https://github.com/PennyLaneAI/PennyLane-IonQ
PennyLane: Automatic differentiation of hybrid quantum-classical computations
Papers with Code 아카이브
BIG-bench Machine Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 2
This branch include most common machine learning fundamental algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/PennyLaneAI/PennyLane-qsharp
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-aqt
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-cirq
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-forest
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-honeywell
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-lightning
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-orquestra
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-pq
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-qiskit
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-qulacs
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/PennyLaneAI/pennylane-sf
PennyLane: Automatic differentiation of hybrid quantum-classical computations
https://github.com/Pepitaw/darknet
https://github.com/PetersonSilvadeJesus/yolov3-data-images
https://github.com/PranjalSahu/InteractiveSmoothingDBT
A Cone-Beam X-Ray CT Data Collection designed for Machine Learning
https://github.com/PrivPkt/PrivPkt
Membership Inference Attacks against Machine Learning Models
https://github.com/Priyanshiguptaaa/Intent_Recognition_BERT
https://github.com/PurdueDualityLab/TensorFlowModels
https://github.com/Pushkar-v/Generating-Synthetic-Data-using-GANs
https://github.com/QVPR/VPRTempo
Making a Spiking Net Work: Robust brain-like unsupervised machine learning
https://github.com/Qengineering/YoloV4-ncnn-Jetson-Nano
https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4
https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-64-bit
https://github.com/QuHackEd/Challenges
https://github.com/RB609/QML
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
https://github.com/Rahmanzia3/darknet
https://github.com/Rahmanzia3/darknet_original
https://github.com/Rahmanzia3/yolo
https://github.com/Rahul-Venugopal/Image-augmentation_1
Augmentor: An Image Augmentation Library for Machine Learning
https://github.com/Rainymood/Gradient-Descent-The-Ultimate-Optimizer
https://github.com/RajeevReddyIlavala/Low-Shot-Learning-Learning
Low-shot Visual Recognition by Shrinking and Hallucinating Features
https://github.com/RasaHQ/rasa
Rasa: Open Source Language Understanding and Dialogue Management
https://github.com/RaulBz/Master_Thesis
Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
https://github.com/RaulBz/Master_Thesis_Code
Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
https://github.com/RedWang1989/YOLOv4
https://github.com/RemyMess/auto-sig-encoder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.