Search Results for author: Wei-Yao Wang

Found 29 papers, 20 papers with code

Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion

no code implementations19 Mar 2024 Kuang-Da Wang, Wei-Yao Wang, Ping-Chun Hsieh, Wen-Chih Peng

(iii) To generate more realistic behavior, RallyNet leverages Geometric Brownian Motion (GBM) to model the interactions between players by introducing a valuable inductive bias for learning player behaviors.

Imitation Learning Inductive Bias +1

Root Cause Analysis In Microservice Using Neural Granger Causal Discovery

1 code implementation2 Feb 2024 Cheng-Ming Lin, Ching Chang, Wei-Yao Wang, Kuang-Da Wang, Wen-Chih Peng

To address these challenges, we propose RUN, a novel approach for root cause analysis using neural Granger causal discovery with contrastive learning.

Causal Discovery Contrastive Learning +2

A Survey on Self-Supervised Learning for Non-Sequential Tabular Data

1 code implementation2 Feb 2024 Wei-Yao Wang, Wei-Wei Du, Derek Xu, Wei Wang, Wen-Chih Peng

Recently, SSL has been a new trend in exploring the representation learning capability in the realm of tabular data, which is more challenging due to not having explicit relations for learning descriptive representations.

Contrastive Learning Descriptive +2

Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection

no code implementations27 Jan 2024 Wei-Yao Wang, Yu-Chieh Chang, Wen-Chih Peng

In this paper, we focus on neural fake news, which refers to content generated by neural networks aiming to mimic the style of real news to deceive people.

Fake News Detection Misinformation +1

ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton

1 code implementation18 Dec 2023 Wei-Yao Wang, Wen-Chih Peng, Wei Wang, Philip S. Yu

Agent forecasting systems have been explored to investigate agent patterns and improve decision-making in various domains, e. g., pedestrian predictions and marketing bidding.

Decision Making Marketing

SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter

1 code implementation17 Dec 2023 Ying-Ying Chang, Wei-Yao Wang, Wen-Chih Peng

In the dynamic and rapidly evolving world of social media, detecting anomalous users has become a crucial task to address malicious activities such as misinformation and cyberbullying.

Contrastive Learning Misinformation

TimeDRL: Disentangled Representation Learning for Multivariate Time-Series

1 code implementation7 Dec 2023 Ching Chang, Chiao-Tung Chan, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen

Multivariate time-series data in numerous real-world applications (e. g., healthcare and industry) are informative but challenging due to the lack of labels and high dimensionality.

Inductive Bias Representation Learning +4

RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training

1 code implementation15 Oct 2023 Yu-Chien Tang, Wei-Yao Wang, An-Zi Yen, Wen-Chih Peng

Existing intent detection approaches have highly relied on adaptively pre-training language models with large-scale datasets, yet the predominant cost of data collection may hinder their superiority.

Intent Detection Response Generation

DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal

1 code implementation2 Sep 2023 Wei-Wei Du, Wei-Yao Wang, Wen-Chih Peng

Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas.

Contrastive Learning Decision Making +1

LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

no code implementations16 Aug 2023 Ching Chang, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen

Recently, researchers have leveraged the representation learning transferability of pre-trained Large Language Models (LLMs) to handle limited non-linguistic datasets effectively.

Chatbot Multivariate Time Series Forecasting +4

ShuttleSet22: Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset

1 code implementation27 Jun 2023 Wei-Yao Wang, Wei-Wei Du, Wen-Chih Peng

We also hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge 2023 to boost researchers to tackle this problem.

Benchmarking

ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis

1 code implementation8 Jun 2023 Wei-Yao Wang, Yung-Chang Huang, Tsi-Ui Ik, Wen-Chih Peng

With the recent progress in sports analytics, deep learning approaches have demonstrated the effectiveness of mining insights into players' tactics for improving performance quality and fan engagement.

Action Detection Sports Analytics

Professional Basketball Player Behavior Synthesis via Planning with Diffusion

no code implementations7 Jun 2023 Xiusi Chen, Wei-Yao Wang, Ziniu Hu, Curtis Chou, Lam Hoang, Kun Jin, Mingyan Liu, P. Jeffrey Brantingham, Wei Wang

To accomplish reward-guided trajectory generation, conditional sampling is introduced to condition the diffusion model on the value function and conduct classifier-guided sampling.

Decision Making

Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification

1 code implementation12 Feb 2023 Wei-Wei Du, Hong-Wei Wu, Wei-Yao Wang, Wen-Chih Peng

Multi-modal fact verification has become an important but challenging issue on social media due to the mismatch between the text and images in the misinformation of news content, which has been addressed by considering cross-modalities to identify the veracity of the news in recent years.

Fact Verification Misinformation

Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal

no code implementations23 Dec 2022 Chih-Chia Li, Wei-Yao Wang, Wei-Wei Du, Wen-Chih Peng

However, existing methods only consider the real estate itself, ignoring the relation between the properties.

Graph Learning Relation

Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton

1 code implementation22 Nov 2022 Kai-Shiang Chang, Wei-Yao Wang, Wen-Chih Peng

To address these challenges, we first introduce the procedure of the Player Movements (PM) graph to exploit the structural movements of players with strategic relations.

Sports Analytics

Track2Vec: fairness music recommendation with a GPU-free customizable-driven framework

2 code implementations29 Oct 2022 Wei-Wei Du, Wei-Yao Wang, Wen-Chih Peng

Recommendation systems have illustrated the significant progress made in characterizing users' preferences based on their past behaviors.

Fairness Music Recommendation +2

Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verification

1 code implementation26 Jan 2022 Wei-Yao Wang, Wen-Chih Peng

In recent years, social media has enabled users to get exposed to a myriad of misinformation and disinformation; thus, misinformation has attracted a great deal of attention in research fields and as a social issue.

Fact Verification Misinformation

ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton

1 code implementation2 Dec 2021 Wei-Yao Wang, Hong-Han Shuai, Kai-Shiang Chang, Wen-Chih Peng

The increasing demand for analyzing the insights in sports has stimulated a line of productive studies from a variety of perspectives, e. g., health state monitoring, outcome prediction.

Position

Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches

2 code implementations14 Sep 2021 Wei-Yao Wang, Teng-Fong Chan, Hui-Kuo Yang, Chih-Chuan Wang, Yao-Chung Fan, Wen-Chih Peng

In this paper, we introduce a badminton language to fully describe the process of the shot and propose a deep learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result.

What Makes Training Multi-Modal Classification Networks Hard?

3 code implementations CVPR 2020 Wei-Yao Wang, Du Tran, Matt Feiszli

Consider end-to-end training of a multi-modal vs. a single-modal network on a task with multiple input modalities: the multi-modal network receives more information, so it should match or outperform its single-modal counterpart.

Action Classification Action Recognition In Videos +4

Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization

no code implementations1 May 2019 Rong Ge, Zhize Li, Wei-Yao Wang, Xiang Wang

Variance reduction techniques like SVRG provide simple and fast algorithms for optimizing a convex finite-sum objective.

How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning

no code implementations24 Apr 2019 Xinlei Pan, Wei-Yao Wang, Xiaoshuai Zhang, Bo Li, Jin-Feng Yi, Dawn Song

To the best of our knowledge, this is the first work to investigate privacy leakage in DRL settings and we show that DRL-based agents do potentially leak privacy-sensitive information from the trained policies.

Autonomous Driving Continuous Control +3

JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

2 code implementations ICML 2018 Yunchen Pu, Shuyang Dai, Zhe Gan, Wei-Yao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin

Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains).

Generative Adversarial Network

Adversarial Symmetric Variational Autoencoder

no code implementations NeurIPS 2017 Yunchen Pu, Wei-Yao Wang, Ricardo Henao, Liqun Chen, Zhe Gan, Chunyuan Li, Lawrence Carin

A new form of variational autoencoder (VAE) is developed, in which the joint distribution of data and codes is considered in two (symmetric) forms: ($i$) from observed data fed through the encoder to yield codes, and ($ii$) from latent codes drawn from a simple prior and propagated through the decoder to manifest data.

Triangle Generative Adversarial Networks

1 code implementation NeurIPS 2017 Zhe Gan, Liqun Chen, Wei-Yao Wang, Yunchen Pu, Yizhe Zhang, Hao liu, Chunyuan Li, Lawrence Carin

The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs.

Attribute Generative Adversarial Network +3

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