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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 301–350 of 641 papers

TitleStatusHype
Selecting Optimal Trace Clustering Pipelines with AutoML—0
Semantic-Based Neural Network Repair—0
Sequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
SigOpt Mulch: An Intelligent System for AutoML of Gradient Boosted Trees—0
Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications—0
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning—0
Study on the effectiveness of AutoML in detecting cardiovascular disease—0
Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues—0
Synthesis of Mathematical programs from Natural Language Specifications—0
Task Selection for AutoML System Evaluation—0
T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging—0
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene—0
Techniques for Automated Machine Learning—0
Testing the Robustness of AutoML Systems—0
Neural Architectural Backdoors—0
The Potential of AutoML for Recommender Systems—0
The Power of Proxy Data and Proxy Networks for Hyper-Parameter Optimization in Medical Image Segmentation—0
The Roles and Modes of Human Interactions with Automated Machine Learning Systems—0
The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry—0
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search—0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
Towards Automated Machine Learning Research—0
Towards Automated Negative Sampling in Implicit Recommendation—0
Towards AutoML in the presence of Drift: first results—0
Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction—0
Towards Green Automated Machine Learning: Status Quo and Future Directions—0
Towards Human Centered AutoML—0
Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks—0
Towards Personalized Preprocessing Pipeline Search—0
TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model—0
Transferable AutoML by Model Sharing Over Grouped Datasets—0
Transfer Learning with Neural AutoML—0
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems—0
United States Road Accident Prediction using Random Forest Predictor—0
A Versatile Graph Learning Approach through LLM-based Agent—0
Using Audio Data to Facilitate Depression Risk Assessment in Primary Health Care—0
Using Combinatorial Optimization to Design a High quality LLM Solution—0
Using Known Information to Accelerate HyperParameters Optimization Based on SMBO—0
Variation in prediction accuracy due to randomness in data division and fair evaluation using interval estimation—0
A User-based Visual Analytics Workflow for Exploratory Model Analysis—0
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation—0
Warm-starting DARTS using meta-learning—0
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap—0
What Can AutoML Do For Continual Learning?—0
What can multi-cloud configuration learn from AutoML?—0
Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows—0
Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019—0
ZeroML: A Next Generation AutoML Language—0
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All datasetsChalearn-AutoML-1Breast Cancer Coimbra Data SetOrdinalDatasetWine

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1marc.boulleRank (AutoML5)6.4—Unverified
2reference_mbRank (AutoML5)5.2—Unverified
3postech.mlg_exbrainRank (AutoML5)5.2—Unverified
4abhishek4Rank (AutoML5)4.6—Unverified
5referenceRank (AutoML5)4.4—Unverified
6reference_lsRank (AutoML5)4—Unverified
7djajeticRank (AutoML5)3—Unverified
8aad_freiburgRank (AutoML5)1.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic RegressionAccuracy97.02—Unverified
#ModelMetricClaimedVerifiedStatus
1Zero-shot-BERT-SORT1:1 Accuracy55—Unverified
#ModelMetricClaimedVerifiedStatus
1Logistic Regressionaccuracy98.33—Unverified