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 501–550 of 641 papers

TitleStatusHype
Demo Application for the AutoGOAL Framework—0
Long Short Term Memory Networks for Bandwidth Forecasting in Mobile Broadband Networks under Mobility—0
Automatic selection of clustering algorithms using supervised graph embeddingCode0
Automated Model Compression by Jointly Applied Pruning and Quantization—0
FDNAS: Improving Data Privacy and Model Diversity in AutoML—0
Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning—0
AgEBO-Tabular: Joint Neural Architecture and Hyperparameter Search with Autotuned Data-Parallel Training for Tabular Data—0
Resource-Aware Pareto-Optimal Automated Machine Learning Platform—0
Crop and weed classification based on AutoML—0
AutoSpeech 2020: The Second Automated Machine Learning Challenge for Speech Classification—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—0
AutoML to Date and Beyond: Challenges and Opportunities—0
AutoBSS: An Efficient Algorithm for Block Stacking Style Search—0
Data Readiness Report—0
AutoADR: Automatic Model Design for Ad Relevance—0
Revisiting Neural Architecture Search—0
Evolutionary Architecture Search for Graph Neural NetworksCode0
Automatic deep learning for trend prediction in time series data—0
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)—0
AutoML for Multilayer Perceptron and FPGA Co-design—0
HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks—0
Can AutoML outperform humans? An evaluation on popular OpenML datasets using AutoML Benchmark—0
Auto-Classifier: A Robust Defect Detector Based on an AutoML HeadCode0
NASirt: AutoML based learning with instance-level complexity information—0
Automated Machine Learning -- a brief review at the end of the early years—0
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