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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/asaparov/parser
공식 코드https://github.com/blaschko/supermodularLP
공식 코드Slack and Margin Rescaling as Convex Extensions of Supermodular Functions
https://github.com/botong516/conformal_structured_prediction
공식 코드https://github.com/cgraber/GSPEN
공식 코드https://github.com/cgraber/NLStruct
공식 코드Deep Structured Prediction with Nonlinear Output Transformations
https://github.com/chao1224/sgnn-ebm
공식 코드Structured Multi-task Learning for Molecular Property Prediction
https://github.com/crazydonkey200/neural-symbolic-machines
공식 코드Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing
https://github.com/cvlab-tohoku/DSEBImageInpainting
공식 코드https://github.com/deep-spin/lp-sparsemap
공식 코드LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction
https://github.com/deepgraphlearning/spn
공식 코드Neural Structured Prediction for Inductive Node Classification
https://github.com/derekchen14/mixture_soft_prompts
공식 코드https://github.com/dorringel/SWVM
공식 코드https://github.com/eaplatanios/makina
공식 코드https://github.com/edvisees/sciDT
공식 코드https://github.com/facebookresearch/dcem
공식 코드https://github.com/faisalml/NucleiSegmentation
공식 코드Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images
https://github.com/fartashf/vsepp
공식 코드VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/fvisin/reseg
공식 코드ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation
https://github.com/gmmarconi/CRiSP-for-Misspecified-Robot-Model
공식 코드Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models
https://github.com/google-research/crossbeam
공식 코드CrossBeam: Learning to Search in Bottom-Up Program Synthesis
https://github.com/hans66hsu/structured_uncertainty_metrics
공식 코드A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on Graphs
https://github.com/harvardnlp/pytorch-struct
공식 코드https://github.com/helloeve/mre-in-one-pass
공식 코드Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
https://github.com/hellowangqian/domain-adaptation-capls
공식 코드Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
https://github.com/hiroki13/instance-based-ner
공식 코드Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
https://github.com/hmph/adversarial-attacks
공식 코드On the Robustness of Semantic Segmentation Models to Adversarial Attacks
https://github.com/hyren/acl
공식 코드https://github.com/iesl/dilated-cnn-ner
공식 코드Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
https://github.com/irfanICMLL/structure_knowledge_distillation
공식 코드https://github.com/jakpra/treeconstructive-supertagging
공식 코드Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories
https://github.com/jind11/HSLN-Joint-Sentence-Classification
공식 코드Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts
https://github.com/kezhang-cs/Video-Summarization-with-LSTM
공식 코드https://github.com/klimzaporojets/consistent-el
공식 코드https://github.com/kmkurn/uxtspwsd
공식 코드Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data
https://github.com/krishnap25/casimir
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.