Search Results for author:
Found 23 papers, 4 papers with code
uestcc@SMM4H’22: RoBERTa based Adverse Drug Events Classification on Tweets
This is a description of our participation in the ADE Mining in English Tweets shared task, organized by the Social Media Mining for Health SMM4H 2022 workshop.
Yet@SMM4H’22: Improved BERT-based classification models with Rdrop and PolyLoss
This paper describes our approach for 11 classification tasks (Task1a, Task2a, Task2b, Task3a, Task3b, Task4, Task5, Task6, Task7, Task8 and Task9) from Social Media Mining for Health (SMM4H) 2022 Shared Tasks.
Zhegu@SMM4H-2022: The Pre-training Tweet & Claim Matching Makes Your Prediction Better
SMM4H-2022 (CITATION) Task 2 is to detect whether containing premise in the tweets of users about COVID-19 on the social medias or their stances for the claims.
zydhjh4593@SMM4H’22: A Generic Pre-trained BERT-based Framework for Social Media Health Text Classification
This paper describes our proposed framework for the 10 text classification tasks of Task 1a, 2a, 2b, 3a, 4, 5, 6, 7, 8, and 9, in the Social Media Mining for Health (SMM4H) 2022.
yiriyou@SMM4H’22: Stance and Premise Classification in Domain Specific Tweets with Dual-View Attention Neural Networks
The paper introduces the methodology proposed for the shared Task 2 of the Social Media Mining for Health Application (SMM4H) in 2022.
Semantic Communication based on Large Language Model for Underwater Image Transmission
Underwater communication is essential for environmental monitoring, marine biology research, and underwater exploration.
MVOC: a training-free multiple video object composition method with diffusion models
Although image composition based on diffusion models has been highly successful, it is not straightforward to extend the achievement to video object composition tasks, which not only exhibit corresponding interaction effects but also ensure that the objects in the composited video maintain motion and identity consistency, which is necessary to composite a physical harmony video.
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data
FedCRL introduces contrastive representation learning (CRL) on shared representations to facilitate knowledge acquisition of clients.
Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets.
Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information
Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization.
PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
To improve fine-tuning performance, we conduct prompt engineering on raw data, including filtering useful information, selecting behaviors of players with high win rates, and further processing them into textual instruction using multiple prompt engineering techniques.
HyperLips: Hyper Control Lips with High Resolution Decoder for Talking Face Generation
First, FaceEncoder is used to obtain latent code by extracting features from the visual face information taken from the video source containing the face frame. Then, HyperConv, which weighting parameters are updated by HyperNet with the audio features as input, will modify the latent code to synchronize the lip movement with the audio.
FedTADBench: Federated Time-Series Anomaly Detection Benchmark
We would like to answer the following questions: (1)How is the performance of time series anomaly detection algorithms when meeting federated learning?
Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets
Considering the growing concern of data privacy, federated learning (FL) is increasingly adopted to train STLF models for utility companies (UCs) in recent research.
DearFSAC: An Approach to Optimizing Unreliable Federated Learning via Deep Reinforcement Learning
In federated learning (FL), model aggregation has been widely adopted for data privacy.
Protum: A New Method For Prompt Tuning Based on "[MASK]"
In response to the above issue, we propose a new \textbf{Pro}mpt \textbf{Tu}ning based on "[\textbf{M}ASK]" (\textbf{Protum}) method in this paper, which constructs a classification task through the information carried by the hidden layer of "[MASK]" tokens and then predicts the labels directly rather than the answer tokens.
Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information
Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)-based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal operation, i. e., optimal power flow (OPF), of power systems with a high uptake of RERs.
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection
Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data.
Supervised Anomaly Detection
Weakly-supervised Anomaly Detection
Ferryman at SemEval-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines
Natural language processing (NLP) has been applied to various fields including text classification and sentiment analysis.
Ferryman at SemEval-2020 Task 12: BERT-Based Model with Advanced Improvement Methods for Multilingual Offensive Language Identification
Furthermore, our team also ranked in the top 20 on the Sub-task A of other languages.
Ferryman as SemEval-2020 Task 5: Optimized BERT for Detecting Counterfactuals
The main purpose of this article is to state the effect of using different methods and models for counterfactual determination and detection of causal knowledge.
Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approach
Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake.
