10.4(top 2%)
impact factor
98(top 100%)
papers
2.9K(top 20%)
citations
23(top 20%)
h-index
10.8(top 2%)
extended IF
109
all documents
3.2K
doc citations
53(top 20%)
g-index
Top Articles
| # | Title | Journal | Year | Citations |
|---|---|---|---|---|
| 1 | Automated Machine Learning | The Springer Series on Challenges in Machine Learning | 2019 | 633 |
| 2 | Hyperparameter Optimization | The Springer Series on Challenges in Machine Learning | 2019 | 434 |
| 3 | Neural Architecture Search | The Springer Series on Challenges in Machine Learning | 2019 | 259 |
| 4 | Auto-sklearn: Efficient and Robust Automated Machine Learning | The Springer Series on Challenges in Machine Learning | 2019 | 169 |
| 5 | Meta-Learning | The Springer Series on Challenges in Machine Learning | 2019 | 168 |
| 6 | TPOT: A Tree-Based Pipeline Optimization Tool for Automating Machine Learning | The Springer Series on Challenges in Machine Learning | 2019 | 149 |
| 7 | Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA | The Springer Series on Challenges in Machine Learning | 2019 | 124 |
| 8 | Adversarial Attacks and Defences Competition | The Springer Series on Challenges in Machine Learning | 2018 | 123 |
| 9 | The Second Conversational Intelligence Challenge (ConvAI2) | The Springer Series on Challenges in Machine Learning | 2020 | 122 |
| 10 | Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges | The Springer Series on Challenges in Machine Learning | 2018 | 92 |
| 11 | Correction to: Neural Architecture Search | The Springer Series on Challenges in Machine Learning | 2019 | 80 |
| 12 | Considerations for Evaluation and Generalization in Interpretable Machine Learning | The Springer Series on Challenges in Machine Learning | 2018 | 61 |
| 13 | Explainable and Interpretable Models in Computer Vision and Machine Learning | The Springer Series on Challenges in Machine Learning | 2018 | 50 |
| 14 | Analysis of the AutoML Challenge Series 2015–2018 | The Springer Series on Challenges in Machine Learning | 2019 | 42 |
| 15 | A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives | The Springer Series on Challenges in Machine Learning | 2017 | 42 |
| 16 | Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening | The Springer Series on Challenges in Machine Learning | 2018 | 37 |
| 17 | Learning to Run Challenge Solutions: Adapting Reinforcement Learning Methods for Neuromusculoskeletal Environments | The Springer Series on Challenges in Machine Learning | 2018 | 37 |
| 18 | Learning Functional Causal Models with Generative Neural Networks | The Springer Series on Challenges in Machine Learning | 2018 | 36 |
| 19 | Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey | The Springer Series on Challenges in Machine Learning | 2017 | 31 |
| 20 | Sign Language Recognition Using Sub-units | The Springer Series on Challenges in Machine Learning | 2017 | 31 |
| 21 | A Brief Review of Image Denoising Algorithms and Beyond | The Springer Series on Challenges in Machine Learning | 2019 | 30 |
| 22 | Towards Automatically-Tuned Deep Neural Networks | The Springer Series on Challenges in Machine Learning | 2019 | 29 |
| 23 | Hyperopt-Sklearn | The Springer Series on Challenges in Machine Learning | 2019 | 28 |
| 24 | The Tracking Machine Learning Challenge: Accuracy Phase | The Springer Series on Challenges in Machine Learning | 2020 | 23 |
| 25 | Aifred Health, a Deep Learning Powered Clinical Decision Support System for Mental Health | The Springer Series on Challenges in Machine Learning | 2018 | 22 |
| 26 | FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks | The Springer Series on Challenges in Machine Learning | 2019 | 22 |
| 27 | Challenges in Multi-modal Gesture Recognition | The Springer Series on Challenges in Machine Learning | 2017 | 22 |
| 28 | Learning to Run Challenge: Synthesizing Physiologically Accurate Motion Using Deep Reinforcement Learning | The Springer Series on Challenges in Machine Learning | 2018 | 21 |
| 29 | Keep It Simple and Sparse: Real-Time Action Recognition | The Springer Series on Challenges in Machine Learning | 2017 | 19 |
| 30 | The Automatic Statistician | The Springer Series on Challenges in Machine Learning | 2019 | 16 |
| 31 | One-Shot Learning Gesture Recognition from RGB-D Data Using Bag of Features | The Springer Series on Challenges in Machine Learning | 2017 | 16 |
| 32 | Artificial Intelligence for Prosthetics: Challenge Solutions | The Springer Series on Challenges in Machine Learning | 2020 | 14 |
| 33 | Gesture Recognition | The Springer Series on Challenges in Machine Learning | 2017 | 13 |
| 34 | One-Shot-Learning Gesture Recognition Using HOG-HOF Features | The Springer Series on Challenges in Machine Learning | 2017 | 11 |
| 35 | The Gesture Recognition Toolkit | The Springer Series on Challenges in Machine Learning | 2017 | 11 |
| 36 | Conditional Distribution Variability Measures for Causality Detection | The Springer Series on Challenges in Machine Learning | 2019 | 11 |
| 37 | Road Layout Understanding by Generative Adversarial Inpainting | The Springer Series on Challenges in Machine Learning | 2019 | 11 |
| 38 | U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting | The Springer Series on Challenges in Machine Learning | 2019 | 9 |
| 39 | Human Gesture Recognition on Product Manifolds | The Springer Series on Challenges in Machine Learning | 2017 | 9 |
| 40 | Adversarial Vision Challenge | The Springer Series on Challenges in Machine Learning | 2020 | 9 |
| 41 | Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions | The Springer Series on Challenges in Machine Learning | 2018 | 8 |
| 42 | Lost in Conversation: A Conversational Agent Based on the Transformer and Transfer Learning | The Springer Series on Challenges in Machine Learning | 2020 | 8 |
| 43 | Multi-layered Gesture Recognition with Kinect | The Springer Series on Challenges in Machine Learning | 2017 | 8 |
| 44 | Multimodal Gesture Recognition via Multiple Hypotheses Rescoring | The Springer Series on Challenges in Machine Learning | 2017 | 8 |
| 45 | Discriminative Hierarchical Part-Based Models for Human Parsing and Action Recognition | The Springer Series on Challenges in Machine Learning | 2017 | 8 |
| 46 | The First Conversational Intelligence Challenge | The Springer Series on Challenges in Machine Learning | 2018 | 7 |
| 47 | Neural Connectomics Challenge | The Springer Series on Challenges in Machine Learning | 2017 | 6 |
| 48 | Learning Interpretable Rules for Multi-Label Classification | The Springer Series on Challenges in Machine Learning | 2018 | 6 |
| 49 | Structuring Neural Networks for More Explainable Predictions | The Springer Series on Challenges in Machine Learning | 2018 | 6 |
| 50 | AutoML @ NeurIPS 2018 Challenge: Design and Results | The Springer Series on Challenges in Machine Learning | 2020 | 6 |