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Found 51 papers, 17 papers with code
NICO++: Towards Better Benchmarking for Domain Generalization
Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains.
Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild
We first applied data augmentation techniques on the physiological and behavioral data to improve the robustness of supervised stress detection models.
More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
For example, although combining bio-signals from multiple sensors (i. e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context.
Federated Graph Neural Networks: Overview, Techniques and Challenges
We propose a unique 3-tiered taxonomy of the FedGNNs literature to provide a clear view into how GNNs work in the context of Federated Learning (FL).
Towards Verifiable Federated Learning
Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models.
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
In this paper, we propose a data heterogeneity-robust FL approach, FedGSP, to address this challenge by leveraging on a novel concept of dynamic Sequential-to-Parallel (STP) collaborative training.
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection
In this paper, we propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges and enhance the generalization ability in unsupervised anomaly detection.
Intelligent Online Selling Point Extraction for E-Commerce Recommendation
In addition, this kind of product description should be eye-catching to the readers.
Automatic Product Copywriting for E-Commerce
It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening.
A Survey of Fairness-Aware Federated Learning
Recent advances in Federated Learning (FL) have brought large-scale machine learning opportunities for massive distributed clients with performance and data privacy guarantees.
MarS-FL: Enabling Competitors to Collaborate in Federated Learning
A key unaddressed scenario is that these FL participants are in a competitive market, where market shares represent their competitiveness.
GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning
In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required, through extensive experiments under diverse realistic data distribution settings.
Towards Out-Of-Distribution Generalization: A Survey
Classic machine learning methods are built on the $i. i. d.$ assumption that training and testing data are independent and identically distributed.
Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering
Noisy labels are commonly found in real-world data, which cause performance degradation of deep neural networks.
Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild
Compared to the baseline method using the samples with complete modalities, the performance of the MFN improved by 1. 6% in f1-scores.
Forecasting Health and Wellbeing for Shift Workers Using Job-role Based Deep Neural Network
According to the differences in self-reported health and wellbeing labels between nurses and doctors, and the correlations among their labels, we proposed a job-role based multitask and multilabel deep learning model, where we modeled physiological and behavioral data for nurses and doctors simultaneously to predict participants' next day's multidimensional self-reported health and wellbeing status.
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection
The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality.
Noise-resistant Deep Metric Learning with Ranking-based Instance Selection
The existence of noisy labels in real-world data negatively impacts the performance of deep learning models.
Towards Personalized Federated Learning
In parallel with the rapid adoption of Artificial Intelligence (AI) empowered by advances in AI research, there have been growing awareness and concerns of data privacy.
HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
the weights of training data, HYDRA assesses the contribution of training data toward test data points throughout the training trajectory.
Privacy and Robustness in Federated Learning: Attacks and Defenses
Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.
Federated Learning for Personalized Humor Recognition
In this paper, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL).
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.
A VCG-based Fair Incentive Mechanism for Federated Learning
In order to build an ecosystem for FL to operate in a sustainable manner, it has to be economically attractive to data owners.
Computer Science and Game Theory
A Robust Spearman Correlation Coefficient Permutation Test
In general, there is common misconception that the tests about $\rho_s=0$ are robust to deviations from bivariate normality.
Methodology Applications
SCNet: A Neural Network for Automated Side-Channel Attack
Information about system characteristics such as power consumption, electromagnetic leaks and sound can be exploited by the side-channel attack to compromise the system.
Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.
Topology-aware Differential Privacy for Decentralized Image Classification
In this paper, we design Top-DP, a novel solution to optimize the differential privacy protection of decentralized image classification systems.
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
The framework consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning, and a discriminator that ensures the generalization of the evaluator.
Threats to Federated Learning: A Survey
It is thus of paramount importance to make FL system designers to be aware of the implications of future FL algorithm design on privacy-preservation.
FedCoin: A Peer-to-Peer Payment System for Federated Learning
In FedCoin, blockchain consensus entities calculate SVs and a new block is created based on the proof of Shapley (PoSap) protocol.
Byzantine-resilient Decentralized Stochastic Gradient Descent
It guarantees that each benign node in a decentralized system can train a correct model under very strong Byzantine attacks with an arbitrary number of faulty nodes.
Multi-Participant Multi-Class Vertical Federated Learning
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants.
FOCUS: Dealing with Label Quality Disparity in Federated Learning
It maintains a small set of benchmark samples on the FL server and quantifies the credibility of the client local data without directly observing them by computing the mutual cross-entropy between performance of the FL model on the local datasets and that of the client local FL model on the benchmark dataset.
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Federated learning (FL) is a promising approach to resolve this challenge.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Although both classical Gaussian mechanisms [1, 2] assume $0 < \epsilon \leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $\epsilon$ and $\delta$ where the added noise amounts of [1, 2] do not achieve $(\epsilon,\delta)$-DP.
Transfer Learning with Dynamic Distribution Adaptation
Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions.
Ranked #5 on
Domain Adaptation
on ImageCLEF-DA
Generating Persuasive Visual Storylines for Promotional Videos
Video contents have become a critical tool for promoting products in E-commerce.
Privacy-preserving Crowd-guided AI Decision-making in Ethical Dilemmas
To investigate such ethical dilemmas, recent studies have adopted preference aggregation, in which each voter expresses her/his preferences over decisions for the possible ethical dilemma scenarios, and a centralized system aggregates these preferences to obtain the winning decision.
Towards Fair and Privacy-Preserving Federated Deep Models
This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates.
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e. g., mobile devices, to distributedly train and globally share models without revealing their local data.
Easy Transfer Learning By Exploiting Intra-domain Structures
In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance.
Ranked #4 on
Transfer Learning
on Office-Home
Ethically Aligned Opportunistic Scheduling for Productive Laziness
In order to enable workforce management systems to follow the IEEE Ethically Aligned Design guidelines to prioritize worker wellbeing, we propose a distributed Computational Productive Laziness (CPL) approach in this paper.
Building Ethics into Artificial Intelligence
As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination.
Autoencoder Based Sample Selection for Self-Taught Learning
In this paper, we propose a metric for the relevance between a source sample and the target samples.
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning.
Ranked #1 on
Domain Adaptation
on Office-Caltech-10
Cross-position Activity Recognition with Stratified Transfer Learning
STL consists of two components: Stratified Domain Selection (STL-SDS) can select the most similar source domain to the target domain; Stratified Activity Transfer (STL-SAT) is able to perform accurate knowledge transfer.
Surveillance Video Parsing with Single Frame Supervision
In this paper, we develop a Single frame Video Parsing (SVP) method which requires only one labeled frame per video in training stage.
A Survey on Artificial Intelligence and Data Mining for MOOCs
In this survey paper, we first review the state-of-the-art artificial intelligence and data mining research applied to MOOCs, emphasising the use of AI and DM tools and techniques to improve student engagement, learning outcomes, and our understanding of the MOOC ecosystem.
