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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/LouisBouvier/WarcraftShortestPaths.jl
https://github.com/LuK2019/ICNN_BA
https://github.com/MindCode-4/code-12/tree/main/Frank-Wolfe-Algorithm
https://github.com/MindCode-4/code-13/tree/main/ReSeg
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
https://github.com/MindCode-4/code-7/tree/main/Frank-Wolfe-Algorithm
https://github.com/MindCode-4/code-8/tree/main/ReSeg
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
https://github.com/MoeinSorkhei/glow2
Structured Output Learning with Conditional Generative Flows
https://github.com/Namkyeong/NoisyNodes_Pytorch
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
https://github.com/NathanDeMaria/AugmentedCycleGAN
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
https://github.com/PranavR-2001/Project_Med_Skim
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
https://github.com/Refefer/Dagger
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
https://github.com/RohitGanji/NLP-Medical-Paper-Abstracts-Classification
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
https://github.com/RohitGanji/medical-paper-abstract-classification
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
https://github.com/SConsul/ReSeg
ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
https://github.com/SciKnowEngine/Molecular_Interaction_Evidence_Fragment_Corpus
https://github.com/Tuofengalways/ee_Model
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
https://github.com/YaoXinZhi/BERT-for-BioNLP-OST2019-AGAC-Task2
A Frustratingly Easy Approach for Entity and Relation Extraction
https://github.com/aabbas90/cluster-fug
https://github.com/aalmah/augmented_cyclegan
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
https://github.com/alanlahoud/datasp
https://github.com/alexbarthhd/3DCV_project
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
https://github.com/amazon-science/tanl
Structured Prediction as Translation between Augmented Natural Languages
https://github.com/armandvilalta/Full-network-multimodal-embeddings
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/aurelio-amerio/concretedropout
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
https://github.com/blake58/openpose
https://github.com/bond005/yandex-shifts-weather
Shifts: A Dataset of Real Distributional Shift Across Multiple Large-Scale Tasks
https://github.com/brijml/xcaliber-task
https://github.com/chanyn/3Dpose_ssl
https://github.com/chengguixiong1010/rnn_STAF_2dpose
https://github.com/chenyouxin113/sota-status-investigation
https://github.com/christina-winkler/master-thesis-project
https://github.com/chunhuililili/mt_dnn
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
https://github.com/coreylowman/ragz
https://github.com/coreylowman/synthesis
https://github.com/cshizhe/hgr_v2t
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.