Search Results for author:
Found 37 papers, 15 papers with code
A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability
In this setting, considering that self-supervised model could be trained by completely different self-supervised paradigms, e. g., masked image modeling and contrastive learning, with complex training details, we propose a unified membership inference method called PartCrop.
A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units
Spatio-Temporal (ST) prediction is crucial for making informed decisions in urban location-based applications like ride-sharing.
Graph Contrastive Learning with Cohesive Subgraph Awareness
However, such stochastic augmentations may severely damage the intrinsic properties of a graph and deteriorate the following representation learning process.
Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment
To mitigate this issue, AI software compression plays a crucial role, which aims to compress model size while keeping high performance.
A Survey for Federated Learning Evaluations: Goals and Measures
Evaluation is a systematic approach to assessing how well a system achieves its intended purpose.
Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer
For example, consider cross-domain fraud detection, where there are two types of transactions: credit and non-credit.
UCTB: An Urban Computing Tool Box for Spatiotemporal Crowd Flow Prediction
To address these issues, we design and implement a spatiotemporal crowd flow prediction toolbox called UCTB (Urban Computing Tool Box), which integrates multiple spatiotemporal domain knowledge and state-of-the-art models simultaneously.
A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management
In this paper, we propose RegionGen, a data-driven region generation framework that can specify regions with key characteristics (e. g., good spatial semantic meaning and predictability) by modeling region generation as a multi-objective optimization problem.
A Survey on Vertical Federated Learning: From a Layered Perspective
From the hardware layer to the vertical federated system layer, researchers contribute to various aspects of VFL.
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks
In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component.
Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals
In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease.
Understanding Self-Supervised Pretraining with Part-Aware Representation Learning
The study is mainly motivated by that random views, used in contrastive learning, and random masked (visible) patches, used in masked image modeling, are often about object parts.
A Unified Knowledge Graph Augmentation Service for Boosting Domain-specific NLP Tasks
We propose KnowledgeDA, a unified domain language model development service to enhance the task-specific training procedure with domain knowledge graphs.
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment
By simulating the attack mechanism as the safety test, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm.
Secure Forward Aggregation for Vertical Federated Neural Networks
In this paper, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN.
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks
Basically, we propose to perturb the original network by adding or removing links, and expect the embedding generated on the perturbed network can leak little information about private links but hold high utility for various downstream tasks.
The Principle of Least Sensing: A Privacy-Friendly Sensing Paradigm for Urban Big Data Analytics
With the worldwide emergence of data protection regulations, how to conduct law-regulated big data analytics becomes a challenging and fundamental problem.
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method
In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method.
E-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation
In this paper, we notice that the class weights of categories that tend to share many adjacent boundary pixels lack discrimination, thereby limiting the performance.
MSP : Refine Boundary Segmentation via Multiscale Superpixel
Inspired by the generated sharp edges of superpixel blocks, we employ superpixel to guide the information passing within feature map.
DistFL: Distribution-aware Federated Learning for Mobile Scenarios
Previous studies focus on the "symptoms" directly, as they try to improve the accuracy or detect possible attacks by adding extra steps to conventional FL models.
Practical and Secure Federated Recommendation with Personalized Masks
In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness.
Exploring Context Generalizability in Citywide Crowd Mobility Prediction: An Analytic Framework and Benchmark
Contextual features are important data sources for building citywide crowd mobility prediction models.
Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemble
To address this issue, we propose a novel semi-supervised transfer learning framework based on optimal transport theory and self-paced ensemble for Sepsis early detection, called SPSSOT, which can efficiently transfer knowledge from the source hospital (with rich labeled data) to the target hospital (with scarce labeled data).
FedEval: A Holistic Evaluation Framework for Federated Learning
In this paper, we propose a holistic evaluation framework for FL called FedEval, and present a benchmarking study on seven state-of-the-art FL algorithms.
Federated Crowdsensing: Framework and Challenges
In particular, we first propose a federated crowdsensing framework, which analyzes the privacy concerns of each crowdsensing stage (i. e., task creation, task assignment, task execution, and data aggregation) and discuss how federated learning techniques may take effect.
Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework
The Spatio-Temporal Traffic Prediction (STTP) problem is a classical problem with plenty of prior research efforts that benefit from traditional statistical learning and recent deep learning approaches.
CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning
Credit investigation is critical for financial services.
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction
Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.
Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting
This task is challenging due to the complicated spatiotemporal dependencies among regions.
Secure Federated Matrix Factorization
The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data.
Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions
However, the shallow models leveraging bilinear forms suffer from limitations on capturing complicated nonlinear interactions between drug pairs.
Smart City Development with Urban Transfer Learning
To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm.
Bike Flow Prediction with Multi-Graph Convolutional Networks
We propose a new multi-graph convolutional neural network model to predict the bike flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective.
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing
Since the sensed data from different cells (sub-areas) of the target sensing area will probably lead to diverse levels of inference data quality, cell selection (i. e., choose which cells of the target area to collect sensed data from participants) is a critical issue that will impact the total amount of data that requires to be collected (i. e., data collection costs) for ensuring a certain level of quality.
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city.
Ridesourcing Car Detection by Transfer Learning
Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool.
