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/mind-Lab/octis
공식 코드https://github.com/mindspore-lab/mindone
공식 코드Multiscale Positive-Unlabeled Detection of AI-Generated Texts
https://github.com/mingkai-zheng/cone
공식 코드CoNe: Contrast Your Neighbours for Supervised Image Classification
https://github.com/minglllli/CLS-RL
공식 코드Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-Tuning
https://github.com/mingruiliu-ml-lab/accelerated-bilevel-optimization-unbounded-smoothness
공식 코드An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness
https://github.com/mingruiliu-ml-lab/bilevel-optimization-under-unbounded-smoothness
공식 코드Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis
https://github.com/mingxuanxia/candist
공식 코드Prompt Candidates, then Distill: A Teacher-Student Framework for LLM-driven Data Annotation
https://github.com/mingyue-cheng/instructtime
공식 코드Advancing Time Series Classification with Multimodal Language Modeling
https://github.com/mingzelucasni/ce-attack
공식 코드Cross-Entropy Attacks to Language Models via Rare Event Simulation
https://github.com/minhlong94/random-mixup
공식 코드Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
https://github.com/minicheshire/robust-prefix-tuning
공식 코드https://github.com/minieggz/effiara
공식 코드https://github.com/minieggz/ruc-misinfo
공식 코드https://github.com/minogame/caffe-qhconv
공식 코드Design of Kernels in Convolutional Neural Networks for Image Classification
https://github.com/mintisan/awesome-kan
공식 코드FourierKAN outperforms MLP on Text Classification Head Fine-tuning
https://github.com/mir-aidj/edm-subgenre-classifier
공식 코드Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features
https://github.com/miracle-2001/gnn4eeg
공식 코드GNN4EEG: A Benchmark and Toolkit for Electroencephalography Classification with Graph Neural Network
https://github.com/miraiaroha/ACAN
공식 코드Attention-based Context Aggregation Network for Monocular Depth Estimation
https://github.com/mirand863/hiclass
공식 코드HiClass: a Python library for local hierarchical classification compatible with scikit-learn
https://github.com/mireshghallah/style-pooling
공식 코드Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness
https://github.com/mirkolai/leave-or-remain
공식 코드#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection
https://github.com/mirsazzathossain/rgc-bent
공식 코드RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification
https://github.com/mirzaalimm/truncationvssummarization
공식 코드Investigating Text Shortening Strategy in BERT: Truncation vs Summarization
https://github.com/misahub2023/benchmarking-codes-of-the-wcebleedgen-dataset
공식 코드https://github.com/misterpawan/lightdxml
공식 코드https://github.com/mitre-attack/tram
공식 코드Cyber-Attack Technique Classification Using Two-Stage Trained Large Language Models
https://github.com/mkachuee/GenerativeImputationStochasticPrediction
공식 코드https://github.com/mkirchmeyer/adaptation-imputation
공식 코드Unsupervised domain adaptation with non-stochastic missing data
https://github.com/mkoshkina/teamId
공식 코드Contrastive Learning for Sports Video: Unsupervised Player Classification
https://github.com/ml-edm/ml_edm
공식 코드https://github.com/ml-group-sdu/macro-auc-cl
공식 코드Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
https://github.com/ml-gsai/dpt
공식 코드Diffusion Models and Semi-Supervised Learners Benefit Mutually with Few Labels
https://github.com/ml-hdu/lbl_lblsig
공식 코드LBL: Logarithmic Barrier Loss Function for One-class Classification
https://github.com/ml-jku/DeepRC
공식 코드Modern Hopfield Networks and Attention for Immune Repertoire Classification
https://github.com/ml-research/xil
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.