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

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Showing 501–550 of 641 papers

TitleStatusHype
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDLCode2
Reusing Trained Layers of Convolutional Neural Networks to Shorten Hyperparameters Tuning Time—0
AutoGAN-Distiller: Searching to Compress Generative Adversarial NetworksCode1
Neural Ensemble Search for Uncertainty Estimation and Dataset ShiftCode1
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?Code1
Is deep learning necessary for simple classification tasks?Code3
Adaptation Strategies for Automated Machine Learning on Evolving DataCode0
Efficient AutoML Pipeline Search with Matrix and Tensor FactorizationCode1
AutoHAS: Efficient Hyperparameter and Architecture Search—0
AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018Code1
A Robust Experimental Evaluation of Automated Multi-Label Classification MethodsCode0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
Noisy Differentiable Architecture SearchCode1
Testing the Robustness of AutoML Systems—0
Frugal Optimization for Cost-related HyperparametersCode2
DriveML: An R Package for Driverless Machine LearningCode1
Lite Transformer with Long-Short Range AttentionCode1
MetaPoison: Practical General-purpose Clean-label Data PoisoningCode1
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical EvolutionCode0
Architecture Disentanglement for Deep Neural NetworksCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Deep-n-Cheap: An Automated Search Framework for Low Complexity Deep LearningCode1
Model-based Asynchronous Hyperparameter and Neural Architecture SearchCode3
AutoGluon-Tabular: Robust and Accurate AutoML for Structured DataCode3
AutoML-Zero: Evolving Machine Learning Algorithms From ScratchCode0
Accelerator-aware Neural Network Design using AutoML—0
BUSU-Net: An Ensemble U-Net Framework for Medical Image Segmentation—0
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning—0
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations—0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoMLCode0
Improving generalisation of AutoML systems with dynamic fitness evaluations—0
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems—0
MixPath: A Unified Approach for One-shot Neural Architecture SearchCode1
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning—0
Searching to Exploit Memorization Effect in Learning with Noisy Labels—0
Evolving Machine Learning Algorithms From Scratch—0
AutoML: Exploration v.s. ExploitationCode0
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates—0
Automatically Optimized Gradient Boosting Trees for Classifying Large Volume High Cardinality Data Streams Under Concept DriftCode1
Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture SearchCode0
Graph Pruning for Model Compression—0
EfficientDet: Scalable and Efficient Object DetectionCode3
FLAML: A Fast and Lightweight AutoML LibraryCode1
Searching to Exploit Memorization Effect in Learning from Corrupted LabelsCode0
Towards Human Centered AutoML—0
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering—0
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures—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