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Found 29 papers, 6 papers with code
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.
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.
Robust Federated Learning for Neural Networks
In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation.
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 USA Air-Traffic
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.
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.
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.
Infill Optimization for Additive Manufacturing -- Approaching Bone-like Porous Structures
Our method builds upon and extends voxel-wise topology optimization.
Graphics
