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Found 9 papers, 5 papers with code
Semantic Parsing with Dual Learning
Semantic parsing converts natural language queries into structured logical forms.
Unsupervised Learning of Local Discriminative Representation for Medical Images
However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks.
Unsupervised Local Discrimination for Medical Images
To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images.
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
Furthermore, we present CSS, a large-scale CrosS-Schema Chinese text-to-SQL dataset, to carry on corresponding studies.
2D LiDAR and Camera Fusion Using Motion Cues for Indoor Layout Estimation
A ground robot explores an indoor space with a single floor and vertical walls, and collects a sequence of intensity images and 2D LiDAR datasets.
Indoor Layout Estimation by 2D LiDAR and Camera Fusion
This paper presents an algorithm for indoor layout estimation and reconstruction through the fusion of a sequence of captured images and LiDAR data sets.
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms.
Ranked #8 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
On the Structural Generalization in Text-to-SQL
Exploring the generalization of a text-to-SQL parser is essential for a system to automatically adapt the real-world databases.
ASTormer: An AST Structure-aware Transformer Decoder for Text-to-SQL
Text-to-SQL aims to generate an executable SQL program given the user utterance and the corresponding database schema.
