https://github.com/microsoft/MSMARCO-Document-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Papers with Code 아카이브
Benchmarking
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/microsoft/MSMARCO-OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Passage-Ranking
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/MSMARCO-Question-Answering
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/Multilingual-Evaluation-of-Generative-AI-MEGA
https://github.com/microsoft/OpenKP
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/TREC-2019-Deep-Learning
MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
https://github.com/microsoft/klite
K-LITE: Learning Transferable Visual Models with External Knowledge
https://github.com/microsoft/unicl
K-LITE: Learning Transferable Visual Models with External Knowledge
https://github.com/mindspore-courses/ABSA-MindSpore
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
https://github.com/minerllabs/minerl
https://github.com/minhanh151/pre
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/minhanh151/respro
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mit-spark/kimera-multi
Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
https://github.com/mitmedialab/viznet
VizNet: Towards A Large-Scale Visualization Learning and Benchmarking Repository
https://github.com/ml-energy/zeus
The ML.ENERGY Benchmark: Toward Automated Inference Energy Measurement and Optimization
https://github.com/ml-jku/cloob
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlbio-epfl/turtle
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlcommons/chakra
Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces
https://github.com/mlcommons/inference
https://github.com/mlcommons/submissions_algorithms
https://github.com/mlcommons/training
https://github.com/mlf-core/liver-ct-segmentation
https://github.com/mlfoundations/Evalchemy
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
https://github.com/mlfoundations/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mlr-org/mlr
Benchmarking time series classification -- Functional data vs machine learning approaches
https://github.com/mmore500/signalgp-lite
SignalGP-Lite: Event Driven Genetic Programming Library for Large-Scale Artificial Life Applications
https://github.com/mmurrad/eeg-to-text
https://github.com/mobarakol/AP-MTL
https://github.com/moein-shariatnia/OpenAI-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/mr-eggplant/eata
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/mr-eggplant/sar
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/mroberto166/nio
OpenFWI: Large-Scale Multi-Structural Benchmark Datasets for Seismic Full Waveform Inversion
https://github.com/msd-irimas/multi_comparison_matrix
Convolutional and Deep Learning based techniques for Time Series Ordinal Classification
https://github.com/mtanti/coco-caption
https://github.com/mtcld/mmdetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.