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 526–550 of 641 papers

TitleStatusHype
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