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Found 31 papers, 7 papers with code
Automated Similarity Metric Generation for Recommendation
Given the critical role of similarity metrics in recommender systems, existing methods mainly employ handcrafted similarity metrics to capture the complex characteristics of user-item interactions.
Robust Federated Contrastive Recommender System against Model Poisoning Attack
In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec.
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
To bridge this gap, this paper explores a user-governed data contribution federated recommendation architecture where users are free to take control of whether they share data and the proportion of data they share to the server.
On-Device Recommender Systems: A Comprehensive Survey
Recently, driven by the advances in storage, communication, and computation capabilities of edge devices, there has been a shift of focus from CloudRSs to on-device recommender systems (DeviceRSs), which leverage the capabilities of edge devices to minimize centralized data storage requirements, reduce the response latency caused by communication overheads, and enhance user privacy and security by localizing data processing and model training.
ROIC-DM: Robust Text Inference and Classification via Diffusion Model
While language models have made many milestones in text inference and classification tasks, they remain susceptible to adversarial attacks that can lead to unforeseen outcomes.
Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion
Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences.
Hide Your Model: A Parameter Transmission-free Federated Recommender System
Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters.
Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings.
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems.
Hybrid Feature Embedding For Automatic Building Outline Extraction
Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment.
Manipulating Visually-aware Federated Recommender Systems and Its Countermeasures
Therefore, when incorporating visual information in FedRecs, all existing model poisoning attacks' effectiveness becomes questionable.
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about specific training instances as if they have never existed in the training set.
Privacy-preserving Adversarial Facial Features
In this paper, we propose an adversarial features-based face privacy protection (AdvFace) approach to generate privacy-preserving adversarial features, which can disrupt the mapping from adversarial features to facial images to defend against reconstruction attacks.
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences.
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
To reveal the real vulnerability of FedRecs, in this paper, we present a new poisoning attack method to manipulate target items' ranks and exposure rates effectively in the top-$K$ recommendation without relying on any prior knowledge.
Interaction-level Membership Inference Attack Against Federated Recommender Systems
An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack.
Federated Unlearning for On-Device Recommendation
It removes a user's contribution by rolling back and calibrating the historical parameter updates and then uses these updates to speed up federated recommender reconstruction.
Hardware Acceleration of Sampling Algorithms in Sample and Aggregate Graph Neural Networks
Sampling is an important process in many GNN structures in order to train larger datasets with a smaller computational complexity.
Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin
In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level.
Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attacks
In this paper, we first design a novel attribute inference attacker to perform a comprehensive privacy analysis of the state-of-the-art federated recommender models.
CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces
The current methods are dedicated to studying the agents' future trajectories under the social interaction and the sceneries' physical constraints.
Unified Question Generation with Continual Lifelong Learning
To solve the problems, we propose a model named Unified-QG based on lifelong learning techniques, which can continually learn QG tasks across different datasets and formats.
View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums
Different frequency bands in the trajectory spectrums could hierarchically reflect agents' motion preferences at different scales.
Ranked #3 on
Trajectory Prediction
on ETH/UCY
MSN: Multi-Style Network for Trajectory Prediction
Then, we assume that the target agents may plan their future behaviors according to each of these categorized styles, thus utilizing different style channels to make predictions with significant style differences in parallel.
CAT: Cross Attention in Vision Transformer
In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps capture global information.
Deformations of Q-curvature II
This is the second article of a sequence of research on deformations of Q-curvature.
Differential Geometry
EAdam Optimizer: How $ε$ Impact Adam
Recently, many variants of Adam, such as Adabound, RAdam and Adabelief, have been proposed and show better performance than Adam.
Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis
Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge.
Semantic Segmentation for Urban Planning Maps based on U-Net
The results indicate the proposed method can serve as a viable tool for urban planning map semantic segmentation task with high accuracy and efficiency.
Multi-view Common Component Discriminant Analysis for Cross-view Classification
Cross-view classification that means to classify samples from heterogeneous views is a significant yet challenging problem in computer vision.
Sparse multi-view matrix factorisation: a multivariate approach to multiple tissue comparisons
The proposed methodology can be interpreted as an extension of principal component analysis in that it provides the means to decompose the total sample variance in each tissue into the sum of two components: one capturing the variance that is shared across tissues, and one isolating the tissue-specific variances.
