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
Channel-wise Hessian Aware trace-Weighted Quantization of Neural Networks—0
NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture SearchCode0
Hardware-Centric AutoML for Mixed-Precision Quantization—0
Iterative Compression of End-to-End ASR Model using AutoML—0
Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap—0
Practical and sample efficient zero-shot HPO—0
AutoRec: An Automated Recommender SystemCode0
Memory-efficient Embedding for Recommendations—0
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation—0
AutoCP: Automated Pipelines for Accurate Prediction Intervals—0
Reusing Trained Layers of Convolutional Neural Networks to Shorten Hyperparameters Tuning Time—0
Adaptation Strategies for Automated Machine Learning on Evolving DataCode0
AutoHAS: Efficient Hyperparameter and Architecture Search—0
A Robust Experimental Evaluation of Automated Multi-Label Classification MethodsCode0
A New Deep Neural Architecture Search Pipeline for Face Recognition—0
Testing the Robustness of AutoML Systems—0
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical EvolutionCode0
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
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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