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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/themody/nas-catastrophicforgetting
공식 코드Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers
https://github.com/themody/nasforsentenceembeddingheads
공식 코드Neural Architecture Search for Sentence Classification with BERT
https://github.com/thepanlab/NACHOS
공식 코드https://github.com/theyou21/BigProject
공식 코드Mental Task Classification Using Electroencephalogram Signal
https://github.com/thomasfirmin/hpo_snn
공식 코드Parallel Hyperparameter Optimization Of Spiking Neural Network
https://github.com/thu-spmi/ST-NAS
공식 코드https://github.com/thumnlab/autogl-light
공식 코드https://github.com/thumnlab/nas-bench-graph
공식 코드NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search
https://github.com/thunderstruct/hivenas
공식 코드HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization
https://github.com/tiandunx/loss_function_search
공식 코드https://github.com/tianyic/only_train_once
공식 코드Automated Search-Space Generation Neural Architecture Search
https://github.com/tim120526/AutoM3L
공식 코드AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models
https://github.com/timofeevalex/ssnas_imbalanced
공식 코드Self-Supervised Neural Architecture Search for Imbalanced Datasets
https://github.com/tinyvision/damo-yolo
공식 코드https://github.com/tisabe/jraph_mpeu
공식 코드Band-gap regression with architecture-optimized message-passing neural networks
https://github.com/tkusmierczyk/bayesian_hyperparameters_matching
공식 코드Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching
https://github.com/toenshoff/lrgb
공식 코드Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
https://github.com/tommiekerssies/autopatch
공식 코드https://github.com/tomomasayamasaki/RBFleX-NAS
공식 코드https://github.com/tongzhou0101/obfunas
공식 코드ObfuNAS: A Neural Architecture Search-based DNN Obfuscation Approach
https://github.com/torchsynth/torchsynth
공식 코드https://github.com/towers-d/nas-unseen-datasets
공식 코드Insights from the Use of Previously Unseen Neural Architecture Search Datasets
https://github.com/tsinghua-ideal/Canvas
공식 코드Canvas: End-to-End Kernel Architecture Search in Neural Networks
https://github.com/tsumansapkota/noisy-heuristics-nas
공식 코드Noisy Heuristics NAS: A Network Morphism based Neural Architecture Search using Heuristics
https://github.com/tsynbio/autope
공식 코드https://github.com/ubc-nlp/llmas
공식 코드LLM Performance Predictors are good initializers for Architecture Search
https://github.com/ubc-nlp/mos
공식 코드https://github.com/ucr-optml/FedNest
공식 코드FedNest: Federated Bilevel, Minimax, and Compositional Optimization
https://github.com/udellgroup/oboe
공식 코드https://github.com/ultmaster/acenas
공식 코드Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain
https://github.com/ultmaster/deeper-insights-weight-sharing
공식 코드Deeper Insights into Weight Sharing in Neural Architecture Search
https://github.com/uma-pi1/grash
공식 코드Start Small, Think Big: On Hyperparameter Optimization for Large-Scale Knowledge Graph Embeddings
https://github.com/umair-nasir14/llmatic
공식 코드LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization
https://github.com/urbslab/streamline
공식 코드https://github.com/utkarshnath/Adjoint-Network
공식 코드Adjoined Networks: A Training Paradigm with Applications to Network Compression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.