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Found 25 papers, 5 papers with code
SeMi: When Imbalanced Semi-Supervised Learning Meets Mining Hard Examples
This method distinguishes the entropy differences among logits of hard and easy examples, thereby identifying hard examples and increasing the utility of unlabeled data, better addressing the imbalance problem in CISSL.
A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism
Neural architecture search (NAS) enables re-searchers to automatically explore vast search spaces and find efficient neural networks.
Solving the Food-Energy-Water Nexus Problem via Intelligent Optimization Algorithms
The application of evolutionary algorithms (EAs) to multi-objective optimization problems has been widespread.
Complex-Valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming
The complex-valued network operations proposed in this study improve the beamforming accuracy of complex-valued ultrasound signals over traditional real-valued methods.
Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction
In our experiments, MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.
Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble
We introduce partial trace operations to enforce non-unitary and reduce the number of trainable parameters by using a parameter-sharing strategy and incorporating temporal information as an input in the backward process.
Transaction Fraud Detection via an Adaptive Graph Neural Network
A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes.
Solving Dynamic Traveling Salesman Problems With Deep Reinforcement Learning
This brings in a dynamic version of the traveling salesman problem (DTSP), which takes into account the information of real-time traffic and customer requests.
Transfer Learning for Fault Diagnosis of Transmission Lines
This method is able to diagnose faults for different transmission line lengths and impedances by transferring the knowledge from a source convolutional neural network to predict a dissimilar target dataset.
Frequent Itemset-driven Search for Finding Minimum Node Separators in Complex Networks
Finding an optimal set of critical nodes in a complex network has been a long-standing problem in the fields of both artificial intelligence and operations research.
IoT-based Route Recommendation for an Intelligent Waste Management System
The need for such sensors is increasing; however, proliferation of technologies comes with various challenges.
Learning to Detect Critical Nodes in Sparse Graphs via Feature Importance Awareness
The critical node problem (CNP) aims to find a set of critical nodes from a network whose deletion maximally degrades the pairwise connectivity of the residual network.
Generalized Nesterov's Acceleration-incorporated Non-negative and Adaptive Latent Factor Analysis
A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful knowledge from non-negative data represented by high-dimensional and sparse (HiDS) matrices arising from various service applications.
MOGAN: Morphologic-structure-aware Generative Learning from a Single Image
For training for ROI, we propose to utilize the data coming from the original image being augmented and bring in a novel module to transform such augmented data into knowledge containing both structures and appearances, thus enhancing the model's comprehension of the sample.
On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems (SAS)
Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies.
Modeling and Analysis of Three Properties of Mobile Interactive Systems Based on Variable Petri Nets
The case study of a practical mobile payment system shows the effectiveness of the proposed method.
Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges
We classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.
AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes
Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches.
G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space
G-images refer to image data defined on irregular graph domains.
Residual-driven Fuzzy C-Means Clustering for Image Segmentation
Due to its inferior characteristics, an observed (noisy) image's direct use gives rise to poor segmentation results.
Kullback-Leibler Divergence-Based Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation
Considering these feature sets as data of clustering, an modified FCM algorithm is proposed, which introduces a KL divergence term in the partition matrix into its objective function.
Residual-Sparse Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet frames
To further enhance the segmentation effects of the improved FCM algorithm, we also employ the morphological reconstruction to smoothen the labels generated by clustering.
Learning Smooth Representation for Unsupervised Domain Adaptation
When constructing a deep end-to-end model, to ensure the effectiveness and stability of unsupervised domain adaptation, three critical factors are considered in our proposed optimization strategy, i. e., the sample amount of a target domain, dimension and batchsize of samples.
Ranked #1 on
Domain Adaptation
on SVNH-to-MNIST
Unsupervised Domain Adaptation with Adversarial Residual Transform Networks
Domain adaptation is widely used in learning problems lacking labels.
