Search Results for author:
Found 19 papers, 11 papers with code
Open-Vocabulary Federated Learning with Multimodal Prototyping
A new user could come up with queries that involve data from unseen classes, and such open-vocabulary queries would directly defect such FL systems.
Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation
The proliferation of online misinformation has posed significant threats to public interest.
Federated Recommendation via Hybrid Retrieval Augmented Generation
To this end, we propose GPT-FedRec, a federated recommendation framework leveraging ChatGPT and a novel hybrid Retrieval Augmented Generation (RAG) mechanism.
Exploring Boundary of GPT-4V on Marine Analysis: A Preliminary Case Study
In this study, we carry out the preliminary and comprehensive case study of utilizing GPT-4V for marine analysis.
Linear Recurrent Units for Sequential Recommendation
State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models.
Feature Decoupling-Recycling Network for Fast Interactive Segmentation
First, our model decouples the learning of source image semantics from the encoding of user guidance to process two types of input domains separately.
Zero- and Few-Shot Event Detection via Prompt-Based Meta Learning
With emerging online topics as a source for numerous new events, detecting unseen / rare event types presents an elusive challenge for existing event detection methods, where only limited data access is provided for training.
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning
As such, MetaAdapt can learn how to adapt the misinformation detection model and exploit the source data for improved performance in the target domain.
Region-Aware Portrait Retouching with Sparse Interactive Guidance
Specifically, we propose a region-aware retouching framework with two branches: an automatic branch and an interactive branch.
QA Domain Adaptation using Hidden Space Augmentation and Self-Supervised Contrastive Adaptation
Unlike existing approaches, we generate pseudo labels and propose to train the model via a novel attention-based contrastive adaptation method.
Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup
Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process.
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks
In many applications with real-world consequences, it is crucial to develop reliable uncertainty estimation for the predictions made by the AI decision systems.
Domain Adaptation for Question Answering via Question Classification
In this work, we investigate the potential benefits of question classification for QA domain adaptation.
Contrastive Domain Adaptation for Early Misinformation Detection: A Case Study on COVID-19
However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e. g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation.
Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders
Additionally, we design an adversarial training method tailored for sequential recommender systems.
Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation
Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time.
Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction
Under this setting, we propose an API-based model extraction method via limited-budget synthetic data generation and knowledge distillation.
Certified Defense via Latent Space Randomized Smoothing with Orthogonal Encoders
Randomized Smoothing (RS), being one of few provable defenses, has been showing great effectiveness and scalability in terms of defending against $\ell_2$-norm adversarial perturbations.
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks
Adversarial Training is proved to be an efficient method to defend against adversarial examples, being one of the few defenses that withstand strong attacks.
