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General Classification

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평가표

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보존된 평가 기록

19

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/devmotion/CalibrationPaper

공식 코드

Calibration tests in multi-class classification: A unifying framework

https://github.com/dfwehs/bsrgan

공식 코드

Bi-Semantic Reconstructing Generative Network for Zero-shot Learning

https://github.com/dginev/arxiv-statement-classification

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Scientific Statement Classification over arXiv.org

https://github.com/dginev/showcase-statement-classification

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Scientific Statement Classification over arXiv.org

https://github.com/dgragnaniello/PQP

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Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers

https://github.com/dhfbk/twitter-abusive-context-dataset

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Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection

https://github.com/dhruboroy29/MSCRNN

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One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification

https://github.com/dice-group/NLIWOD/tree/master/qa.ml

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Using Multi-Label Classification for Improved Question Answering

https://github.com/dicecco1/fpga_caffe

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Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

https://github.com/digitalepidemiologylab/covid-twitter-bert

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COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter

https://github.com/dimonenka/DEDPUL

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DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled Learning

https://github.com/dinghanshen/SWEM

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Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms

https://github.com/diningphil/CGMM

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Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

https://github.com/diningphil/continual_learning_for_graphs

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Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph Classification

https://github.com/diningphil/gnn-comparison

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A Fair Comparison of Graph Neural Networks for Graph Classification

https://github.com/discovershu/SoRR

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Learning by Minimizing the Sum of Ranked Range

https://github.com/divamgupta/mttdsc

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Multi-task Learning for Target-dependent Sentiment Classification

https://github.com/divelab/lsl-fly

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Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples

https://github.com/divelab/vqa-text

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Learning Convolutional Text Representations for Visual Question Answering

https://github.com/divyanshrm/Polyth-Net-Classification-of-Polythene-Bags-Using-Deep-Dearning

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Polyth-Net: Classification of Polythene Bags for Garbage Segregation Using Deep Learning

https://github.com/dki-lab/covid19-classification

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Document Classification for COVID-19 Literature

https://github.com/dkoguciuk/ensemble_learning_for_point_clouds

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3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models

https://github.com/dl4amc/dds

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Ensemble Wrapper Subsampling for Deep Modulation Classification

https://github.com/dl4amc/dl4wii

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Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection

https://github.com/dl4amc/source

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Fast Deep Learning for Automatic Modulation Classification

https://github.com/dlmacedo/entropic-out-of-distribution-detection

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Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples

https://github.com/dmbee/seglearn

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Seglearn: A Python Package for Learning Sequences and Time Series

https://github.com/dmilios/dirichletGPC

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Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification

https://github.com/dmitryako/aus10spectrograms

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Data-Efficient Classification of Birdcall Through Convolutional Neural Networks Transfer Learning

https://github.com/dmlc/gluon-cv

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Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/dmlc/xgboost

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XGBoost: A Scalable Tree Boosting System

https://github.com/dobriban/high-dim-risk-experiments

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High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification

https://github.com/dominikalfke/GCNModel

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Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks

https://github.com/dominikalfke/LowRankGCNExperiments

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Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks

https://github.com/dominikalfke/TimeSeriesSSL

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An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classification

https://github.com/dominiksinsaarland/DocSCAN

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DocSCAN: Unsupervised Text Classification via Learning from Neighbors

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