https://github.com/mengyuanchen21/re-edl
공식 코드Revisiting Essential and Nonessential Settings of Evidential Deep Learning
Papers with Code 아카이브
Model Optimization
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To Optimize already existing models in Training/Inferencing tasks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/qlib
공식 코드R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
https://github.com/microsoft/rd-agent
공식 코드R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
https://github.com/mirsayedshah/AI-coherent-data-driven-forecasting-model-for-a-combined-cycle-power-plant
공식 코드AI-coherent data-driven forecasting model for a combined cycle power plant
https://github.com/muammar/ml4chem
공식 코드ML4Chem: A Machine Learning Package for Chemistry and Materials Science
https://github.com/naver-ai/elva
공식 코드https://github.com/nci/fourcastnext
공식 코드FourCastNeXt: Optimizing FourCastNet Training for Limited Compute
https://github.com/opendatalab/DocLayout-YOLO
공식 코드https://github.com/opendatalab/openhueval
공식 코드OpenHuEval: Evaluating Large Language Model on Hungarian Specifics
https://github.com/openvinotoolkit/anomalib
공식 코드https://github.com/proteus-mlsys24/mlsys24-artifact
공식 코드Proteus: Preserving Model Confidentiality during Graph Optimizations
https://github.com/puerrrr/Self-Paced-Adversarial-Training
공식 코드Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting
https://github.com/purushottamkar/agglio
공식 코드https://github.com/raison-lab/g2d
공식 코드G$^{2}$D: Boosting Multimodal Learning with Gradient-Guided Distillation
https://github.com/recovercoalition/recover
공식 코드https://github.com/reddy-lab-code-research/PPOCoder
공식 코드Execution-based Code Generation using Deep Reinforcement Learning
https://github.com/renqii/dvr
공식 코드Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
https://github.com/reso1/mip-mcpp
공식 코드https://github.com/reycn/multi-modal-scale
공식 코드Hateful Meme Detection through Context-Sensitive Prompting and Fine-Grained Labeling
https://github.com/ricardobnjunior/OctHU-PageScan
공식 코드A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation
https://github.com/ryh1218/fsol
공식 코드https://github.com/salesforce/iclrec
공식 코드https://github.com/skyorca/bottrans
공식 코드BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
https://github.com/slp-rl/sc-phase
공식 코드A Systematic Comparison of Phonetic Aware Techniques for Speech Enhancement
https://github.com/snowteam2023/gfriend
공식 코드GFRIEND: Generative Few-shot Reward Inference through EfficieNt DPO
https://github.com/sssssuperior/vscode
공식 코드VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning
https://github.com/sstary/ssrs
공식 코드SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
https://github.com/sulaimanvesal/CardiacQuanNet
공식 코드Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
https://github.com/sumonbis/ML-Fairness
공식 코드https://github.com/supersyq/rienet
공식 코드Reliable Inlier Evaluation for Unsupervised Point Cloud Registration
https://github.com/sxpro/rfsr
공식 코드RFSR: Improving ISR Diffusion Models via Reward Feedback Learning
https://github.com/tencent/hunyuandit
공식 코드https://github.com/tian-bai/optcs
공식 코드Optimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization
https://github.com/tlc4418/llm_optimization
공식 코드https://github.com/uitrbn/idm
공식 코드https://github.com/uw-bionlp/CACER
공식 코드CACER: Clinical Concept Annotations for Cancer Events and Relations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.