that simultaneously predicts the impact of the traffic incidents and identifies the critical groups of temporal features via a multi-task learning framework.

Management Multi-Task Learning

Modeling the Relationship between User Comments and Edits in Document Revision

no code implementations IJCNLP 2019 Xuchao Zhang, Dheeraj Rajagopal, Michael Gamon, Sujay Kumar Jauhar, Chang-Tien Lu

Thus, in this paper we explore the relationship between comments and edits by defining two novel, related tasks: Comment Ranking and Edit Anchoring.

Management

Mitigating Uncertainty in Document Classification

1 code implementation NAACL 2019 Xuchao Zhang, Fanglan Chen, Chang-Tien Lu, Naren Ramakrishnan

The uncertainty measurement of classifiers' predictions is especially important in applications such as medical diagnoses that need to ensure limited human resources can focus on the most uncertain predictions returned by machine learning models.

Document Classification General Classification +2

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

1 code implementation22 May 2019 Taoran Ji, Zhiqian Chen, Nathan Self, Kaiqun Fu, Chang-Tien Lu, Naren Ramakrishnan

For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent.

Citation Prediction Point Processes

Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning

no code implementations14 Feb 2019 Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Sai Manoj P. D., Houman Homayoun, Liang Zhao, Chang-Tien Lu

Deobfuscation runtime could have a large span ranging from few milliseconds to thousands of years or more, depending on the number and layouts of the ICs and camouflaged gates.

Robust Regression via Online Feature Selection under Adversarial Data Corruption

no code implementations5 Feb 2019 Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu

The presence of data corruption in user-generated streaming data, such as social media, motivates a new fundamental problem that learns reliable regression coefficient when features are not accessible entirely at one time.

feature selection regression

Water Disaggregation via Shape Features based Bayesian Discriminative Sparse Coding

no code implementations26 Aug 2018 Bingsheng Wang, Xuchao Zhang, Chang-Tien Lu, Feng Chen

As the issue of freshwater shortage is increasing daily, it is critical to take effective measures for water conservation.

Distributed Self-Paced Learning in Alternating Direction Method of Multipliers

no code implementations6 Jul 2018 Xuchao Zhang, Liang Zhao, Zhiqian Chen, Chang-Tien Lu

One key issue in SPL is the training process required for each instance weight depends on the other samples and thus cannot easily be run in a distributed manner in a large-scale dataset.

Learning to Fuse Music Genres with Generative Adversarial Dual Learning

1 code implementation5 Dec 2017 Zhiqian Chen, Chih-Wei Wu, Yen-Cheng Lu, Alexander Lerch, Chang-Tien Lu

FusionGAN is a novel genre fusion framework for music generation that integrates the strengths of generative adversarial networks and dual learning.

Music Generation

Multimodal Storytelling via Generative Adversarial Imitation Learning

no code implementations5 Dec 2017 Zhiqian Chen, Xuchao Zhang, Arnold P. Boedihardjo, Jing Dai, Chang-Tien Lu

Deriving event storylines is an effective summarization method to succinctly organize extensive information, which can significantly alleviate the pain of information overload.

Imitation Learning

Online and Distributed Robust Regressions under Adversarial Data Corruption

no code implementations2 Oct 2017 Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu

In today's era of big data, robust least-squares regression becomes a more challenging problem when considering the adversarial corruption along with explosive growth of datasets.

regression

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