Search Results for author:
Found 27 papers, 9 papers with code
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 Service for Developing Domain Language Models in AI Software
By enhancing the task-specific training procedure with domain knowledge graphs, we propose KnowledgeDA, a unified and low-code domain language model development service.
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: An Information-Theoretic Approach
Federated learning (FL) is a promising machine learning paradigm that enables cross-party data collaboration for real-world AI applications in a privacy-preserving and law-regulated way.
ES-CRF: Embedded Superpixel CRF for Semantic Segmentation
It utilizes CRF to guide the message passing between pixels in high-level features to purify the feature representation of boundary pixels, with the help of inner pixels belong to the same object.
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.
Benchmarking Contextual Factor Generalizability in Spatiotemporal Crowd Flow Prediction
In this paper, we develop an experimental platform composed of large-scale spatiotemporal crowd flow data, contextual data, and state-of-the-art spatiotemporal prediction models to conduct a comprehensive experimental study to quantitatively investigate the generalizability of different contextual features and modeling techniques in three urban crowd flow prediction scenarios (bike flow, metro passenger flow, and electric vehicle charging demand).
CityNet: A Multi-city Multi-modal Dataset for Smart City Applications
Data-driven approaches have been applied to many problems in urban computing.
Semi-supervised Optimal Transport with Self-paced Ensemble for Cross-hospital Sepsis Early Detection
In SPSSOT, we first extract the same clinical indicators from the source domain (e. g., hospital with rich labeled data) and the target domain (e. g., hospital with little labeled data), then we combine the semi-supervised domain adaptation based on optimal transport theory with self-paced under-sampling to avoid a negative transfer possibly caused by covariate shift and class imbalance.
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.
