AutoML
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Benchmarks
Most implemented
EfficientDet: Scalable and Efficient Object Detection
EfficientNetV2: Smaller Models and Faster Training
MixConv: Mixed Depthwise Convolutional Kernels
Auto-Keras: An Efficient Neural Architecture Search System
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Once-for-All: Train One Network and Specialize it for Efficient Deployment
Papers
Imbalanced Regression Pipeline Recommendation
Imbalanced problems are prevalent in various real-world scenarios and are extensively explored in classification tasks. However, they also present challenges for regression tasks due to the rarity of certain target value…
AutoMLMeta-LearningregressionOptimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage
Extended Stability Runge-Kutta (ESRK) methods are crucial for solving large-scale computational problems in science and engineering, including weather forecasting, aerodynamic analysis, and complex biological modelling. …
AutoMLComputational EfficiencyHeuristic SearchReinforcement Learning (RL)+1Multimodal Representation Learning and Fusion
Multi-modal learning is a fast growing area in artificial intelligence. It tries to help machines understand complex things by combining information from different sources, like images, text, and audio. By using the stre…
AutoMLRepresentation Learningspeech-recognitionSpeech RecognitionOvertuning in Hyperparameter Optimization
Hyperparameter optimization (HPO) aims to identify an optimal hyperparameter configuration (HPC) such that the resulting model generalizes well to unseen data. As the expected generalization error cannot be optimized dir…
AutoMLHyperparameter OptimizationFrom Tiny Machine Learning to Tiny Deep Learning: A Survey
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). Whil…
AutoMLModel OptimizationNeural Architecture SearchQuantization+1CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction
Caco-2 permeability serves as a critical in vitro indicator for predicting the oral absorption of drug candidates during early-stage drug discovery. To enhance the accuracy and efficiency of computational predictions, we…
AutoMLDiversityDrug DiscoveryFeature Importance+1