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AutoML

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하위 태스크: 3
평가표: 4
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/awslabs/adatune

공식 코드

MARTHE: Scheduling the Learning Rate Via Online Hypergradients

https://github.com/awslabs/autogluon

공식 코드

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation

https://github.com/awslabs/syne-tune

공식 코드

Model-based Asynchronous Hyperparameter and Neural Architecture Search

https://github.com/azoz01/liltab

공식 코드

Rethinking of Encoder-based Warm-start Methods in Hyperparameter Optimization

https://github.com/baofff/stability_ho

공식 코드

Stability and Generalization of Bilevel Programming in Hyperparameter Optimization

https://github.com/basusourya/autoequivariant_networks

공식 코드

Autoequivariant Network Search via Group Decomposition

https://github.com/bbopt/HyperNOMAD

공식 코드

HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search

https://github.com/beichenzbc/supernet-shifting

공식 코드

Boosting Order-Preserving and Transferability for Neural Architecture Search: a Joint Architecture Refined Search and Fine-tuning Approach

https://github.com/ben-hawks/pytorch-jet-classify

공식 코드

Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference

https://github.com/bermanmaxim/AOWS

공식 코드

AOWS: Adaptive and optimal network width search with latency constraints

https://github.com/bestfleer/RepNAS

공식 코드

RepNAS: Searching for Efficient Re-parameterizing Blocks

https://github.com/bestfleer/shiftnas

공식 코드

ShiftNAS: Improving One-shot NAS via Probability Shift

https://github.com/bhpfelix/MTLNAS

공식 코드

MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning

https://github.com/bidyutsaha/tinytnas

공식 코드

TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classification

https://github.com/bigdama/declarativeautoml

공식 코드

AutoML in Heavily Constrained Applications

https://github.com/bingqingcheng/linear-regression-benchmarks

공식 코드

BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale

https://github.com/bingzhaozhu/xtab

공식 코드

XTab: Cross-table Pretraining for Tabular Transformers

https://github.com/bkolosk1/bablfusion

공식 코드

AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification

https://github.com/boschresearch/metanas

공식 코드

Meta-Learning of Neural Architectures for Few-Shot Learning

https://github.com/brdav/atrc

공식 코드

Exploring Relational Context for Multi-Task Dense Prediction

https://github.com/brdav/bmtas

공식 코드

Automated Search for Resource-Efficient Branched Multi-Task Networks

https://github.com/brian-moser/dartsrenet

공식 코드

DartsReNet: Exploring new RNN cells in ReNet architectures

https://github.com/btma48/ITNAS

공식 코드

Inter-layer Transition in Neural Architecture Search

https://github.com/bychen515/bnnas

공식 코드

BN-NAS: Neural Architecture Search with Batch Normalization

https://github.com/bychen515/glit

공식 코드

GLiT: Neural Architecture Search for Global and Local Image Transformer

https://github.com/c0pep0d/rlfl0w

공식 코드

A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows

https://github.com/callowbird/Harmonica

공식 코드

Hyperparameter Optimization: A Spectral Approach

https://github.com/cambridge-mlg/arch_uncert

공식 코드

Variational Depth Search in ResNets

https://github.com/cambridgeltl/autopeft

공식 코드

AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning

https://github.com/cap-lab/S3NAS

공식 코드

S3NAS: Fast NPU-aware Neural Architecture Search Methodology

https://github.com/capoe/benchml

공식 코드

BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale

https://github.com/cat-loves-donuts/ImmuNetNAS

공식 코드

ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures

https://github.com/cc-hpc-itwm/HPO_LazyGPR

공식 코드

Scalable Hyperparameter Optimization with Lazy Gaussian Processes

https://github.com/cc-hpc-itwm/PHS

공식 코드

PHS: A Toolbox for Parallel Hyperparameter Search

https://github.com/ccc-frankfurt/DP-NAS

공식 코드

Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference

https://github.com/cecilia-xue/hyt-nas

공식 코드

Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image Classification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.