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Invert to Learn to Invert [article]

Patrick Putzky, Max Welling
2019 arXiv   pre-print
Iterative learning to infer approaches have become popular solvers for inverse problems.  ...  In this work, we propose an iterative inverse model with constant memory that relies on invertible networks to avoid storing intermediate activations.  ...  Learn to Invert Many recent approached to solving inverse problems focus on models that learn to invert the forward problem by mimicking the behaviour of an iterative optimization algorithm.  ... 
arXiv:1911.10914v1 fatcat:tyxhrfx2xfhbfo3pjhec2nhrri

RADYNVERSION: Learning to Invert a Solar Flare Atmosphere with Invertible Neural Networks

Christopher M. J. Osborne, John A. Armstrong, Lyndsay Fletcher
2019 Astrophysical Journal  
Here we present a novel deep learning approach, an invertible neural network, to understanding the chromospheric physics of a flaring solar atmosphere via the inversion of observed solar line profiles  ...  The inverted atmospheres obtained from observations provide physical information on the electron number density, temperature and bulk velocity flow of the plasma throughout the solar atmosphere ranging  ...  We design and train an invertible neural network (INN; similar to that introduced in Dinh et al. 2016; Ardizzone et al. 2018) to learn the output Hα and Ca II 8542 Å spectral lines corresponding to many  ... 
doi:10.3847/1538-4357/ab07b4 fatcat:mtb6fz7gwvd4ff57edesnb4iv4

Composite Learning Control With Application to Inverted Pendulums [article]

Yongping Pan, Lin Pan, Haoyong Yu
2015 arXiv   pre-print
The effectiveness of the proposed MRCLC has been verified by the application to an inverted pendulum control problem.  ...  In the composite learning, an integral during a moving-time window is utilized to construct a prediction error, a linear filter is applied to alleviate the derivation of plant states, and both the tracking  ...  The proposed approach has also been applied to an inverted pendulum model, where superior control and learning performances have been demonstrated compared with the conventional MRAC and the MRCAC with  ... 
arXiv:1507.07844v2 fatcat:6ew47uweozf6tkz2wiet4aar2i

Learning to Invert: Signal Recovery via Deep Convolutional Networks [article]

Ali Mousavi, Richard G. Baraniuk
2017 arXiv   pre-print
In this paper, we attack both of these challenges head-on by developing a new signal recovery framework we call DeepInverse that learns the inverse transformation from measurement vectors to signals using  ...  Second, current high-performance recovery algorithms are slow to converge, which limits CS to either non-real-time applications or scenarios where massive back-end computing is available.  ...  By training a DCN, we learn a nonlinear mapping from a signal proxyx to its original signal x.  ... 
arXiv:1701.03891v1 fatcat:6xqtcmtbjbep5dpodvnbtpngde

Inverting Gradients – How easy is it to break privacy in federated learning? [article]

Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, Michael Moeller
2020 arXiv   pre-print
The idea of federated learning is to collaboratively train a neural network on a server.  ...  This protocol has been designed not only to train neural networks data-efficiently, but also to provide privacy benefits for users, as their input data remains on device and only parameter gradients are  ...  be (mostly) invertible [13] .  ... 
arXiv:2003.14053v2 fatcat:cuzoihte3vfnffzzl7wivjttnq

Inverted Classroom to Develop Self-regulated Learning in University Students in Times of Pandemic

Juan Carlos Chumacero Calle
2021 Revista GEINTEC  
the competence of learning to learn.  ...  Universities will need to pay close attention to having educators trained and equipped with digital technology for a smooth teaching-learning process.  ...  in order to prioritize the construction of inverted learning, meaningful learning, experiential learning, blended learning and student-centered learning (Landa, 2017) .  ... 
doi:10.47059/revistageintec.v11i3.1971 fatcat:qzkouvmfercc7keyzpoeosjmx4

The online inverted classroom model (oICM). A blueprint to adapt the inverted classroom to an online learning setting in medical and health education

Daniel Tolks, Bernd FM Romeike, Jan Ehlers, Sebastian Kuhn, Christin Kleinsorgen, Johanna Huber, Martin R Fischer, Christoph Bohne, Inga Hege
2020 MedEdPublish  
The proposed online Inverted Classroom Model (oICM) includes the following phases: (1) pre-phase, (2) self-learning-phase, (3) Synchronous online face-to-face phase, (4) transfer-phase, (5) evaluation.  ...  The classical concept of the inverted classroom was modified to meet the special needs of online settings.  ...  The traditional Inverted Classroom Model According to several meta-analyses, the inverted or flipped classroom method (ICM) showed positive effects regarding engagement, motivation, overall satisfaction  ... 
doi:10.15694/mep.2020.000113.1 fatcat:bdlcxnsonfcbtoi6mr5tujt2de

Learning to control an inverted pendulum using neural networks

C.W. Anderson
1989 Control Systems Magazine  
An inverted pendulum is simulated as a control task with the goal of learning to balance the pendulum with no a priori knowledge of the dynamics.  ...  In contrast to other applications of neural networks to the inverted pendulum task, performance feedback is assumed to be unavailable on each step, appearing only as a failure signal when the pendulum  ...  Barto et al. [5] demonstrated a quantization of the state space of the inverted pendulum with which singlelayer networks could learn to balance the pendulum.  ... 
doi:10.1109/37.24809 fatcat:szc2isnd45csvbtdri7zddl2e4

To swing up an inverted Pendulum using stochastic real-valued Reinforcement Learning [chapter]

A. Standfuss, R. Eckmiller
1994 ICANN '94  
We proposed the hypothesis that the net learned a strategy, which had to be inverted above the horizontal.  ...  It is thus a challenge for reinforcement learning over time (Sutton, 1988 Fig. 1 schema of an inverted pendulum The problem to swing up an inverted pendulum means to move the pole from hanging upside  ... 
doi:10.1007/978-1-4471-2097-1_154 fatcat:iwrqxfl3evee5hricn4xkcafuy

Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution [article]

Huiqi Deng, Na Zou, Weifu Chen, Guocan Feng, Mengnan Du, Xia Hu
2021 arXiv   pre-print
In addition, we propose a Mutual Information Preserving Inverse Network, termed MIP-IN, in which the parameters of each layer are recursively trained to learn how to invert.  ...  The basic idea is to learn a source signal by back-propagation such that the mutual information between input and output should be as much as possible preserved in the mutual information between input  ...  MIP-IN recursively retrain the parameters of each layer to learn how to invert and then adopt forward relu to adapt the global framework to each specific input.  ... 
arXiv:2104.06629v1 fatcat:hyc7jig54ralbadtj3oethpy3m

DEEP LEARNING APPROACH TO FACE CONDITIONING USING INVERTIBLE CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS (ICGAN)

Utpal Srivastav, Vikas Thada, Amit Kumar, Maulik Garach, Adit Paliwal
2020 International Journal of Innovative Research in Computer Science & Technology  
The preparation strategy for G is to expand the likelihood of D committing an error. This system compares to a minimax two-player game.  ...  In the space of discretionary capacities G and D, an interesting arrangement exists, with G recuperating the preparation information conveyance and D equivalent to 1/2 all over the place.  ...  The minimax game in Eq. 1 can now be reformulated as: Deep Learning Approach To Face Conditioning Using Invertible Conditional Generative Adversarial Networks (ICGAN) Theorem 1.  ... 
doi:10.21276/ijircst.2020.8.3.24 fatcat:kvru5b4q2vezjpkuj44b75trle

Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization [article]

Yongfa Ling, Wenbo Guan, Qiang Ruan, Heping Song, Yuping Lai
2021 arXiv   pre-print
The finite invert Beta-Liouville mixture model (IBLMM) has recently gained some attention due to its positive data modeling capability.  ...  With the recently proposed extended variational inference (EVI) framework, a new function is proposed to replace the original variational object function in order to avoid intractable moment computation  ...  Variational Learning for the Inverted Beta-Liouville Mixture Model and Its Application to Text Categorization  ... 
arXiv:2112.14375v1 fatcat:25r5oiggufgdhl2sgsgdvrpux4

Applying Inverted Classroom to Software Engineering Education

Eun Choi
2013 International Journal of e-Education, e-Business, e-Management and e-Learning  
Lecture time was devoted to active learning activities that can be done such as design studio and game storming.  ...  For case study inverted classroom concept was applied to introductory software engineering course, and measured by controlled experiments.  ...  Inverted classroom replaced a passive learning to a variety of active group activities was done in time to meet professors and class mates.  ... 
doi:10.7763/ijeeee.2013.v3.205 fatcat:xzwjnrslw5gvbh7gpefsokpb2i

The relation of dental students' learning styles to their satisfaction with traditional and inverted classroom models

Rong Wang, Chuanyong Liu
2019 BMC Medical Education  
This study aimed to determine students' learning styles and investigate the relationship between learning style and satisfaction with different instruction approaches and components of the ICM.  ...  The authors' medical school has adopted an inverted classroom model (ICM) for physiology classes.  ...  Acknowledgements We would like to thank Dr. Liu J, a statistician from the School of Public Health, Shandong University, who has provided support with the statistics. Funding None.  ... 
doi:10.1186/s12909-019-1749-x pmid:31438946 pmcid:PMC6704638 fatcat:hlmmokardvah5nwvhdotz7lxrm

Evaluation of Contribution of PV Array and Inverter Configurations to Rooftop PV System Energy Yield Using Machine Learning Techniques

Ngoc Thien Le, Watit Benjapolakul
2019 Energies  
In this study, the rooftop PV systems from many high-volume installed PV systems countries and regions were collected to evaluate the lifetime energy yield of these systems based on machine learning techniques  ...  Then, we obtained an association between the lifetime energy yield and technical configuration details of PV such as rated solar panel power, number of panels, rated inverter power, and number of inverters  ...  Applied Machine Learning Techniques Machine learning techniques are based on the power of a computer to build and train models according to the input datasets.  ... 
doi:10.3390/en12163158 fatcat:cxcdzpydzvaehfnbcwgyvnjq4y
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