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Found 8 papers, 2 papers with code
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Our experiments show the effectiveness of our segmentation approach on thousands of real-world point clouds.
Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation
In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains.
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Multi-target Domain Adaptation
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Blended-target Domain Adaptation
Multi-target Domain Adaptation
Crowd-Machine Collaboration for Item Screening
In this paper we describe how crowd and machine classifier can be efficiently combined to screen items that satisfy a set of predicates.
Combining Crowd and Machines for Multi-predicate Item Screening
This paper discusses how crowd and machine classifiers can be efficiently combined to screen items that satisfy a set of predicates.
Siamese Graph Neural Networks for Data Integration
In this work, we propose a general approach to modeling and integrating entities from structured data, such as relational databases, as well as unstructured sources, such as free text from news articles.
Active Learning from Crowd in Document Screening
In this paper, we explore how to efficiently combine crowdsourcing and machine intelligence for the problem of document screening, where we need to screen documents with a set of machine-learning filters.
Active Hybrid Classification
Hybrid crowd-machine classifiers can achieve superior performance by combining the cost-effectiveness of automatic classification with the accuracy of human judgment.
Business Entity Matching with Siamese Graph Convolutional Networks
Data integration has been studied extensively for decades and approached from different angles.
