AutoML

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

9 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

3 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 295 papers with code (641 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• EfficientDet: Scalable and Efficient Object Detection
  20 Nov 2019 64 repositories listed Syntology ran 11 of 70 samples · 59 unverified · 3 pointer-only (licence)
  Model efficiency has become increasingly important in computer vision.
• EfficientNetV2: Smaller Models and Faster Training
  1 Apr 2021 26 repositories listed Syntology ran 41 of 79 samples · 38 unverified · 10 pointer-only (licence)
  By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.
• MixConv: Mixed Depthwise Convolutional Kernels
  22 Jul 2019 13 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  In this paper, we systematically study the impact of different kernel sizes, and observe that combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency.
• Auto-Keras: An Efficient Neural Architecture Search System
  27 Jun 2018 13 repositories listed Syntology ran 3 of 6 samples · 3 unverified
  In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.
• AMC: AutoML for Model Compression and Acceleration on Mobile Devices
  10 Feb 2018 12 repositories listed Syntology ran 1 of 2 samples · 1 unverified · 1 pointer-only (licence)
  Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets.
• Once-for-All: Train One Network and Specialize it for Efficient Deployment
  26 Aug 2019 10 repositories listed Syntology ran 4 of 34 samples · 30 unverified
  On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4.
• The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development
  22 May 2019 8 repositories listed
  To address these problems, we introduce the Machine Learning Bazaar, a new framework for developing machine learning and automated machine learning software systems.
• TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
  5 Jul 2022 7 repositories listed Syntology ran 1 of 4 samples · 3 unverified · 1 pointer-only (licence)
  We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification…
• AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
  13 Mar 2020 7 repositories listed Syntology ran 0 of 11 samples · 11 unverified
  We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file.
• Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
  9 Feb 2024 5 repositories listed
  Machine-learning from a disparate set of tables, a data lake, requires assembling features by merging and aggregating tables.
• MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
  5 Dec 2020 5 repositories listed
  Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only.
• Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
  8 Jul 2020 4 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success.
• SAFE ML: Surrogate Assisted Feature Extraction for Model Learning
  28 Feb 2019 4 repositories listed
  Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift.
• Benchmarking Automatic Machine Learning Frameworks
  17 Aug 2018 4 repositories listed
  AutoML serves as the bridge between varying levels of expertise when designing machine learning systems and expedites the data science process.
• LEMUR Neural Network Dataset: Towards Seamless AutoML
  14 Apr 2025 3 repositories listed
  We introduce LEMUR, an open source dataset of neural network models with well-structured code for diverse architectures across tasks such as object detection, image classification, segmentation, and natural language…
• TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
  6 Nov 2023 3 repositories listed Syntology ran 8 of 8 samples · 0 unverified
  We introduce TabRepo, a new dataset of tabular model evaluations and predictions.
• AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting
  10 Aug 2023 3 repositories listed
  We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting.
• AutoML Two-Sample Test
  17 Jun 2022 3 repositories listed Syntology ran 10 of 11 samples · 1 unverified · 2 pointer-only (licence)
  Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts.
• MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification
  27 Oct 2021 3 repositories listed
  We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D.
• LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
  3 Sep 2021 3 repositories listed
  We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that this ecosystem has for AutoML solutions.
• VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
  19 Jul 2021 3 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and…
• Bilinear Scoring Function Search for Knowledge Graph Learning
  1 Jul 2021 3 repositories listed
  We first set up a search space for AutoBLM by analyzing existing scoring functions.
• Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
  22 Apr 2021 3 repositories listed
  The scoring function, which measures the plausibility of triplets in knowledge graphs (KGs), is the key to ensure the excellent performance of KG embedding, and its design is also an important problem in the literature.
• Rethinking Neural Operations for Diverse Tasks
  29 Mar 2021 3 repositories listed Syntology ran 18 of 34 samples · 16 unverified
  An important goal of AutoML is to automate-away the design of neural networks on new tasks in under-explored domains.
• Robust and Accurate Object Detection via Adversarial Learning
  23 Mar 2021 3 repositories listed
  Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection.
• MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
  28 Oct 2020 3 repositories listed Syntology ran 2 of 10 samples · 8 unverified
  We present MedMNIST, a collection of 10 pre-processed medical open datasets.
• Cardea: An Open Automated Machine Learning Framework for Electronic Health Records
  1 Oct 2020 3 repositories listed
  An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018.
• GAMA: a General Automated Machine learning Assistant
  9 Jul 2020 3 repositories listed Syntology ran 0 of 17 samples · 17 unverified
  The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate…
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
  15 Jun 2020 3 repositories listed Syntology ran 1 of 8 samples · 7 unverified
  Inspired by the recent success of AutoML in deep compression, we introduce AutoML to GAN compression and develop an AutoGAN-Distiller (AGD) framework.
• Model-based Asynchronous Hyperparameter and Neural Architecture Search
  24 Mar 2020 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified
  We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization.

Syntology lines on 17 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.