scholarly journals Machine Learning of Coq Proof Guidance: First Experiments

10.29007/lmmg ◽  
2018 ◽  
Author(s):  
Cezary Kaliszyk ◽  
Lionel Mamane ◽  
Josef Urban
Keyword(s):  
Machine Learning ◽  
Evaluation Method ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Comparable Performance

We report the results of the first experiments with learning proof dependencies from the formalizations done with the Coq system. We explain the process of obtaining the dependencies from the Coq proofs, the characterization of formulas that is used for the learning, and the evaluation method. Various machine learning methods are compared on a dataset of 5021 toplevel Coq proofs coming from the CoRN repository. The best resulting method covers on average 75% of the needed proof dependencies among the first 100 predictions, which is a comparable performance of such initial experiments on other large-theory corpora.

scholarly journals Hyperparameters optimisation for time varying signals

2019 ◽  
Vol XXII (1) ◽  
pp. 196-199
Author(s):  
Rogobete M.
Keyword(s):  
Machine Learning ◽  
General Class ◽  
Performance Model ◽  
Specific Class ◽  
Time Varying ◽  
Learning Methods ◽  
Amount Of Information ◽  
Machine Learning Methods ◽  
Validation Set

For the most machine learning methods, for cyclo-stationary or even stochastic signals, the performance depends critically on hyperparameters. Moreover, the tuning of more hyperparameters based on the feedback of the performance model will leak an increasingly significant amount of information about the validation set into the model. Therefore, we propose in this research two classes of hyperparameters, a general class that makes the characterization of general signal curve and the second, a specific class that define special parameters connected to the phenomena type (e.g. sensor type).

Recent Development of Computational Predicting Bioluminescent Proteins

2020 ◽  
Vol 25 (40) ◽  
pp. 4264-4273 ◽  
Author(s):  
Dan Zhang ◽  
Zheng-Xing Guan ◽  
Zi-Mei Zhang ◽  
Shi-Hao Li ◽  
Fu-Ying Dao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Light Emission ◽  
Biotechnological Application ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Technological Advances ◽  
Living Organisms ◽  
Role Of Light

Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized to be essential for many medical, commercial and other general technological advances. Therefore, the prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence and promote the development of application of bioluminescence. Since the experimental methods are money and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and accurate prediction of BLPs by combining their sequences information with machine learning methods. In this review, we summarized and compared the application of machine learning methods in the prediction of BLPs from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.

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