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Found 46 papers, 12 papers with code
Exploiting Point-Wise Attention in 6D Object Pose Estimation Based on Bidirectional Prediction
Traditional geometric registration based estimation methods only exploit the CAD model implicitly, which leads to their dependence on observation quality and deficiency to occlusion.
Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides
Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects.
Differentially Private Graph Neural Network with Importance-Grained Noise Adaption
We propose NAP-GNN, a node-importance-grained privacy-preserving GNN algorithm with privacy guarantees based on adaptive differential privacy to safeguard node information.
When UAVs Meet ISAC: Real-Time Trajectory Design for Secure Communications
The real-time unmanned aerial vehicle (UAV) trajectory design of secure integrated sensing and communication (ISAC) is optimized.
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios
In this paper, we propose a novel privacy inference-empowered stealthy backdoor attack (PI-SBA) scheme for FL under non-IID scenarios.
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized.
FakeSwarm: Improving Fake News Detection with Swarming Characteristics
We evaluate our system on a public dataset and demonstrate the effectiveness of incorporating swarm features in fake news identification, achieving an f1-score and accuracy of over 97% by combining all three types of swarm features.
MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression
Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment.
FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction
To address this issue, we propose FINEEHR, a system that utilizes two representation learning techniques, namely metric learning and fine-tuning, to refine clinical note embeddings, while leveraging the intrinsic correlations among different health statuses and note categories.
BotTriNet: A Unified and Efficient Embedding for Social Bots Detection via Metric Learning
Second, we demonstrate that metric learning techniques can be applied in this context to refine raw embeddings and improve classification performance.
BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns
An essential topic in online social network security is how to accurately detect bot accounts and relieve their harmful impacts (e. g., misinformation, rumor, and spam) on genuine users.
Non-IID Transfer Learning on Graphs
To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph.
Aircraft Ground Taxiing Deduction and Conflict Early Warning Method Based on Control Command Information
Aircraft taxiing conflict is a threat to the safety of airport operations, mainly due to the human error in control command infor-mation.
RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map
In addition, we derive sufficient conditions of feature extractors for the representation preserving the roto-translation invariance, making RING++ a framework applicable to generic multi-channel features.
A Unified Meta-Learning Framework for Dynamic Transfer Learning
Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task.
Towards Two-view 6D Object Pose Estimation: A Comparative Study on Fusion Strategy
We ascertain the Mid- Fusion approach is the best approach to restore the most precise 3D keypoints useful for object pose estimation.
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages.
Adaptive Transfer Learning for Plant Phenotyping
Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on studying the diverse traits of plants related to the plants' growth.
Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation
Current monocular-based 6D object pose estimation methods generally achieve less competitive results than RGBD-based methods, mostly due to the lack of 3D information.
RF-LighGBM: A probabilistic ensemble way to predict customer repurchase behaviour in community e-commerce
Given the complex problem of feature engineering, the classic model RFM in the field of customer relationship management is improved, and an improved model is proposed to describe the characteristics of customer buying behaviour, which includes five indicators.
Local $K$-means: An Efficient Optimization Algorithm And Its Generalization
We also developed a general model that unified LKM, KSUMS, and SC, and discussed the connection among them.
Automatically Lock Your Neural Networks When You're Away
The smartphone and laptop can be unlocked by face or fingerprint recognition, while neural networks which confront numerous requests every day have little capability to distinguish between untrustworthy and credible users.
A Universal Model for Cross Modality Mapping by Relational Reasoning
With the aim of matching a pair of instances from two different modalities, cross modality mapping has attracted growing attention in the computer vision community.
Robust Federated Learning for Neural Networks
In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation.
Continuous Transfer Learning
One major challenge associated with continuous transfer learning is the time evolving relatedness of the source domain and the current target domain as the target domain evolves over time.
Leveraging AI and Intelligent Reflecting Surface for Energy-Efficient Communication in 6G IoT
The ever-increasing data traffic, various delay-sensitive services, and the massive deployment of energy-limited Internet of Things (IoT) devices have brought huge challenges to the current communication networks, motivating academia and industry to move to the sixth-generation (6G) network.
GAN-based Recommendation with Positive-Unlabeled Sampling
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.
Search-based Kinodynamic Motion Planning for Omnidirectional Quadruped Robots
Autonomous navigation has played an increasingly significant role in quadruped robot system.
Autonomous Navigation
Motion Planning
Robotics
Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks
Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles.
REDE: End-to-end Object 6D Pose Robust Estimation Using Differentiable Outliers Elimination
Object 6D pose estimation is a fundamental task in many applications.
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
In this work, we show that this paradigm might inherit the adversarial vulnerability of the centralized neural network, i. e., it has deteriorated performance on adversarial examples when the model is deployed.
Structure Learning for Cyclic Linear Causal Models
We consider the problem of structure learning for linear causal models based on observational data.
Continuous Transfer Learning with Label-informed Distribution Alignment
To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain.
A framework for adaptive width control of dense contour-parallel toolpaths in fused deposition modeling
In this paper we present a framework which supports multiple schemes to generate toolpaths with adaptive width, by employing a function to decide the number of beads and their widths.
Graphics Robotics Systems and Control Systems and Control J.6
Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks
We alleviate this issue by training a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal.
Progressive DARTS: Bridging the Optimization Gap for NAS in the Wild
With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks.
Robust Data Preprocessing for Machine-Learning-Based Disk Failure Prediction in Cloud Production Environments
To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e. g., Backblaze SMART logs).
Hierarchical Attention Networks for Medical Image Segmentation
Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Graph data widely exist in many high-impact applications.
Ranked #1 on
Node Classification
on BlogCatalog
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search.
Autoencoder Based Residual Deep Networks for Robust Regression Prediction and Spatiotemporal Estimation
To have a superior generalization, a deep learning neural network often involves a large size of training sample.
ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data
Then, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, in which context sampling uses VDRW and the label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs in different classes.
Social and Information Networks
Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval
This paper proposed a new explicit nonlinear dimensionality reduction using neural networks for image retrieval tasks.
Perceptual uniform descriptor and Ranking on manifold: A bridge between image representation and ranking for image retrieval
Incompatibility of image descriptor and ranking is always neglected in image retrieval.
Three Tiers Neighborhood Graph and Multi-graph Fusion Ranking for Multi-feature Image Retrieval: A Manifold Aspect
Multi-feature fusion ranking can be utilized to improve the ranking list of query.
Infill Optimization for Additive Manufacturing -- Approaching Bone-like Porous Structures
Our method builds upon and extends voxel-wise topology optimization.
Graphics
