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Found 18 papers, 5 papers with code
Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation
Experiments on English-German and English-Romanian translation show that: (i) Epi-Curriculum improves both model's robustness and adaptability in seen and unseen domains; (ii) Our episodic training framework enhances the encoder and decoder's robustness to domain shift.
Towards Implementing Energy-aware Data-driven Intelligence for Smart Health Applications on Mobile Platforms
Recent breakthrough technological progressions of powerful mobile computing resources such as low-cost mobile GPUs along with cutting-edge, open-source software architectures have enabled high-performance deep learning on mobile platforms.
MC-GEN:Multi-level Clustering for Private Synthetic Data Generation
MC-GEN applies multi-level clustering and differential private generative model to improve the utility of synthetic data.
SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classification
The results show that our two-stage training strategy effectively addresses the class imbalance classification problem, and significantly improves existing works in terms of F1-score and AUC score, resulting in state-of-the-art performance.
Locally Differentially Private Distributed Deep Learning via Knowledge Distillation
As such, it motivates the researchers to conduct distributed deep learning, where the data user would like to build DL models using the data segregated across multiple different data owners.
COVID-19 Pneumonia Severity Prediction using Hybrid Convolution-Attention Neural Architectures
This study proposed a novel framework for COVID-19 severity prediction, which is a combination of data-centric and model-centric approaches.
A compressive multi-kernel method for privacy-preserving machine learning
Compressive Privacy is a privacy framework that employs utility-preserving lossy-encoding scheme to protect the privacy of the data, while multi-kernel method is a kernel based machine learning regime that explores the idea of using multiple kernels for building better predictors.
Contrastive Self-supervised Neural Architecture Search
This paper proposes a novel cell-based neural architecture search algorithm (NAS), which completely alleviates the expensive costs of data labeling inherited from supervised learning.
Generating Black-Box Adversarial Examples in Sparse Domain
In this paper, we propose a novel approach to generate a black-box attack in sparse domain whereas the most important information of an image can be observed.
Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation
The generalization capability of machine learning models, which refers to generalizing the knowledge for an "unseen" domain via learning from one or multiple seen domain(s), is of great importance to develop and deploy machine learning applications in the real-world conditions.
Ranked #26 on
Domain Generalization
on VLCS
Utility-aware Privacy-preserving Data Releasing
Later, our approach leverages the learned knowledge to precisely perturb the data owners' data into privatized data that can be successfully utilized for certain intended purpose (learning to succeed), without jeopardizing certain predefined privacy (training to fail).
CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification
Since the skin lesion datasets are usually limited and statistically biased, while designing an effective fusion approach, it is important to consider not only the performance of each classifier on the training/validation dataset, but also the relative discriminative power (e. g., confidence) of each classifier regarding an individual sample in the testing phase, which calls for an active fusion approach.
SAIA: Split Artificial Intelligence Architecture for Mobile Healthcare System
Hence, most of the mobile healthcare systems leverage the cloud computing infrastructure, where the data collected by the mobile and IoT devices would be transmitted to the cloud computing platforms for analysis.
Privacy-Preserving Image Classification in the Local Setting
To protect the image privacy, we propose to locally perturb the image representation before revealing to the data user.
Privacy-Preserving Boosting in the Local Setting
The privacy concern raises when such data leaves the hand of the owners and be further explored or mined.
Locally Differentially Private Naive Bayes Classification
In order to train a Naive Bayes classifier in an untrusted setting, we propose to use methods satisfying local differential privacy.
Privacy Preserving Machine Learning: Threats and Solutions
For privacy concerns to be addressed adequately in current machine learning systems, the knowledge gap between the machine learning and privacy communities must be bridged.
DynaMo: Dynamic Community Detection by Incrementally Maximizing Modularity
Community detection is of great importance for online social network analysis.
Social and Information Networks Cryptography and Security
