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A Game-theoretic Approach Towards Collaborative Coded Computation Offloading
Coded distributed computing (CDC) has emerged as a promising approach be...
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Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
Ever since Machine Learning as a Service (MLaaS) emerges as a viable bus...
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TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation
We investigate how to solve the cross-corpus news recommendation for uns...
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Privacy and Robustness in Federated Learning: Attacks and Defenses
As data are increasingly being stored in different silos and societies b...
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Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness
Deep neural networks (DNNs) are known to be prone to adversarial attacks...
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FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning
As an innovative solution for privacy-preserving machine learning (ML), ...
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PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation
Transfer learning is an effective technique to improve a target recommen...
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Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning
Transfer learning has become a common practice for training deep learnin...
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Protect, Show, Attend and Tell: Image Captioning Model with Ownership Protection
By and large, existing Intellectual Property Right (IPR) protection on d...
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Privacy Threats Against Federated Matrix Factorization
Matrix Factorization has been very successful in practical recommendatio...
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Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
This paper investigates capabilities of Privacy-Preserving Deep Learning...
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SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Since the first alert launched by the World Health Organization (5 Janua...
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Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning
In 5G and Beyond networks, Artificial Intelligence applications are expe...
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Graph Random Neural Network
Graph neural networks (GNNs) have generalized deep learning methods into...
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Network On Network for Tabular Data Classification in Real-world Applications
Tabular data is the most common data format adopted by our customers ran...
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Fisher Deep Domain Adaptation
Deep domain adaptation models learn a neural network in an unlabeled tar...
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Threats to Federated Learning: A Survey
With the emergence of data silos and popular privacy awareness, the trad...
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A Survey towards Federated Semi-supervised Learning
The success of Artificial Intelligence (AI) should be largely attributed...
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Mechanism Design for Multi-Party Machine Learning
In a multi-party machine learning system, different parties cooperate on...
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RPN: A Residual Pooling Network for Efficient Federated Learning
Federated learning is a new machine learning framework which enables dif...
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FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Visual object detection is a computer vision-based artificial intelligen...
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A Communication Efficient Vertical Federated Learning Framework
One critical challenge for applying today's Artificial Intelligence (AI)...
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Advances and Open Problems in Federated Learning
Federated learning (FL) is a machine learning setting where many clients...
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A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
Data privacy and security becomes a major concern in building machine le...
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Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
Joint extraction of aspects and sentiments can be effectively formulated...
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L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition
Modern Automatic Speech Recognition (ASR) systems primarily rely on scor...
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Federated Transfer Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) is widely used in autonomous driving tasks a...
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Federated Learning in Mobile Edge Networks: A Comprehensive Survey
In recent years, mobile devices are equipped with increasingly advanced ...
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Transfer Learning with Dynamic Distribution Adaptation
Transfer learning aims to learn robust classifiers for the target domain...
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HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
Electroencephalography (EEG) classification techniques have been widely ...
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Secure Federated Matrix Factorization
To protect user privacy and meet law regulations, federated (machine) le...
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Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
An effective content recommendation in modern social media platforms sho...
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Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
Graph convolution network based approaches have been recently used to mo...
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Easy Transfer Learning By Exploiting Intra-domain Structures
Transfer learning aims at transferring knowledge from a well-labeled dom...
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Learning to Transfer Examples for Partial Domain Adaptation
Domain adaptation is critical for learning in new and unseen environment...
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AutoML @ NeurIPS 2018 challenge: Design and Results
We organized a competition on Autonomous Lifelong Machine Learning with ...
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Federated Machine Learning: Concept and Applications
Today's AI still faces two major challenges. One is that in most industr...
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SecureBoost: A Lossless Federated Learning Framework
The protection of user privacy is an important concern in machine learni...
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Federated Reinforcement Learning
In reinforcement learning, building policies of high-quality is challeng...
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Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Collaborative filtering (CF) is the key technique for recommender system...
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Secure Federated Transfer Learning
Machine learning relies on the availability of a vast amount of data for...
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Building Ethics into Artificial Intelligence
As artificial intelligence (AI) systems become increasingly ubiquitous, ...
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Privacy-preserving Transfer Learning for Knowledge Sharing
In many practical machine-learning applications, it is critical to allow...
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Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification
Aspect-level sentiment classification (ASC) aims at identifying sentimen...
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Smart City Development with Urban Transfer Learning
The rapid development of big data techniques has offered great opportuni...
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Bike Flow Prediction with Multi-Graph Convolutional Networks
One fundamental issue in managing bike sharing systems is the bike flow ...
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Learning to Multitask
Multitask learning has shown promising performance in many applications ...
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Parameter Transfer Unit for Deep Neural Networks
Parameters in deep neural networks which are trained on large-scale data...
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Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment
Dialogue policy transfer enables us to build dialogue policies in a targ...
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Millionaire: A Hint-guided Approach for Crowdsourcing
Modern machine learning is migrating to the era of complex models, which...
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