Explore the words cloud of the BeyondBlackbox project. It provides you a very rough idea of what is the project "BeyondBlackbox" about.
The following table provides information about the project.
Coordinator |
ALBERT-LUDWIGS-UNIVERSITAET FREIBURG
Organization address contact info |
Coordinator Country | Germany [DE] |
Total cost | 1˙495˙000 € |
EC max contribution | 1˙495˙000 € (100%) |
Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
Code Call | ERC-2016-STG |
Funding Scheme | ERC-STG |
Starting year | 2017 |
Duration (year-month-day) | from 2017-01-01 to 2021-12-31 |
Take a look of project's partnership.
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1 | ALBERT-LUDWIGS-UNIVERSITAET FREIBURG | DE (FREIBURG) | coordinator | 1˙495˙000.00 |
Deep neural networks (DNNs) have led to dramatic improvements of the state-of-the-art for many important classification problems, such as object recognition from images or speech recognition from audio data. However, DNNs are also notoriously dependent on the tuning of their hyperparameters. Since their manual tuning is time-consuming and requires expert knowledge, recent years have seen the rise of Bayesian optimization methods for automating this task. While these methods have had substantial successes, their treatment of DNN performance as a black box poses fundamental limitations, allowing manual tuning to be more effective for large and computationally expensive data sets: humans can (1) exploit prior knowledge and extrapolate performance from data subsets, (2) monitor the DNN's internal weight optimization by stochastic gradient descent over time, and (3) reactively change hyperparameters at runtime. We therefore propose to model DNN performance beyond a blackbox level and to use these models to develop for the first time:
1. Next-generation Bayesian optimization methods that exploit data-driven priors to optimize performance orders of magnitude faster than currently possible; 2. Graybox Bayesian optimization methods that have access to -- and exploit -- performance and state information of algorithm runs over time; and 3. Hyperparameter control strategies that learn across different datasets to adapt hyperparameters reactively to the characteristics of any given situation.
DNNs play into our project in two ways. First, in all our methods we will use (Bayesian) DNNs to model and exploit the large amounts of performance data we will collect on various datasets. Second, our application goal is to optimize and control DNN hyperparameters far better than human experts and to obtain:
4. Computationally inexpensive auto-tuned deep neural networks, even for large datasets, enabling the widespread use of deep learning by non-experts.
year | authors and title | journal | last update |
---|---|---|---|
2018 |
Matthias Feurer
Katharina Eggensperger
Stefan Falkner
Marius Lindauer
Frank Hutter Practical Automated Machine Learning for the AutoML Challenge 2018 published pages: , ISSN: , DOI: |
ICML 2018 | 2020-04-15 |
2019 |
Jör Franke, Jörg
Gregor Köhler
Noor Awad
Frank Hutter Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control published pages: , ISSN: , DOI: |
NeurIPS 2019 | 2020-04-15 |
2018 |
Matthias Feurer
Frank Hutter Towards Further Automation in AutoML published pages: , ISSN: , DOI: |
ICML 2018 | 2020-04-15 |
2019 |
Hutter, Frank
Elsken, Thomas
Metzen, Jan Hendrik Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution - Published as a conference paper at ICLR 2019 published pages: , ISSN: , DOI: |
2019-10-03 | |
2017 |
Bischl, Bernd; Casalicchio, Giuseppe; Feurer, Matthias; Hutter, Frank; Lang, Michel; Mantovani, Rafael G.; van Rijn, Jan N.; Vanschoren, Joaquin OpenML Benchmarking Suites and the OpenML100 published pages: , ISSN: , DOI: |
1 | 2019-08-29 |
2019 |
Runge, Frederic; Stoll, Danny; Falkner, Stefan; Hutter, Frank Learning to Design RNA published pages: , ISSN: , DOI: |
ICLR 2019 5 | 2019-08-29 |
2018 |
Chrabaszcz, Patryk; Loshchilov, Ilya; Hutter, Frank Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari published pages: , ISSN: , DOI: |
IJCAI 2018 1 | 2019-08-29 |
2019 |
Hutter, Frank, Kotthoff, Lars, Vanschoren, Joaquin (Eds.) Automated Machine Learning: Methods, Systems, Challenges published pages: , ISSN: , DOI: |
The Springer Series on Challenges in Machine Learning | 2019-08-29 |
2018 |
Zela, Arber; Klein, Aaron; Falkner, Stefan; Hutter, Frank Towards Automated Deep Learning: Efficient Joint Neural Architecture and Hyperparameter Search published pages: , ISSN: , DOI: |
AutoML Workshop 1 | 2019-08-29 |
2018 |
Falkner, Stefan; Klein, Aaron; Hutter, Frank BOHB: Robust and Efficient Hyperparameter Optimization at Scale published pages: , ISSN: , DOI: |
ICML 2018 5 | 2019-08-29 |
2019 |
Loshchilov, Ilya; Hutter, Frank Decoupled Weight Decay Regularization published pages: , ISSN: , DOI: |
ICLR 2019 5 | 2019-08-29 |
2017 |
Chrabaszcz, Patryk; Loshchilov, Ilya; Hutter, Frank A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets published pages: , ISSN: , DOI: |
arXiv 5 | 2019-08-29 |
2019 |
Ying, Chris; Klein, Aaron; Real, Esteban; Christiansen, Eric; Murphy, Kevin; Hutter, Frank NAS-Bench-101: Towards Reproducible Neural Architecture Search published pages: , ISSN: , DOI: |
ICML 2019 5 | 2019-08-29 |
2018 |
Wilson, James T.; Hutter, Frank; Deisenroth, Marc Peter Maximizing acquisition functions for Bayesian optimization published pages: , ISSN: , DOI: |
NeurIPS 2018 1 | 2019-08-29 |
2019 |
Elsken, Thomas; Metzen, Jan Hendrik; Hutter, Frank Neural Architecture Search: A Survey published pages: , ISSN: 1533-7928, DOI: |
JMLR 5 | 2019-08-29 |
2017 |
Aaron Klein, Stefan Falkner, Jost Tobias Springenberg, Frank Hutter Learning curve predictionwith bayesian neural networks published pages: , ISSN: , DOI: |
proceedings of ICLR | 2019-06-13 |
2017 |
Aaron Klein, Stefan Falkner, Numair Mansur, Frank Hutter RoBO: A Flexible and Robust Bayesian OptimizationFramework in Python published pages: , ISSN: , DOI: |
Proceedings of BayesOpt 2017 | 2019-06-13 |
2017 |
Jan N. van Rijn, Frank Hutter An Empirical Study of Hyperparameter Importance Across Datasets published pages: , ISSN: , DOI: |
proceedings of AutoML | 2019-06-13 |
2017 |
Klaus Greff, Aaron Klein, Martin Chovanec, Frank Hutter, Jürgen Schmidhuber The Sacred Infrastructure for ComputationalResearch published pages: , ISSN: , DOI: |
proceedings of the 15th python in science conference | 2019-06-13 |
2017 |
James T. Wilson, Riccardo Moriconi, Frank Hutter, Marc Peter Deisenroth The reparameterization trick for acquisition functions published pages: , ISSN: , DOI: |
Proceedings of BayesOpt 2017 | 2019-06-13 |
2017 |
Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter Fast Bayesian hyperparameter optimization on large datasets published pages: , ISSN: 1935-7524, DOI: |
Electronic Journal of Statistics | 2019-06-13 |
2018 |
Jan N. van Rijn, Frank Hutter Hyperparameter Importance Across Datasets published pages: , ISSN: , DOI: |
Proceedings of KDD 2018 | 2019-06-13 |
2017 |
Stefan Falkner, Aaron Klein, Frank Hutter Combining Hyperband and Bayesian Optimization published pages: , ISSN: , DOI: |
Proceedings of BayesOpt 2017 | 2019-06-13 |
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The information about "BEYONDBLACKBOX" are provided by the European Opendata Portal: CORDIS opendata.