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Found 27 papers, 8 papers with code
Federated Deep Multi-View Clustering with Global Self-Supervision
Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data.
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
These issues compromise both the accuracy and the computational efficiency of models in both Machine Learning and Unlearning.
Zero-shot information extraction from radiological reports using ChatGPT
Information extraction is the strategy to transform the sequence of characters into structured data, which can be employed for secondary analysis.
Unsupervised Multiplex Graph Learning with Complementary and Consistent Information
Unsupervised multiplex graph learning (UMGL) has been shown to achieve significant effectiveness for different downstream tasks by exploring both complementary information and consistent information among multiple graphs.
A Universal Unbiased Method for Classification from Aggregate Observations
This motivates the study on classification from aggregate observations (CFAO), where the supervision is provided to groups of instances, instead of individual instances.
Using Caterpillar to Nibble Small-Scale Images
Recently, MLP-based models have become popular and attained significant performance on medium-scale datasets (e. g., ImageNet-1k).
An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization
Therefore, we also explore the robustness of diffusion models to MIA in the text-to-speech (TTS) task, which is an audio generation task.
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy
Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models.
Explicit and Implicit Semantic Ranking Framework
The core challenge in numerous real-world applications is to match an inquiry to the best document from a mutable and finite set of candidates.
Investigating and Mitigating the Side Effects of Noisy Views in Multi-view Clustering in Practical Scenarios
Multi-view clustering (MvC) aims at exploring category structures among multi-view data without label supervision.
Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition
In order to better learn these neutral expression-disentangled features (NDFs) and to alleviate the non-convex optimization problem, a self-paced learning (SPL) strategy based on NDFs is proposed in the training stage.
GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis
To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE.
FedNI: Federated Graph Learning with Network Inpainting for Population-Based Disease Prediction
Then we train a global GCN node classifier across institutions using a federated graph learning platform.
Self-paced Principal Component Analysis
Notably, the complexity of each sample is calculated at the beginning of each iteration in order to integrate samples from simple to more complex into training.
Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering
The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views.
Multi-level Feature Learning for Contrastive Multi-view Clustering
Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces.
Self-paced Resistance Learning against Overfitting on Noisy Labels
They might significantly deteriorate the performance of convolutional neural networks (CNNs), because CNNs are easily overfitted on corrupted labels.
Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.
Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan
To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives.
Neural Network Retraining for Model Serving
We address two challenges of life-long retraining: catastrophic forgetting and efficient retraining.
Listwise Learning to Rank by Exploring Unique Ratings
We encode all of the documents already selected by an RNN model.
Frosting Weights for Better Continual Training
Training a neural network model can be a lifelong learning process and is a computationally intensive one.
High dynamic range image forensics using cnn
High dynamic range (HDR) imaging has recently drawn much attention in multimedia community.
Identification of multi-scale hierarchical brain functional networks using deep matrix factorization
We present a deep semi-nonnegative matrix factorization method for identifying subject-specific functional networks (FNs) at multiple spatial scales with a hierarchical organization from resting state fMRI data.
Semantic Document Distance Measures and Unsupervised Document Revision Detection
In this paper, we model the document revision detection problem as a minimum cost branching problem that relies on computing document distances.
Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
In this paper, we present hierarchical relationbased latent Dirichlet allocation (hrLDA), a data-driven hierarchical topic model for extracting terminological ontologies from a large number of heterogeneous documents.
Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis
We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.
