https://github.com/michaelzhang01/gpmoe
공식 코드Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Papers with Code 아카이브
AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
- 11
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/michailchatzianastasis/graph-based_nas_with_operation_embeddings
공식 코드Graph-based Neural Architecture Search with Operation Embeddings
https://github.com/microsoft/AutoML
공식 코드https://github.com/microsoft/CoML
공식 코드MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
https://github.com/microsoft/FLAML
공식 코드https://github.com/microsoft/NeuralSpeech
공식 코드LightSpeech: Lightweight and Fast Text to Speech with Neural Architecture Search
https://github.com/microsoft/archai
공식 코드https://github.com/microsoft/automoe
공식 코드https://github.com/microsoft/cream
공식 코드https://github.com/microsoft/petridishnn
공식 코드https://github.com/mikelzc1990/nsganetv2
공식 코드Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification
https://github.com/mila-iqia/Awesome-Offline-Model-Based-Optimization
공식 코드https://github.com/milkmilk511/lapt
공식 코드Design Principle Transfer in Neural Architecture Search via Large Language Models
https://github.com/mind-Lab/octis
공식 코드https://github.com/mindspore-ai/models/tree/master/research/cv/ISyNet
공식 코드ISyNet: Convolutional Neural Networks design for AI accelerator
https://github.com/mingkai-zheng/genius
공식 코드https://github.com/mingruiliu-ml-lab/bilevel-optimization-under-unbounded-smoothness
공식 코드Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis
https://github.com/minh1409/dft-npzs-nas
공식 코드https://github.com/minimario/morbit
공식 코드Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning
https://github.com/miraclehh/nas_privacy
공식 코드https://github.com/mit-han-lab/amc
공식 코드AMC: AutoML for Model Compression and Acceleration on Mobile Devices
https://github.com/mit-han-lab/gan-compression
공식 코드GAN Compression: Efficient Architectures for Interactive Conditional GANs
https://github.com/mit-han-lab/hardware-aware-transformers
공식 코드HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
https://github.com/mit-han-lab/lite-transformer
공식 코드https://github.com/mit-han-lab/once-for-all
공식 코드Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/mit-han-lab/spvnas
공식 코드Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/mjbommar/gpt-takes-the-bar-exam
공식 코드https://github.com/mkhodak/relax
공식 코드https://github.com/mlsysops/active-learning-as-a-service
공식 코드Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
https://github.com/mlsysops/alaas
공식 코드Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI
https://github.com/mming11/robustdatasetdistillation
공식 코드Group Distributionally Robust Dataset Distillation with Risk Minimization
https://github.com/mo666666/sgm
공식 코드On the Adversarial Transferability of Generalized "Skip Connections"
https://github.com/modeloriented/forester
공식 코드https://github.com/mohamed-imed-eddine/harmonic-nas
공식 코드Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices
https://github.com/mohsaied/zero-cost-nas
공식 코드https://github.com/moremnas/MoreMNAS
공식 코드Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.