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Data Summarization
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Data Summarization is a central problem in the area of machine learning, where we want to compute a small summary of the data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MikeJaredS/hermiter
공식 코드https://github.com/PythonOT/COOT
공식 코드https://github.com/alirezaghods/t2p-time-to-pattern
공식 코드Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization
https://github.com/andymlau/Deuteros_2.0
공식 코드https://github.com/decile-team/submodlib
공식 코드https://github.com/dilab-ustcsz/cmust
공식 코드Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework
https://github.com/dionman/beta-cores
공식 코드$β$-Cores: Robust Large-Scale Bayesian Data Summarization in the Presence of Outliers
https://github.com/facebookresearch/blackbox-coresets-vi
공식 코드https://github.com/ibramjub/Fast-and-Accurate-Least-Mean-Squares-Solvers
공식 코드https://github.com/ilia10000/dataset-distillation
공식 코드Soft-Label Dataset Distillation and Text Dataset Distillation
https://github.com/j-yuan/gequality
공식 코드https://github.com/jmschrei/apricot
공식 코드apricot: Submodular selection for data summarization in Python
https://github.com/matthklein/fair_k_center_clustering
공식 코드https://github.com/neato47/neural-numeric-to-text-generation
공식 코드Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data
https://github.com/ondrejbohdal/label-distillation
공식 코드Flexible Dataset Distillation: Learn Labels Instead of Images
https://github.com/pranonrahman/chartsumm
공식 코드ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries
https://github.com/s3-lab-iit/diffred
공식 코드https://github.com/sbuschjaeger/SubmodularStreamingMaximization
공식 코드https://github.com/sinead/dmmd
공식 코드Understanding collections of related datasets using dependent MMD coresets
https://github.com/wxiao0421/onlineNPCORR
공식 코드https://github.com/yhwang1990/code-bsm-release
공식 코드Balancing Utility and Fairness in Submodular Maximization (Technical Report)
https://github.com/yhwang1990/code-fdm
공식 코드Streaming Algorithms for Diversity Maximization with Fairness Constraints
https://github.com/zalanborsos/bilevel_coresets
공식 코드Semi-supervised Batch Active Learning via Bilevel Optimization
https://github.com/DamianStraszak/FairDiverseDPPSampling
https://github.com/Guang000/Awesome-Dataset-Distillation
Soft-Label Dataset Distillation and Text Dataset Distillation
https://github.com/cran/hermiter
Sequential estimation of Spearman rank correlation using Hermite series estimators
https://github.com/easeml/datascope
https://github.com/ehsankazemi/streamingkextendible
Streaming Submodular Maximization under a $k$-Set System Constraint
https://github.com/gowthamasu/Coverage_based_sample_design
Coverage-Based Designs Improve Sample Mining and Hyper-Parameter Optimization
https://github.com/harshp1802/text-dataset-distillation
Soft-Label Dataset Distillation and Text Dataset Distillation
https://github.com/huggingface/transformers
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering
https://github.com/parajain/StructuredData_To_Descriptions
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization
https://github.com/sstocksieker/dair
https://github.com/webis-de/small-text
https://github.com/zaeemzadeh/Active-Learning-UCF101-IPM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.