https://github.com/LONG-9621/Extract_Point_3D
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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Semantic Parsing is the task of transducing natural language utterances into formal meaning representations. The target meaning representations can be defined according to a wide variety of formalisms. This include linguistically-motivated semantic representations that are designed to capture the meaning of any sentence such as λ-calculus or the abstract meaning representations. Alternatively, for more task-driven approaches to Semantic Parsing, it is common for meaning representations to represent executable programs such as SQL queries, robotic commands, smart phone instructions, and even general-purpose programming languages like Python and Java.
Source: Tranx: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffold
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Robust Text-to-SQL Generation with Execution-Guided Decoding
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
TAPEX: Table Pre-training via Learning a Neural SQL Executor
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
OccluMix: Towards De-Occlusion Virtual Try-on by Semantically-Guided Mixup
A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
A Syntactic Neural Model for General-Purpose Code Generation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.