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AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/vanderschaarlab/clairvoyance
공식 코드https://github.com/vanderschaarlab/climb
공식 코드CliMB: An AI-enabled Partner for Clinical Predictive Modeling
https://github.com/varunakk/imaginator
공식 코드IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images
https://github.com/vascolopes/lcmnas
공식 코드https://github.com/vascolopes/nas-benchmark-evaluation
공식 코드Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importance
https://github.com/vdigpku/flownas
공식 코드FlowNAS: Neural Architecture Search for Optical Flow Estimation
https://github.com/vdigpku/nas-bnn
공식 코드NAS-BNN: Neural Architecture Search for Binary Neural Networks
https://github.com/vidhur2k/CS8803-Project
공식 코드Efficient Incorporation of Multiple Latency Targets in the Once-For-All Network
https://github.com/viharoszsolt/idenas
공식 코드https://github.com/vimar-gu/msinet
공식 코드MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID
https://github.com/vis-opt-group/bl
공식 코드Bilevel Fast Scene Adaptation for Low-Light Image Enhancement
https://github.com/visinf/adapter_plus
공식 코드https://github.com/vita-group/architecture_convergence
공식 코드Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis
https://github.com/vita-group/principled_scaling_lr_init
공식 코드https://github.com/vita-group/tegnas
공식 코드https://github.com/vovantuan1999a/OoD-ViT-NAS
공식 코드https://github.com/walkerning/aw_nas
공식 코드A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
https://github.com/walkerning/aw_nas/tree/master/examples/research/ftt-nas
공식 코드FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture
https://github.com/waltafwilliams/amdet
공식 코드AMDet: A Tool for Mitotic Cell Detection in Histopathology Slides
https://github.com/wentaozhu/AutoShot
공식 코드AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection
https://github.com/wentaozhu/speechnas
공식 코드https://github.com/wenwei202/autogrow
공식 코드AutoGrow: Automatic Layer Growing in Deep Convolutional Networks
https://github.com/western-oc2-lab/automl-and-adversarial-attack-defense-for-zero-touch-network-security
공식 코드Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis
https://github.com/western-oc2-lab/automl-implementation-for-static-and-dynamic-data-analytics
공식 코드IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective
https://github.com/western-oc2-lab/autonomouscyber-automl-based-autonomous-intrusion-detection-system
공식 코드Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection
https://github.com/western-oc2-lab/cross-layer-autonomous-cybersecurity-framework
공식 코드Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks
https://github.com/wglab/integer_divsibility
공식 코드Classification of integers based on residue classes via modern deep learning algorithms
https://github.com/whittle-org/plm_pruning
공식 코드Structural Pruning of Pre-trained Language Models via Neural Architecture Search
https://github.com/wiljohnhong/dropnas
공식 코드DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
https://github.com/willieneis/bayesian-algorithm-execution
공식 코드https://github.com/wmcnally/evopose2d
공식 코드https://github.com/wnhsu/ScalableFHVAE
공식 코드Scalable Factorized Hierarchical Variational Autoencoder Training
https://github.com/woodywff/nas_3d_unet
공식 코드Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging
https://github.com/wronnyhuang/metapoison
공식 코드MetaPoison: Practical General-purpose Clean-label Data Poisoning
https://github.com/wufan-cse/g-nas
공식 코드G-NAS: Generalizable Neural Architecture Search for Single Domain Generalization Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.