https://github.com/xiuyu0000/vision/blob/main/mindvision/ms3d/models/pointnet2cls.py
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
Abstract Syntax Networks for Code Generation and Semantic Parsing
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
A Syntactic Neural Model for General-Purpose Code Generation
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models
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
Towards Robustness of Text-to-SQL Models against Synonym Substitution
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Automated Concatenation of Embeddings for Structured Prediction
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
A Syntactic Neural Model for General-Purpose Code Generation
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.