https://github.com/theophilec/PAC-Bayes-ILE-Structured-Prediction
공식 코드A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings
Papers with Code 아카이브
Structured Prediction
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Structured Prediction is an area of machine learning focusing on representations of spaces with combinatorial structure, and algorithms for inference and parameter estimation over these structures. Core methods include both tractable exact approaches like dynamic programming and spanning tree algorithms as well as heuristic techniques such as linear programming relaxations and greedy search.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/thomas0809/rxnscribe
공식 코드RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing
https://github.com/thu-spmi/JSA
공식 코드Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models
https://github.com/twke18/Adversarial_Structure_Matching
공식 코드Adversarial Structure Matching for Structured Prediction Tasks
https://github.com/uclanlp/reducingbias
공식 코드Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
https://github.com/utahnlp/learning-constraints
공식 코드Learning Constraints for Structured Prediction Using Rectifier Networks
https://github.com/utahnlp/prompts-for-structures
공식 코드https://github.com/vene/sparsemap
공식 코드https://github.com/vyraun/long-tailed
공식 코드https://github.com/wanmok/metametric
공식 코드A Unified View of Evaluation Metrics for Structured Prediction
https://github.com/wouterkool/estimating-gradients-without-replacement
공식 코드Estimating Gradients for Discrete Random Variables by Sampling without Replacement
https://github.com/xingyaoww/code4struct
공식 코드Code4Struct: Code Generation for Few-Shot Event Structure Prediction
https://github.com/xkianteb/leaqi
공식 코드https://github.com/yandex-research/shifts
공식 코드Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
https://github.com/yolu1055/conditional-glow
공식 코드Structured Output Learning with Conditional Generative Flows
https://github.com/ywyue/roomformer
공식 코드Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries
https://github.com/zhongxingyu/seer
공식 코드Learning the Relation between Code Features and Code Transforms with Structured Prediction
https://github.com/zjunlp/speech
공식 코드SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres
https://github.com/zkytony/graphspn
공식 코드Learning Graph-Structured Sum-Product Networks for Probabilistic Semantic Maps
https://github.com/zzsfornlp/zmsp
공식 코드https://worksheets.codalab.org/worksheets/0x6a264a96efea41158847eef9ec2f76bc
공식 코드https://worksheets.codalab.org/worksheets/0xc6edf0c9bec643ac9e74418bd6ad4136
공식 코드https://github.com/ARiSE-Lab/DeepInspect
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
https://github.com/AbhinavAtrishi/semisupervised-multitask-learning
UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation
https://github.com/Adk2001tech/GAN-Image-Super-Resolution
Super-Resolution with Deep Convolutional Sufficient Statistics
https://github.com/CMU-Perceptual-Computing-Lab/openpose
https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin
https://github.com/Cadene/recipe1m.bootstrap.pytorch
Deep Metric Learning via Lifted Structured Feature Embedding
https://github.com/ClePol/CPM_Presentation
https://github.com/D1vyansh/BodyJointDetection
https://github.com/Daniil-Osokin/gccpm-look-into-person-cvpr19.pytorch
https://github.com/Demfier/ebm-sanskrit-word-segmentation
https://github.com/DsSaurabh/SkimLit-Project
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
https://github.com/ErfanMN/Augmented_CycleGAN_Pytorch
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
https://github.com/JM-data/Unsupervised_DeepFunctionalMaps
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
https://github.com/Kaif10/Pose-Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.