The paradox resonates with the enigmatic, blackbox nature of deep learning; the abundance of data in fields such as computer vision and natural language processing and increasingly massive computational power ha
Deep learning (DL) is a powerful tool for mining features from data, which can theoretically avoid assumptions (e.g., linear events) constraining conventional interpolation methods. Motivated by this and inspired by image-to-image translation, we applied DL to irregularly and regularly missing data...
model.py: This file contain the code for creating the residual learning CNN model as well as the algorithm for conjugate-gradient on complex data. trn.py: This is the training code tstDemo.py: This is the testing code Contact The code is provided to support reproducible research. If the ...
At first sight, the task of training a deep learning algorithm to accurately identify a nonlinear map from a few – potentially very high-dimensional – input and output data pairs seems at best naive. Coming to our rescue, for many cases pertaining to the modeling of physical and biological...
Fig. 10: Growth in interest in using “deep learning for phase recovery” overtime is depicted by the number of publications and citations on Web of Science. The used search code is “TS = ((“phase recovery” OR “phase retrieval” OR “phase imaging” OR “holography” OR “phase...
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…
README Code of conduct BSD-3-Clause license Introduction DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems using deep learning. The goal of deepinv is to accelerate the development of deep learning based methods for imaging inverse problems, by combining popu...
In my previous blog, we delved into the concept of physics-informed DeepONet (PI-DeepONet) and explored why it is particularly suitable for operator learning, i.e., learning mappings from an input function to an output function. We also turned theory into code ...
Patel’s group is also embarking on exploring inverse reinforcement learning and optimal control. “While we can observe the motion, we’re still not sure why the cheetah does what it does,” he said. “Is the cat trying to conserve energy when running, increase maneuverabilit...
Moreover, the wide variety of existing deep learning architectures such as recurrent or convolutional neural networks makes it difficult to select an appropriate model for a specific use-case at hand and understand their respective requirements on the format and properties of the input data. To the...