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General Classification

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평가표

11

보존된 평가 기록

19

코드

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

https://github.com/tailintalent/distillation

공식 코드

Pareto-optimal data compression for binary classification tasks

https://github.com/takase/relPatSim

공식 코드

Composing Distributed Representations of Relational Patterns

https://github.com/takashiishida/comp

공식 코드

Complementary-Label Learning for Arbitrary Losses and Models

https://github.com/takashiishida/pconf

공식 코드

Binary Classification from Positive-Confidence Data

https://github.com/takedarts/swgridnet

공식 코드

SwGridNet: A Deep Convolutional Neural Network based on Grid Topology for Image Classification

https://github.com/takeuchi-lab/Learning-Interpretable-Metric-between-Graphs

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Distance Metric Learning for Graph Structured Data

https://github.com/talbaumel/MIMIC

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Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment

https://github.com/tanmay-ty/SpectralNET

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SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification

https://github.com/taolei87/text_convnet

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Molding CNNs for text: non-linear, non-consecutive convolutions

https://github.com/tapilab/aaai-2021-counterfactuals

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Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals

https://github.com/tapilab/emnlp-2020-spurious

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Identifying Spurious Correlations for Robust Text Classification

https://github.com/taufikxu/Triple-GAN

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Triple Generative Adversarial Networks

https://github.com/taufikxu/youtube

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The YouTube-8M Kaggle Competition: Challenges and Methods

https://github.com/tbdair/fedperlv1.0

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Semi-Supervised Federated Peer Learning for Skin Lesion Classification

https://github.com/tdopierre/FewShotText

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A Neural Few-Shot Text Classification Reality Check

https://github.com/tensorflow/models

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Adversarial Training Methods for Semi-Supervised Text Classification

https://github.com/tensorflow/tcav

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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

https://github.com/tensorflow/tensorflow

공식 코드

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

https://github.com/tensorflow/tpu

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CondConv: Conditionally Parameterized Convolutions for Efficient Inference

https://github.com/terryum/Data-Augmentation-For-Wearable-Sensor-Data

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Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks

https://github.com/tgcsaba/GPSig

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Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

https://github.com/tgisaturday/Seq2CNN

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Abstractive Text Classification Using Sequence-to-convolution Neural Networks

https://github.com/thammegowda/005-nmt-imbalance

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Finding the Optimal Vocabulary Size for Neural Machine Translation

https://github.com/thartvigsen/stopandhop

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Stop&Hop: Early Classification of Irregular Time Series

https://github.com/the-lost-explorer/iClaimNet

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A Data Set of Internet Claims and Comparison of their Sentiments with Credibility

https://github.com/theogf/AugmentedGaussianProcesses.jl

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Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation

https://github.com/therebellll/NegIoU-PosIoU-Miou

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Optimized Loss Functions for Object detection: A Case Study on Nighttime Vehicle Detection

https://github.com/theyou21/BigProject

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Mental Task Classification Using Electroencephalogram Signal

https://github.com/thomaskeck/FastBDT

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FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification

https://github.com/thomastkok/active-selection-of-classification-features

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Active Selection of Classification Features

https://github.com/thu-ml/imagenet-a-plus

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Rethinking Natural Adversarial Examples for Classification Models

https://github.com/thuiar/Adaptive-Decision-Boundary

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Deep Open Intent Classification with Adaptive Decision Boundary

https://github.com/thulas/dac-label-noise

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Combating Label Noise in Deep Learning Using Abstention

https://github.com/thumbe3/label-noise-nlp

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Towards Robustness to Label Noise in Text Classification via Noise Modeling

https://github.com/thuml/CDAN

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Conditional Adversarial Domain Adaptation

https://github.com/thunlp/IKRL

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Image-embodied Knowledge Representation Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.