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Found 106 papers, 27 papers with code
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.
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.
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.
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
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.
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.
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.
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
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.
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.
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.
Generating Persuasive Visual Storylines for Promotional Videos
Video contents have become a critical tool for promoting products in E-commerce.
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 #7 on
Domain Adaptation
on ImageCLEF-DA
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.
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.
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Federated learning (FL) is a promising approach to resolve this challenge.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
Towards Out-Of-Distribution Generalization: A Survey
This paper represents the first comprehensive, systematic review of OOD generalization, encompassing a spectrum of aspects from problem definition, methodological development, and evaluation procedures, to the implications and future directions of the field.
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.
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.
Towards Fairness-Aware Federated Learning
However, most current works focus on the interest of the central controller in FL, and overlook the interests of the FL clients.
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.
Intelligent Online Selling Point Extraction for E-Commerce Recommendation
In addition, this kind of product description should be eye-catching to the readers.
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.
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.
Towards Verifiable Federated Learning
Federated learning (FL) is an emerging paradigm of collaborative machine learning that preserves user privacy while building powerful models.
Federated Graph Neural Networks: Overview, Techniques and Challenges
This has led to the rapid development of the emerging research field of federated graph neural networks (FedGNNs).
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.
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.
STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network
—The revolutionary advances in machine learning and data mining techniques have contributed greatly to the rapid developments of maritime Internet of Things (IoT).
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.
Bias Reducing Multitask Learning on Mental Health Prediction
However, there is still a lack of standard in evaluating bias in such machine learning models in the field, which leads to challenges in providing reliable predictions and in addressing disparities.
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
To the best of our knowledge, FedOBD is the first approach to perform dropout on FL models at the block level rather than at the individual parameter level.
Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning
As such, building trustworthy AIRTB auctioning systems has emerged as an important direction of research in this field in recent years.
Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation
Prompt tuning, or the conditioning of a frozen pretrained language model (PLM) with soft prompts learned from data, has demonstrated impressive performance on a wide range of NLP tasks.
Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning
As an effective technique to increase the data variability and thus train deep models with better generalization, data augmentation (DA) is a critical step for the success of deep learning models on biobehavioral time series data.
LEAVES: Learning Views for Time-Series Data in Contrastive Learning
But the view-learning method is not well developed for time-series data.
Measure-Theoretic Probability of Complex Co-occurrence and E-Integral
Probability and conditional probability of co-occurrence are introduced by being defined in a general setting with set functions to develop a rigorous measure-theoretic foundation for the inherent challenge of data sparseness.
Multi-Resource Allocation for On-Device Distributed Federated Learning Systems
This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system.
On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey
Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs).
FIXED: Frustratingly Easy Domain Generalization with Mixup
Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations.
PiRL: Participant-Invariant Representation Learning for Healthcare
Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications.
Stable Learning via Sparse Variable Independence
The problem of covariate-shift generalization has attracted intensive research attention.
Towards AI-Empowered Crowdsourcing
Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e. g., Uber, Airbnb).
Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff
The Top-K cutoff technique optimises the inference of SNN, and the regularisation are proposed to affect the training and construct SNN with optimised performance for cutoff.
Clustered Embedding Learning for Recommender Systems
However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up.
PiRL: Participant-Invariant Representation Learning for Healthcare Using Maximum Mean Discrepancy and Triplet Loss
Due to individual heterogeneity, person-specific models are usually achieving better performance than generic (one-size-fits-all) models in data-driven health applications.
FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning
Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about largely overlapping sets of data sample(s), to jointly train a useful global model.
Efficient Training of Large-scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications.
Towards Interpretable Federated Learning
Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data.
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization
Yet the current definition of flatness discussed in SAM and its follow-ups are limited to the zeroth-order flatness (i. e., the worst-case loss within a perturbation radius).
FedGH: Heterogeneous Federated Learning with Generalized Global Header
It is a communication and computation-efficient model-heterogeneous FL framework which trains a shared generalized global prediction header with representations extracted by heterogeneous extractors for clients' models at the FL server.
DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation
To eliminate the influences of adverse weather conditions, we propose a dual attention and dual frequency-guided dehazing network (termed DADFNet) for real-time visibility enhancement.
Utility-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning
However, this assumption is not realistic in practical AFL marketplaces in which multiple data consumers can compete to attract data owners to join their respective FL tasks.
Multi-Tier Client Selection for Mobile Federated Learning Networks
However, the problem of optimizing FL client selection in mobile federated learning networks (MFLNs), where devices move in and out of each others' coverage and no FL server knows all the data owners, remains open.
Rethinking the Evaluation Protocol of Domain Generalization
This paper examines the risks of test data information leakage from two aspects of the current evaluation protocol: supervised pretraining on ImageNet and oracle model selection.
Meta Adaptive Task Sampling for Few-Domain Generalization
Moreover, we propose a Meta Adaptive Task Sampling (MATS) procedure to differentiate base tasks according to their semantic and domain-shift similarity to the novel task.
Towards Quantum Federated Learning
This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.
Cost-Effective Task Offloading Scheduling for Hybrid Mobile Edge-Quantum Computing
We develop cost-effective designs for both task offloading mode selection and resource allocation, subject to the individual link latency constraint guarantees for mobile devices, while satisfying the required success ratio for their computation tasks.
Hierarchical Federated Learning Incentivization for Gas Usage Estimation
Federated learning (FL) offers a solution to this problem by enabling local data processing on each participant, such as gas companies and heating stations.
Flatness-Aware Minimization for Domain Generalization
However, we reveal that Adam is not necessarily the optimal choice for the majority of current DG methods and datasets.
Fairness-Aware Client Selection for Federated Learning
By not using threshold-based reputation filtering, it provides FL clients with opportunities to redeem their reputations after a perceived poor performance, thereby further enhancing fair client treatment.
The Prospect of Enhancing Large-Scale Heterogeneous Federated Learning with Transformers
Federated learning (FL) addresses data privacy concerns by enabling collaborative training of AI models across distributed data owners.
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices.
LR-XFL: Logical Reasoning-based Explainable Federated Learning
Under LR-XFL, FL clients create local logic rules based on their local data and send them, along with model updates, to the FL server.
FedLogic: Interpretable Federated Multi-Domain Chain-of-Thought Prompt Selection for Large Language Models
To improve interpretability and explore the balance principle between generality and personalization under a multi-domain CoT prompt selection scenario, we propose the Federated Logic rule learning approach (FedLogic).
Efficient RLHF: Reducing the Memory Usage of PPO
To address this issue, we present a comprehensive analysis the memory usage, performance, and training time of memory-savings techniques for PPO.
Image Aesthetics Assessment via Learnable Queries
Image aesthetics assessment (IAA) aims to estimate the aesthetics of images.
ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal
In this work, we propose a novel ECG-Segment based Learning (ECG-SL) framework to explicitly model the periodic nature of ECG signals.
Hire When You Need to: Gradual Participant Recruitment for Auction-based Federated Learning
The success of Federated Learning (FL) depends on the quantity and quality of the data owners (DOs) as well as their motivation to join FL model training.
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
Federated learning (FL) is an emerging machine learning paradigm in which a central server coordinates multiple participants (clients) collaboratively to train on decentralized data.
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing
To allow each data owner (a. k. a., FL clients) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged.
Multi-Session Budget Optimization for Forward Auction-based Federated Learning
Based on hierarchical reinforcement learning, MultiBOS-AFL jointly optimizes inter-session budget pacing and intra-session bidding for AFL MUs, with the objective of maximizing the total utility.
Transformer-empowered Multi-modal Item Embedding for Enhanced Image Search in E-Commerce
Since its deployment in March 2023, it has achieved a remarkable 9. 90% increase in terms of clicks per user and a 4. 23% boost in terms of orders per user for the image search feature on the Shopee e-commerce platform.
Multi-dimensional Fair Federated Learning
Federated learning (FL) has emerged as a promising collaborative and secure paradigm for training a model from decentralized data without compromising privacy.
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
Existing MHPFL approaches often rely on a public dataset with the same nature as the learning task, or incur high computation and communication costs.
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Federated learning (FL) provides a privacy-preserving approach for collaborative training of machine learning models.
Fairness-Aware Job Scheduling for Multi-Job Federated Learning
Federated learning (FL) enables multiple data owners (a. k. a.
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
It assigns a shared homogeneous small feature extractor and a local gating network for each client's local heterogeneous large model.
A Survey on Evaluation of Out-of-Distribution Generalization
In closing, we propose several promising directions for future research in OOD evaluation.
Machine Learning Assisted Adjustment Boosts Inferential Efficiency of Randomized Controlled Trials
In this work, we proposed a novel inferential procedure assisted by machine learning based adjustment for randomized control trials.
Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks
In the rapidly advancing field of federated learning (FL), ensuring efficient FL task delegation while incentivising FL client participation poses significant challenges, especially in wireless networks where FL participants' coverage is limited.
A Note on LoRA
LoRA (Low-Rank Adaptation) has emerged as a preferred method for efficiently adapting Large Language Models (LLMs) with remarkable simplicity and efficacy.
Intelligent Agents for Auction-based Federated Learning: A Survey
To enhance the efficiency in AFL decision support for stakeholders (i. e., data consumers, data owners, and the auctioneer), intelligent agent-based techniques have emerged.
Advances and Open Challenges in Federated Learning with Foundation Models
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational efficiency.
