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Found 36 papers, 6 papers with code
Live and Learn: Continual Action Clustering with Incremental Views
Finally, a three-step alternate optimization is proposed, in which the category memory library and consensus partition matrix are optimized.
Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification
The assessment of breast density is crucial in the context of breast cancer screening, especially in populations with a higher percentage of dense breast tissues.
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation
Specifically, our strategy considers both forward and backward adaptation, to transfer the forgery knowledge from the source domain to the target domain in forward adaptation and then reverse the adaptation from the target domain to the source domain in backward adaptation.
Energizing Federated Learning via Filter-Aware Attention
Federated learning (FL) is a promising distributed paradigm, eliminating the need for data sharing but facing challenges from data heterogeneity.
Privacy-Preserving Encrypted Low-Dose CT Denoising
In this way, the proposed methods can achieve two merits, the data privacy is well protected and the server model is free from the risk of model leakage.
CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations
Therefore, it is necessary to develop a method for predicting molecular properties that effectively combines spatial structural information while maintaining the simplicity and efficiency of graph neural networks.
A Dynamic Model for Frequency Response Optimization in Photovoltaic Visible Light Communication
The RC constant can be reduced for PVLC receivers working near open-circuit voltage conditions by adding a moderate local light to decrease the internal resistance value.
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations
Motivated by the important role of ID, in this paper, we propose a novel deep representation learning approach with autoencoder, which incorporates regularization of the global and local ID constraints into the reconstruction of data representations.
A Deep Learning Method for Real-time Bias Correction of Wind Field Forecasts in the Western North Pacific
In this study, we developed the Multi-Task-Double Encoder Trajectory Gated Recurrent Unit (MT-DETrajGRU) model, which uses an improved double-encoder forecaster architecture to model the spatiotemporal sequence of the U and V components of the wind field; we designed a multi-task learning loss function to correct wind speed and wind direction simultaneously using only one model.
Impact of Virtual Inertia on DC Grid Stability with Constant Power Loads
We derive a closed-form stability criterion that can be used to evaluate the impact of virtual inertia on the system stability, and demonstrate that, given a set of system parameters, the stability of a DC grid powering CPLs can be improved for a range of virtual inertia designs.
GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction
In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model.
Ranked #1 on
Link Property Prediction
on ogbl-ddi
Application of Data Encryption in Chinese Named Entity Recognition
Recently, with the continuous development of deep learning, the performance of named entity recognition tasks has been dramatically improved.
Chinese Named Entity Recognition
named-entity-recognition
+1
Filter-enhanced MLP is All You Need for Sequential Recommendation
Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation.
Synthesizing Tensor Transformations for Visual Self-attention
Self-attention shows outstanding competence in capturing long-range relationships while enhancing performance on vision tasks, such as image classification and image captioning.
A Novel Grid-forming Voltage Control Strategy for Supplying Unbalanced Microgrid Loads Using Inverter-based Resources
Compared with the conventional rotating reference frame ($dq$) based control scheme, the proposed scheme shows better dynamic performance.
STNN-DDI: A Substructure-aware Tensor Neural Network to Predict Drug-Drug Interactions
Results: In this paper, by supposing that the interactions between two given drugs are caused by their local chemical structures (sub-structures) and their DDI types are determined by the linkages between different substructure sets, we design a novel Substructure-ware Tensor Neural Network model for DDI prediction (STNN-DDI).
SSI–DDI: Substructure–Substructure Interactions for Drug–Drug Interaction Prediction
A major concern with co-administration of different drugs is the high risk of interference between their mechanisms of action, known as adverse drug–drug interactions (DDIs), which can cause serious injuries to the organism.
A Unified Power-Setpoint Tracking Algorithm for Utility-Scale PV Systems with Power Reserves and Fast Frequency Response Capabilities
By using MPPE to decouple the impact of irradiance changes on the measured PV output power, we develop a fast convergence technique for tracking power-reference changes within three FPPT iterations.
Dual Discriminator Adversarial Distillation for Data-free Model Compression
Then the generated samples are used to train the compact student network under the supervision of the teacher.
Similarity Transfer for Knowledge Distillation
The proposed approach promotes the performance of student model as the virtual sample created by multiple images produces a similar probability distribution in the teacher and student networks.
3D Facial Geometry Recovery from a Depth View with Attention Guided Generative Adversarial Network
We present to recover the complete 3D facial geometry from a single depth view by proposing an Attention Guided Generative Adversarial Networks (AGGAN).
Real-time 3D Facial Tracking via Cascaded Compositional Learning
We propose to learn a cascade of globally-optimized modular boosted ferns (GoMBF) to solve multi-modal facial motion regression for real-time 3D facial tracking from a monocular RGB camera.
PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments
The experimental results show that PIoU loss can dramatically improve the performance of OBB detectors, particularly on objects with high aspect ratios and complex backgrounds.
Accelerating convolutional neural network by exploiting sparsity on GPUs
Multiplications and additions for zero values in the feature map are useless for convolution results.
Distributed, Parallel, and Cluster Computing
A K-means-based Multi-subpopulation Particle Swarm Optimization for Neural Network Ensemble
This paper presents a k-means-based multi-subpopulation particle swarm optimization, denoted as KMPSO, for training the neural network ensemble.
Deep Clustering With Intra-class Distance Constraint for Hyperspectral Images
The high dimensionality of hyperspectral images often results in the degradation of clustering performance.
Parallel Medical Imaging for Intelligent Medical Image Analysis: Concepts, Methods, and Applications
Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge.
Dense 3D Facial Reconstruction from a Single Depth Image in Unconstrained Environment
With the increasing demands of applications in virtual reality such as 3D films, virtual Human-Machine Interactions and virtual agents, the analysis of 3D human face analysis is considered to be more and more important as a fundamental step for those virtual reality tasks.
Improve the Evaluation of Fluency Using Entropy for Machine Translation Evaluation Metrics
The widely-used automatic evaluation metrics cannot adequately reflect the fluency of the translations.
An Automatic Machine Translation Evaluation Metric Based on Dependency Parsing Model
To avoid these problems, we propose a novel automatic evaluation metric based on dependency parsing model, with no need to define sub-structures by human.
Ship Detection and Segmentation using Image Correlation
There have been intensive research interests in ship detection and segmentation due to high demands on a wide range of civil applications in the last two decades.
