Solving Boltzmann optimization problems with deep learningDecades of exponential scaling in high performance computing (HPC) efficiency is coming to an end. Transistor based logic in complementary metal-oxide s
3. Deep learning applications in RS with the small data problem 4. Practical recommendations for DL implementation strategies 5. Conclusions Declaration of Competing Interest Acknowledgments Data availability ReferencesShow full outline Cited by (90) Figures (7) Show 1 more figure Tables (1) Table ...
In this chapter, we focus on the reconstruction task; especially consider tomographic imaging problems with incomplete, corrupted, or noisy data; and demonstrate how deep learning methods enable us to solve such tasks in a unified manner. We present the basic ideas of these methods assuming paired...
The more recent deep learning methods try to enforce fairness protection with images through additional constraints, by removing sensitive features, and/or by learning fair representations. These strategies are often applied during training with the overarching objective of minimizing prediction gaps across ...
Benchmarking deep learning-based models on nanophotonic inverse design problemsTaigao Ma ¹, Mustafa Tobah ², 王浩竹 Haozhu Wang ³, 郭凌杰 L. Jay Guo ³ ¹ Department of Physics, The University of…
"Data is the new oil"is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having"The sexiest job ...
If you have any questions or suggestions, please join the conversation in our Discord server. The recommended way to get in touch with the developers is to open an issue on the issue tracker.About PyTorch library for solving imaging inverse problems using deep learning deepinv.github.io Top...
Deep Learning Workstations, Servers, and Laptops Thanks to all thesupporterswho made the book possible, with especial thanks to Pavel Dudrenov. Thanks also to all the contributors to theBugfinder Hall of Fame. Resources Michael Nielsen on Twitter ...
However, methods such as Deep Stacked Network (DSN) have some problems that increase its training time and memory usage. To deal with these problems, Fast DSN (FDSN) was proposed, where the modules are trained using an Extreme Learning Machine (ELM) variant. Nonetheless, to speed-up the ...
Learning I: the value of pre-training In the academic world of machine learning, there is little focus on obtaining datasets. Instead, it is even the opposite: in order to compare deep learning techniques with other approaches and ensure that one method outperforms others, the standard procedure...