Search Results for author:
Found 31 papers, 14 papers with code
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising
To address data scarcity and privacy concerns, we combine diffusion models with federated learning -- a decentralized training approach where models are trained individually at different sites, and their parameters are aggregated on a central server over multiple iterations.
An Interpretable ML-based Model for Predicting p-y Curves of Monopile Foundations in Sand
Predicting the lateral pile response is challenging due to the complexity of pile-soil interactions.
Bi-directional Mapping of Morphology Metrics and 3D City Blocks for Enhanced Characterization and Generation of Urban Form
This paper highlights the importance of establishing a bi-directional mapping between morphology metrics and complex urban form to enable the integration of urban form generation with performance evaluation.
Lighten CARAFE: Dynamic Lightweight Upsampling with Guided Reassemble Kernels
Experiments on several mainstream vision tasks show that our DLU achieves comparable and even better performance to the original CARAFE, but with much lower complexity, e. g., DLU requires 91% fewer parameters and at least 63% fewer FLOPs (Floating Point Operations) than CARAFE in the case of 16x upsampling, but outperforms the CARAFE by 0. 3% mAP in object detection.
Dose-aware Diffusion Model for 3D Low-dose PET: Multi-institutional Validation with Reader Study and Real Low-dose Data
However, existing models have often resulted in compromised image quality when achieving low-dose PET and have limited generalizability to different image noise-levels, acquisition protocols, and patient populations.
LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising
Deep learning-based positron emission tomography (PET) image denoising offers the potential to reduce radiation exposure and scanning time by transforming low-count images into high-count equivalents.
Discrete Conditional Diffusion for Reranking in Recommendation
To overcome these challenges, we propose a novel Discrete Conditional Diffusion Reranking (DCDR) framework for recommendation.
Measuring Item Global Residual Value for Fair Recommendation
In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items.
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Through theoretical analyses, we find that the conservatism of existing methods fails in pursuing users' long-term satisfaction.
Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation
An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems.
Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition
By incorporating additional contextual information, deep biasing methods have emerged as a promising solution for speech recognition of personalized words.
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored.
Practice of the conformer enhanced AUDIO-VISUAL HUBERT on Mandarin and English
Considering the bimodal nature of human speech perception, lips, and teeth movement has a pivotal role in automatic speech recognition.
Disentangled Causal Embedding With Contrastive Learning For Recommender System
Most previous studies neglect user's conformity and entangle interest with it, which may cause the recommender systems fail to provide satisfying results.
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective
Embedding-based retrieval (EBR) methods are widely used in modern recommender systems thanks to its simplicity and effectiveness.
Real-time Short Video Recommendation on Mobile Devices
However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate.
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
To facilitate model learning, we further collect rich features of users and items as well as users' behavior history.
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction.
A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images
To address this limitation, a dual neighborhood hypergraph neural network is proposed in this article, which combines the multiscale superpixel segmentation and hypergraph convolution to model and exploit the complex relationships.
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive.
LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
The proposed approach is currently serving over hundreds of millions of users on the platform and achieves one of the most tremendous improvements over these months.
An Improved Discriminative Optimization for 3D Rigid Point Cloud Registration
The Discriminative Optimization (DO) algorithm has been proved much successful in 3D point cloud registration.
A Multiscale Graph Convolutional Network for Change Detection in Homogeneous and Heterogeneous Remote Sensing Images
In this paper, a novel CD method based on the graph convolutional network (GCN) and multiscale object-based technique is proposed for both homogeneous and heterogeneous images.
A Learning-based Optimization Algorithm:Image Registration Optimizer Network
As the search space of registration is usually non-convex, the optimization algorithm, which aims to search the best transformation parameters, is a challenging step.
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs
To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years.
LOSSLESS SINGLE IMAGE SUPER RESOLUTION FROM LOW-QUALITY JPG IMAGES
Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task.
s-LWSR: Super Lightweight Super-Resolution Network
Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance.
Position Focused Attention Network for Image-Text Matching
Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence.
Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification
To be specific, the proposed CDCL method is an iterative process of three main stages, i. e. twice of RW-based pseudolabeling and cross domain learning via C-CCA.
Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification
Then we consider the subspace invariance between two domains as projection matrices and original tensors are projected as core tensors with lower dimensions into the invariant tensor subspace by applying Tucker decomposition.
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.
Quantitative Methods
