Search Results for author:
Found 35 papers, 23 papers with code
NYCU_TWD@LT-EDI-ACL2022: Ensemble Models with VADER and Contrastive Learning for Detecting Signs of Depression from Social Media
This paper presents a state-of-the-art solution to the LT-EDI-ACL 2022 Task 4: Detecting Signs of Depression from Social Media Text.
MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models
Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes.
COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Data
Utilizing the characteristics of the recipe, we maximize the use of samples with missing values, derive embeddings from intersections with an initial graph that incorporates expert knowledge and chronological order, and create a sensor ordering graph.
BADGE: BADminton report Generation and Evaluation with LLM
Following report generation, the LLM evaluates and scores the reports to assess their quality.
Offline Imitation of Badminton Player Behavior via Experiential Contexts and Brownian Motion
(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.
Team Trifecta at Factify5WQA: Setting the Standard in Fact Verification with Fine-Tuning
In this paper, we present Pre-CoFactv3, a comprehensive framework comprised of Question Answering and Text Classification components for fact verification.
Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data
In our experiments, we observe that clinicalBERT and PubMed-BERT, when combined with attention fusion, can achieve an accuracy of 73% in multiclass chronic diseases and diabetes prediction.
Root Cause Analysis In Microservice Using Neural Granger Causal Discovery
To address these challenges, we propose RUN, a novel approach for root cause analysis using neural Granger causal discovery with contrastive learning.
A Survey on Self-Supervised Learning for Non-Sequential Tabular Data
Recently, SSL has become 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.
Style-News: Incorporating Stylized News Generation and Adversarial Verification for Neural Fake News Detection
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.
ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton
Agent forecasting systems have been explored to investigate agent patterns and improve decision-making in various domains, e. g., pedestrian predictions and marketing bidding.
SeGA: Preference-Aware Self-Contrastive Learning with Prompts for Anomalous User Detection on Twitter
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.
TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
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.
RSVP: Customer Intent Detection via Agent Response Contrastive and Generative Pre-Training
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.
DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal
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.
LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
Recently, researchers have leveraged the representation learning transferability of pre-trained Large Language Models (LLMs) to handle limited non-linguistic datasets effectively.
Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset
In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection.
ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis
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.
NYCU-TWO at Memotion 3: Good Foundation, Good Teacher, then you have Good Meme Analysis
In this work, we use CLIP to extract aligned image-text features and propose a novel meme sentiment analysis framework, consisting of a Cooperative Teaching Model (CTM) for Task A and a Cascaded Emotion Classifier (CEC) for Tasks B&C.
Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact Verification
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.
Detecting and Ranking Causal Anomalies in End-to-End Complex System
By collecting a large amount of machine sensor data, we can have many ways to find anomalies.
Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal
However, existing methods only consider the real estate itself, ignoring the relation between the properties.
Where Will Players Move Next? Dynamic Graphs and Hierarchical Fusion for Movement Forecasting in Badminton
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.
A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)
Recent techniques for analyzing sports precisely has stimulated various approaches to improve player performance and fan engagement.
Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact Verification
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.
A Lightweight and Accurate Spatial-Temporal Transformer for Traffic Forecasting
The information fusion module captures the complex spatial-temporal dependency between regions.
ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton
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.
Exploring the Long Short-Term Dependencies to Infer Shot Influence in Badminton Matches
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.
Probabilistic Value Selection for Space Efficient Model
Unlike the existing methods such as feature selection that removes features and instance selection that eliminates instances, value selection eliminates the values (with respect to each feature) in the dataset with two purposes: reducing the model size and preserving its accuracy.
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation
To generate inspired headlines, we propose a novel framework called POpularity-Reinforced Learning for inspired Headline Generation (PORL-HG).
Sequence-Aware Factorization Machines for Temporal Predictive Analytics
As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics.
CoachAI: A Project for Microscopic Badminton Match Data Collection and Tactical Analysis
For automatically and systematically competition data collection and tactical analysis, a project called CoachAI has been supported by the Ministry of Science and Technology, Taiwan.
TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications
The proposed heatmap-based deep learning network is trained to not only recognize the ball image from a single frame but also learn flying patterns from consecutive frames.
On the Feature Discovery for App Usage Prediction in Smartphones
By analyzing real App usage log data, we discover two kinds of features: The Explicit Feature (EF) from sensing readings of built-in sensors, and the Implicit Feature (IF) from App usage relations.
