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Found 13 papers, 2 papers with code
A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques
A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades.
Face Swap via Diffusion Model
This technical report presents a diffusion model based framework for face swapping between two portrait images.
SimAC: A Simple Anti-Customization Method against Text-to-Image Synthesis of Diffusion Models
Despite the success of diffusion-based customization methods on visual content creation, increasing concerns have been raised about such techniques from both privacy and political perspectives.
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data
To address this issue, we develop a novel personalized federated learning framework for heterogeneous data, which we refer to as FedSplit.
Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy
Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit.
Cooperation and interdependence in global science funding
Investments in research and development are key to scientific and economic growth and to the well-being of society.
Improved Naive Bayes with Mislabeled Data
Labeling mistakes are frequently encountered in real-world applications.
Diversity-Aware Meta Visual Prompting
We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone.
Hierarchical Terrain Attention and Multi-Scale Rainfall Guidance For Flood Image Prediction
With the deterioration of climate, the phenomenon of rain-induced flooding has become frequent.
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss
Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions.
Knowledge-Enhanced Relation Extraction Dataset
Recently, knowledge-enhanced methods leveraging auxiliary knowledge graphs have emerged in relation extraction, surpassing traditional text-based approaches.
Jointly Dynamic Topic Model for Recognition of Lead-lag Relationship in Two Text Corpora
To discover the lead-lag relationship, we propose a jointly dynamic topic model and also develop an embedding extension to address the modeling problem of large-scale text corpus.
Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.
