10 skills found
geomstats / GeomstatsComputations and statistics on manifolds with geometric structures.
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liborty / RstatsStatistics, Information Measures, Linear Algebra, Cholesky Matrix Decomposition, Mahalanobis Distance, Householder QR Decomposition, Clifford Algebra, Multidimensional Data Analysis, Geometric Median, Hulls, Machine Learning, multithreading implementation...
morphomatics / MorphomaticsGeometric morphometrics in non-Euclidean shape spaces.
leichenNUSJ / AAMandDCMThis project is to implement “Attention-Adaptive and Deformable Convolutional Modules for Dynamic Scene Deblurring(with ERCNN)” . To run this project you need to setup the environment, download the dataset, and then you can train and test the network models. ## Prerequiste The project is tested on Ubuntu 16.04, GPU Titan XP. Note that one GPU is required to run the code. Otherwise, you have to modify code a little bit for using CPU. If using CPU for training, it may too slow. So I recommend you using GPU strong enough and about 12G RAM. ## Dependencies Python 3.5 or 3.6 are recommended. ``` tqdm==4.19.9 numpy==1.17.3 torch==1.0.0 Pillow==6.1.0 torchvision==0.2.2 ``` ## Environment I recommend using ```virtualenv``` for making an environment. If you using ```virtualenv```, ## Dataset I use GOPRO dataset for training and testing. __Download links__: [GOPRO_Large](https://drive.google.com/file/d/1H0PIXvJH4c40pk7ou6nAwoxuR4Qh_Sa2/view?usp=sharing) | Statistics | Training | Test | Total | | ----------- | -------- | ---- | ----- | | sequences | 22 | 11 | 33 | | image pairs | 2103 | 1111 | 3214 | After downloading dataset successfully, you need to put images in right folders. By default, you should have images on dataset/train and dataset/valid folders. ## Demo ## Training Run the following command ``` python demo_train.py ('data_dir' is needed before running ) ``` For training other models, you should uncommend lines in scripts/train.sh file. I used ADAM optimizer with a mini-batch size 16 for training. The learning rate is 1e-4. Total training takes 600 epochs to converge. To prevent our network from overfitting, several data augmentation techniques are involved. In terms of geometric transformations, patches are randomly rotated by 90, 180, and 270 degrees. To take image degradations into account, saturation in HSV colorspace is multiplied by a random number within [0.8, 1.2].  ## Testing Run the following command ``` python demo_test.py ('data_dir' is needed before running ) ``` ## pretrained models if you need the pretrained models,please contact us by chenleinj@njust.edu.cn ## Acknowledge Our code is based on Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring [MSCNN](http://openaccess.thecvf.com/content_cvpr_2017/papers/Nah_Deep_Multi-Scale_Convolutional_CVPR_2017_paper.pdf), which is a nice work for dynamic scene deblurring .
akkoyun / StatisticalDescriptive statistics such as mean, geometric mean, maximum, minimum, sample standard deviation and standard error, median, mode, coefficient of variation, and linear regression. Written for various data type arrays
reduce-algebra / Dimsymdimsym: Geometric and algebraic techniques for differential equations (with modelling applications); Symmetry Determination and Linear Differential Equation Package, mirrored from https://www.latrobe.edu.au/mathematics-and-statistics/research/geometric-and-algebraic-techniques-for-differential-equations/dimsym/
spolavar / L Measure3D Neuronal morphology analytical tool for deriving geometrical and topological statistics of digital neuronal reconstructions
tdegeus / GooseEYEGeometrical statistics
anishacharya / BGMD AISTATS 2022Geometric median (GM) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 0.5. However, its computational complexity makes it infeasible for robustifying stochastic gradient descent (SGD) for high-dimensional optimization problems. In this paper, we show that by applying Gm to only a judiciously chosen block of coordinates at a time and using a memory mechanism, one can retain the breakdown point of 0.5 for smooth non-convex problems, with non-asymptotic convergence rates comparable to the SGD with GM.
