SOTAVerified

AutoML

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

Papers

Showing 150 of 641 papers

TitleStatusHype
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe SystemsCode7
AutoGluon-TimeSeries: AutoML for Probabilistic Time Series ForecastingCode6
TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML ApplicationsCode6
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed ClusterCode5
MobileVLM V2: Faster and Stronger Baseline for Vision Language ModelCode5
MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly DetectionCode5
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a SecondCode5
Open Problems in Applied Deep LearningCode4
Efficient Automated Deep Learning for Time Series ForecastingCode4
AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative InvestmentCode3
Benchmarking Multimodal AutoML for Tabular Data with Text FieldsCode3
Model-based Asynchronous Hyperparameter and Neural Architecture SearchCode3
Robust and Accurate Object Detection via Adversarial LearningCode3
DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningCode3
EfficientDet: Scalable and Efficient Object DetectionCode3
Benchmarking Automatic Machine Learning FrameworksCode3
MLZero: A Multi-Agent System for End-to-end Machine Learning AutomationCode3
Auto-Sklearn 2.0: Hands-free AutoML via Meta-LearningCode3
Is deep learning necessary for simple classification tasks?Code3
EfficientNetV2: Smaller Models and Faster TrainingCode3
Layered TPOT: Speeding up Tree-based Pipeline OptimizationCode3
AutoGluon-Tabular: Robust and Accurate AutoML for Structured DataCode3
Efficient and Robust Automated Machine LearningCode3
MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile DevicesCode3
Towards Automatically-Tuned Deep Neural NetworksCode2
AMC: AutoML for Model Compression and Acceleration on Mobile DevicesCode2
CAPO: Cost-Aware Prompt OptimizationCode2
ChaCha for Online AutoMLCode2
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter OptimizationCode2
QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video ComprehensionCode2
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and PracticeCode2
MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationCode2
Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AICode2
LightAutoML: AutoML Solution for a Large Financial Services EcosystemCode2
GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data AnalysisCode2
IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning PerspectiveCode2
AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoMLCode2
Fraud Dataset Benchmark and ApplicationsCode2
forester: A Tree-Based AutoML Tool in RCode2
AMLB: an AutoML BenchmarkCode2
DeepMol: An Automated Machine and Deep Learning Framework for Computational ChemistrCode2
HyperFast: Instant Classification for Tabular DataCode2
AutoFormer: Searching Transformers for Visual RecognitionCode2
Frugal Optimization for Cost-related HyperparametersCode2
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDLCode2
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image AnalysisCode2
From Tiny Machine Learning to Tiny Deep Learning: A SurveyCode2
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement LearningCode1
CATE: Computation-aware Neural Architecture Encoding with TransformersCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4Unverified
2reference_mbRank (AutoML5)5.2Unverified
3postech.mlg_exbrainRank (AutoML5)5.2Unverified
4abhishek4Rank (AutoML5)4.6Unverified
5referenceRank (AutoML5)4.4Unverified
6reference_lsRank (AutoML5)4Unverified
7djajeticRank (AutoML5)3Unverified
8aad_freiburgRank (AutoML5)1.6Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33Unverified