What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Artificial intelligence, Machine learning, Bayesian optimization, Hyperparameter and Algorithm.
His study on Deep learning is often connected to Resource consumption as part of broader study in Artificial intelligence.
His research in the fields of Hyperparameter optimization, Stochastic gradient descent and Evolutionary algorithm overlaps with other disciplines such as Resource constraints.
Frank Hutter focuses mostly in the field of Bayesian optimization, narrowing it down to topics relating to Bayesian probability and, in certain cases, Initialization, Speedup, Overhead and Robustness.
His study on Hyperparameter also encompasses disciplines like
- Artificial neural network that intertwine with fields like Reinforcement learning,
- Feature selection most often made with reference to Data mining.
His Local search, Integer programming and Algorithm configuration study in the realm of Algorithm connects with subjects such as Function.
His most cited work include:
- Sequential model-based optimization for general algorithm configuration (1111 citations)
- SGDR: Stochastic Gradient Descent with Warm Restarts (963 citations)
- Decoupled Weight Decay Regularization. (797 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Machine learning, Hyperparameter, Algorithm and Hyperparameter optimization.
His research related to Deep learning, Bayesian optimization, Artificial neural network, Reinforcement learning and Benchmark might be considered part of Artificial intelligence.
His studies deal with areas such as Range, Variety and Data mining as well as Machine learning.
The Hyperparameter study combines topics in areas such as Process, Prior probability, Leverage and Statistical model.
His Algorithm study combines topics in areas such as Mathematical optimization and Set.
His work carried out in the field of Hyperparameter optimization brings together such families of science as Random forest, Generative model, Model selection and Random search.
He most often published in these fields:
- Artificial intelligence (53.14%)
- Machine learning (41.00%)
- Hyperparameter (24.27%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (53.14%)
- Machine learning (41.00%)
- Hyperparameter (24.27%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Machine learning, Hyperparameter, Deep learning and Benchmark.
When carried out as part of a general Artificial intelligence research project, his work on Artificial neural network and Range is frequently linked to work in Benchmarking and Meta learning, therefore connecting diverse disciplines of study.
His work on Hyperparameter optimization is typically connected to Focus as part of general Machine learning study, connecting several disciplines of science.
His studies deal with areas such as Process, Bayesian optimization, Leverage and Reinforcement learning as well as Hyperparameter.
His Deep learning research is multidisciplinary, incorporating perspectives in Feature, Regularization, Set, Pipeline and Mathematical optimization.
His studies in Benchmark integrate themes in fields like Regret and Factor.
Between 2019 and 2021, his most popular works were:
- Understanding and Robustifying Differentiable Architecture Search (86 citations)
- NAS-BENCH-1SHOT1: BENCHMARKING AND DISSECTING ONE-SHOT NEURAL ARCHITECTURE SEARCH (38 citations)
- The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Frank Hutter mostly deals with Artificial intelligence, Machine learning, Deep learning, Benchmark and Hyperparameter.
His Artificial intelligence research incorporates elements of Differentiable function and Optimization problem.
His work on Bayesian optimization as part of general Machine learning research is often related to Focus, thus linking different fields of science.
His biological study spans a wide range of topics, including Range, Convolutional neural network, Feature and Relaxation.
He has researched Range in several fields, including Regularization, Performance prediction, Data mining and Generalization.
His research integrates issues of Regret and Factor in his study of Benchmark.
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