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Explore advanced techniques in neural architecture optimization across multiple objectives and hardware devices, focusing on Pareto front profiling and hypernetwork-based solutions.
Explore advanced neural architecture search techniques for optimizing few-shot learning adaptation strategies, focusing on meta-training and meta-testing trade-offs in multi-domain contexts.
Explore how Mamba, a state space model, demonstrates in-context learning capabilities comparable to transformers while handling longer input sequences more efficiently in AI applications.
Discover a methodology for efficiently selecting and fine-tuning pretrained models through meta-learning, optimizing model selection and hyperparameter configurations for new datasets.
Explore groundbreaking research on OmniPred's innovative approach to using language models as universal regressors, demonstrating superior performance over traditional regression methods across diverse experiments.
Discover how AutoGluon 1.0 revolutionizes machine learning with Zeroshot-HPO and Dynamic Stacking, achieving superior performance in automated model training and ensemble stability.
Explore groundbreaking research on addressing bias in face recognition systems through neural architecture optimization, revealing how architectural choices impact fairness more than data preprocessing alone.
Discover how MotherNet hypernetwork architecture revolutionizes tabular classification by generating optimized neural networks through in-context learning, offering faster training and competitive performance against XGBoost.
Discover advanced hyperparameter optimization techniques through Population Based Training (PBT) and its multi-objective evolution, focusing on real-world applications in precision, fairness, and robustness.
Discover how CAAFE leverages large language models to enhance automated feature engineering in data science, improving model performance through context-aware feature generation and semantic understanding.
Delve into efficient training algorithms for Transformer models, exploring dynamic architectures, batch selection, and optimizers while examining their effectiveness against baseline methods.
Explore the complexities of hyperparameter optimization in Reinforcement Learning, examining AutoML tools and methods to better understand and tune RL algorithms for improved performance.
Discover advanced Bayesian optimization techniques for biomedical data analysis, focusing on multi-objective approaches with heuristic objectives in unsupervised bioinformatics workflows.
Delve into evolutionary meta-learning techniques for discovering black-box optimizers, exploring advanced concepts in evolution strategies and genetic algorithms through self-attention architectures.
Explore dynamic algorithm configuration and hyperparameter optimization approaches, comparing AutoML and black-box optimization methods for efficient, adaptive parameter tuning.
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