Papers with Code 아카이브
Classification
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Classification is the task of categorizing a set of data into predefined classes or groups. The aim of classification is to train a model to correctly predict the class or group of new, unseen data. The model is trained on a labeled dataset where each instance is assigned a class label. The learning algorithm then builds a mapping between the features of the data and the class labels. This mapping is then used to predict the class label of new, unseen data points. The quality of the prediction is usually evaluated using metrics such as accuracy, precision, and recall.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shjo-april/TTD
공식 코드TTD: Text-Tag Self-Distillation Enhancing Image-Text Alignment in CLIP to Alleviate Single Tag Bias
https://github.com/shmsw25/Channel-LM-Prompting
공식 코드Noisy Channel Language Model Prompting for Few-Shot Text Classification
https://github.com/shreydesai/adaptive-ensembling
공식 코드Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis
https://github.com/shrezaei/MultitaskTrafficClassification
공식 코드https://github.com/shuaizhao95/Unlearning
공식 코드Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation
https://github.com/shubham30vatsal/web-of-law
공식 코드Classification of US Supreme Court Cases using BERT-Based Techniques
https://github.com/shubhamchaudhary2015/ct_covid19_cap_cnn
공식 코드Detecting COVID-19 and Community Acquired Pneumonia using Chest CT scan images with Deep Learning
https://github.com/shuningjin/discrete-text-rep
공식 코드Discrete Latent Variable Representations for Low-Resource Text Classification
https://github.com/shuo-zhou/gsda-lateralization
공식 코드https://github.com/shurongwang/DynFrs
공식 코드DynFrs: An Efficient Framework for Machine Unlearning in Random Forest
https://github.com/shuzhouyuan/gnnavi
공식 코드GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network
https://github.com/shwangtangjun/Diff-ResNet
공식 코드Diffusion Mechanism in Residual Neural Network: Theory and Applications
https://github.com/shweta-jacob/ssnp
공식 코드Stochastic Subgraph Neighborhood Pooling for Subgraph Classification
https://github.com/shwinshaker/simseed
공식 코드https://github.com/shyam671/mask2anomaly-unmasking-anomalies-in-road-scene-segmentation
공식 코드https://github.com/shyamgupta196/bone-fracture-classification
공식 코드https://github.com/shyamvarahagiri/3d-convsst
공식 코드3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classification
https://github.com/shyhyawJou/ExquisiteNetV2
공식 코드A Novel lightweight Convolutional Neural Network, ExquisiteNetV2
https://github.com/siavashshams/ssamba
공식 코드SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model
https://github.com/sibirbil/RuleDiscovery
공식 코드Rule Generation for Classification: Scalability, Interpretability, and Fairness
https://github.com/sibirbil/SimpleKernels
공식 코드Low-dimensional Interpretable Kernels with Conic Discriminant Functions for Classification
https://github.com/sid-unizar/ling-int-survey
공식 코드Linguistic Interpretability of Transformer-based Language Models: a systematic review
https://github.com/sidchaini/distclassipy
공식 코드Light Curve Classification with DistClassiPy: a new distance-based classifier
https://github.com/sidchaini/lightcurvedistanceclassification
공식 코드Light Curve Classification with DistClassiPy: a new distance-based classifier
https://github.com/siddhant-doshi/spin
공식 코드Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification
https://github.com/siddhantbikram/memeclip
공식 코드MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification
https://github.com/sigeisler/s2gnn
공식 코드https://github.com/siheon-park/quic-projects
공식 코드Variational Quantum Approximate Support Vector Machine with Inference Transfer
https://github.com/siku-bert/sikugpt
공식 코드https://github.com/silvialaurapintea/reg-cls
공식 코드A step towards understanding why classification helps regression
https://github.com/siml3/hl-net
공식 코드HL-Net: Heterophily Learning Network for Scene Graph Generation
https://github.com/simon-tan/DKT-DSC
공식 코드Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing
https://github.com/simonZhou86/Advanced-Data-Analytics-Project
공식 코드Shoulder Implant X-Ray Manufacturer Classification: Exploring with Vision Transformer
https://github.com/simonbon/fishing
공식 코드FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection
https://github.com/simonucl/HJCL
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.