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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/souryadey/deep-n-cheap

공식 코드

Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep Learning

https://github.com/spikelearn/spikelearn

공식 코드

AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures

https://github.com/sprocketlab/sparse_matrix_fine_tuning

공식 코드

MoRe Fine-Tuning with 10x Fewer Parameters

https://github.com/sserranw/abg-nas

공식 코드

ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning

https://github.com/statistical-deep-learning/dcs-attention

공식 코드

Differentiable Channel Selection in Self-Attention For Person Re-Identification

https://github.com/stepbuystep/lightnas

공식 코드

You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms

https://github.com/striderdu/spa

공식 코드

Search to Pass Messages for Temporal Knowledge Graph Completion

https://github.com/subhodipbiswas/BayesianEvolution

공식 코드

Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization

https://github.com/sumny/reshuffling

공식 코드

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization

https://github.com/sungdukyu/two-step-hpo

공식 코드

Two-step hyperparameter optimization method: Accelerating hyperparameter search by using a fraction of a training dataset

https://github.com/sunsmarterjie/DAAS

공식 코드

Discretization-Aware Architecture Search

https://github.com/sunsmarterjie/SDL-Skeleton

공식 코드

Adaptive Linear Span Network for Object Skeleton Detection

https://github.com/supernlogn/squeezeDetTL

공식 코드

A Framework of Transfer Learning in Object Detection for Embedded Systems

https://github.com/survival-org/scoring-rules-2024

공식 코드

Examining marginal properness in the external validation of survival models with squared and logarithmic losses

https://github.com/susan0199/stacnas

공식 코드

Hierarchical Neural Architecture Search via Operator Clustering

https://github.com/sxjscience/automl_multimodal_benchmark

공식 코드

Benchmarking Multimodal AutoML for Tabular Data with Text Fields

https://github.com/t-harden/llm4automl

공식 코드

Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey

https://github.com/talcs/naap-440

공식 코드

NAAP-440 Dataset and Baseline for Neural Architecture Accuracy Prediction

https://github.com/taloncb/manas

공식 코드

Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)

https://github.com/tanglang96/DDPNAS

공식 코드

DDPNAS: Efficient Neural Architecture Search via Dynamic Distribution Pruning

https://github.com/tanglang96/MDENAS

공식 코드

Multinomial Distribution Learning for Effective Neural Architecture Search

https://github.com/tanmay-ty/tntrules

공식 코드

Explainable Bayesian Optimization

https://github.com/taoyang1122/revisit_trainingfree_nas

공식 코드

Revisiting Training-free NAS Metrics: An Efficient Training-based Method

https://github.com/tdchaitanya/nas-robustness

공식 코드

On Adversarial Robustness: A Neural Architecture Search perspective

https://github.com/tdenottelander/MacroNAS

공식 코드

Local Search is a Remarkably Strong Baseline for Neural Architecture Search

https://github.com/teddy4445/substrat

공식 코드

SubStrat: A Subset-Based Strategy for Faster AutoML

https://github.com/tend93/cvpr2021_nas_competition_track1_1st_solution

공식 코드

Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive Training

https://github.com/tensorflow/adanet

공식 코드

Improving Neural Architecture Search Image Classifiers via Ensemble Learning

https://github.com/tensorflow/models

공식 코드

Neural Architecture Search with Reinforcement Learning

https://github.com/tensorflow/models/blob/master/official/vision/modeling/backbones/mobilenet.py

공식 코드

MobileNetV4 -- Universal Models for the Mobile Ecosystem

https://github.com/tensorflow/models/tree/master/official/vision/beta/modeling/backbones

공식 코드

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/tensorflow/tensor2tensor

공식 코드

The Evolved Transformer

https://github.com/tensorflow/tpu

공식 코드

MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

공식 코드

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/ternencewu123/gautomri

공식 코드

Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search

https://github.com/tess022095/fair-automl

공식 코드

Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML

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