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08/04/2023
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
Time series remains one of the most challenging modalities in machine le...
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06/30/2023
FedBone: Towards Large-Scale Federated Multi-Task Learning
Heterogeneous federated multi-task learning (HFMTL) is a federated learn...
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11/07/2022
FIXED: Frustratingly Easy Domain Generalization with Mixup
Domain generalization (DG) aims to learn a generalizable model from mult...
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09/15/2022
Generalized Representations Learning for Time Series Classification
Time series classification is an important problem in real world. Due to...
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09/01/2022
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
The distribution shifts between training and test data typically undermi...
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07/25/2022
Domain-invariant Feature Exploration for Domain Generalization
Deep learning has achieved great success in the past few years. However,...
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07/21/2022
Domain Generalization for Activity Recognition via Adaptive Feature Fusion
Human activity recognition requires the efforts to build a generalizable...
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06/17/2022
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
Federated learning has attracted increasing attention to building models...
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06/14/2022
Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition
It is expensive and time-consuming to collect sufficient labeled data to...
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01/03/2022
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection
Unsupervised anomaly detection aims to build models to effectively detec...
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12/01/2021
Federated Learning with Adaptive Batchnorm for Personalized Healthcare
There is a growing interest in applying machine learning techniques for ...
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07/27/2021
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
Nowadays, multi-sensor technologies are applied in many fields, e.g., He...
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06/02/2021
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare
The success of machine learning applications often needs a large quantit...
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10/17/2020
Secure Weighted Aggregation in Federated Learning
Federated learning (FL) schemes enable multiple clients to jointly solve...
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07/17/2020
Learning to Match Distributions for Domain Adaptation
When the training and test data are from different distributions, domain...
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01/29/2020
FOCUS: Dealing with Label Quality Disparity in Federated Learning
Ubiquitous systems with End-Edge-Cloud architecture are increasingly bei...
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09/18/2019
Transfer Learning with Dynamic Adversarial Adaptation Network
The recent advances in deep transfer learning reveal that adversarial le...
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09/17/2019
Transfer Learning with Dynamic Distribution Adaptation
Transfer learning aims to learn robust classifiers for the target domain...
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07/22/2019
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare
With the rapid development of computing technology, wearable devices suc...
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07/05/2019
A Mobile Cloud Collaboration Fall Detection System Based on Ensemble Learning
Falls are one of the important causes of accidental or unintentional inj...
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04/02/2019
Easy Transfer Learning By Exploiting Intra-domain Structures
Transfer learning aims at transferring knowledge from a well-labeled dom...
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07/20/2018
Deep Transfer Learning for Cross-domain Activity Recognition
Human activity recognition plays an important role in people's daily lif...
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07/19/2018
Visual Domain Adaptation with Manifold Embedded Distribution Alignment
Visual domain adaptation aims to learn robust classifiers for the target...
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07/02/2018
Balanced Distribution Adaptation for Transfer Learning
Transfer learning has achieved promising results by leveraging knowledge...
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06/26/2018
Cross-position Activity Recognition with Stratified Transfer Learning
Human activity recognition aims to recognize the activities of daily liv...
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12/25/2017
Stratified Transfer Learning for Cross-domain Activity Recognition
In activity recognition, it is often expensive and time-consuming to acq...
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07/12/2017
