Benchmarking
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/airglow/simitate_imitation_learning_benchmark
https://github.com/aizhanti/jarunc
https://github.com/ajayjain/vectorascent
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/akulkailwoo/zalando-fashion-mnist
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
https://github.com/aleflabo/HypAD
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
https://github.com/alejandrodemiquel/arc
https://github.com/alex-f1tor/Image-Caption
https://github.com/alexa/alexa-prize-topical-chat-dataset
Topical-Chat: Towards Knowledge-Grounded Open-Domain Conversations
https://github.com/alexa/alexa-with-dstc10-track2-dataset
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
https://github.com/alexssanchez/unet-app-pucp
https://github.com/ali-nsua/quickRecommender
Yum-me: A Personalized Nutrient-based Meal Recommender System
https://github.com/alibaba-damo-academy/VCBench
Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency
https://github.com/alibaba-nlp/webagent
https://github.com/alibaba/EasyNLP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/alirezakazemipour/ppo-rnd
https://github.com/aliyun/qwen-dianjin
https://github.com/allen-li1231/treehop-rag
MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries
https://github.com/allenai/natural-instructions-expansion
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/amazon-science/intent-aware-encoder
Benchmarking Natural Language Understanding Services for building Conversational Agents
https://github.com/amenra/ranx
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/amir-hassan25/IdeaBench
IdeaBench: Benchmarking Large Language Models for Research Idea Generation
https://github.com/amkall/tcc_coco
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
https://github.com/andreamattiagaravagno/colabnas
https://github.com/andreped/livermask
https://github.com/andrey1908/mmdetection
https://github.com/andyreagan/sentiment-analysis-comparison-online-appendix
https://github.com/anissa218/medfair_ukbb
https://github.com/ansfl/learning-based-mems-gyrocompassing
https://github.com/antoinebellemare/dat_gpt
https://github.com/antoinedemathelin/openood
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
https://github.com/apple/ml-mobileclip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/aradhanacha/aif360
https://github.com/archettialberto/federated_survival_forests
Heterogeneous Datasets for Federated Survival Analysis Simulation
https://github.com/arcprize/arc-agi-2
https://github.com/armaank/archlectures
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.