23 skills found
facebookresearch / FBTT EmbeddingThis is a Tensor Train based compression library to compress sparse embedding tables used in large-scale machine learning models such as recommendation and natural language processing. We showed this library can reduce the total model size by up to 100x in Facebook’s open sourced DLRM model while achieving same model quality. Our implementation is faster than the state-of-the-art implementations. Existing the state-of-the-art library also decompresses the whole embedding tables on the fly therefore they do not provide memory reduction during runtime of the training. Our library decompresses only the requested rows therefore can provide 10,000 times memory footprint reduction per embedding table. The library also includes a software cache to store a portion of the entries in the table in decompressed format for faster lookup and process.
cp2k / DbcsrDBCSR: Distributed Block Compressed Sparse Row matrix library
uestla / Sparse MatrixC++ implementation of sparse matrix using CRS (Compressed Row Storage) format
CRAFT-THU / RoDeA Row Decomposition-based Approach for Sparse Matrix Multiplication on GPUs
mygulamali / SparseMatrixA C++ class to represent a sparse matrix in compressed row format. Useful for FEM codes.
mikolalysenko / Csr MatrixCompressed sparse row matrix for node.js
notini / Csr FormatterC++ package to store Matrix Market (.mtx) file format sparse matrices in Compressed Row Storage (CSR) format.
munuxi / SparseRREFSparse Reduced Row Echelon Form (RREF) in C++
cloudgraph / CloudgraphCloudGraph is a suite of Java data-graph wide-row mapping and ad hoc query services for big-table sparse, columnar "cloud" and other databases
xhweei / Hypergraph Learning Based Discriminative Band SelectionFor hyperspectral images (HSIs), it is a challenging task to select discriminative bands due to the lack of labeled samples and complex noise. In this article, we present a novel local-view-assisted discriminative band selection method with hypergraph autolearning (LvaHAl) to solve these problems from both local and global perspectives. Specifically, the whole band space is first randomly divided into several subspaces (LVs) of different dimensions, where each LV denotes a set of lowdimensional representations of training samples consisting of bands associated with it. Then, for different LVs, a robust hinge loss function for isolated pixels regularized by the row-sparsity is adopted to measure the importance of the corresponding bands. In order to simultaneously reduce the bias of LVs and encode the complementary information between them, samples from all LVs are further projected into the label space. Subsequently, a hypergraph model that automatically learns the hyperedge weights is presented. In this way, the local manifold structure of these projections can be preserved, ensuring that samples of the same class have a small distance. Finally, a consensus matrix is used to integrate the importance of bands corresponding to different LVs, resulting in the optimal selection of expected bands from a global perspective. The classification experiments on three HSI data sets show that our method is competitive with other comparison methods
softdevteam / SparsevecCompress sparse tables using row displacement
oxfordcontrol / SOSADMMAn open source first-order MATLAB solver for conic programs with row sparsity.
barko / CosovoOCaml CSV parser, supporting sparse rows and comments
vnatesh / RoskoRow-skipping outer product CPU kernels for sparse-dense matrix multiplication in Deep Neural Networks
Cagnurt / HPC SpMV With BCSR FormSparse Matrix Vector Multiplication (SpMV) is one of the High Performance Computing (HPC) problems. In this project, SpMV problem is practices with two assumptions: Sparse Matrix is Symmetric Nine-Banded matrix. and the matrix is in Block Compress Sparse Row Format (BCSR) storage scheme.
foucart / HTPThese are three MATLAB routines to be used when trying to recover a sparse vector x or a row-sparse matrix X from the incomplete linear measurements y=Ax or Y=AX. They are implementations of the HTP, FHTP, and SHTP algorithms.
ChengHuangUCAS / Cvr SpmvImplementation of Compressed Vectorization-oriented sparse Row on CPU and GPU
lorentzbf / BRMergeThe source code of the paper "Accelerating CPU-based Sparse General Matrix Multiplication with Binary Row Merging"
ashkan-abbasi66 / K SVD For Inpainting OCT ImagesA simple example to show inpainting missing columns or rows of a down-sampled OCT image through K-SVD dictionary learning and sparse representation methods.
RohithM191 / TSNE On Amazon Fine Food Reviews DatasetAmazon-Food-Reviews-Analysis-and-Modelling Using Various Machine Learning Models Performed Exploratory Data Analysis, Data Cleaning, Data Visualization and Text Featurization(BOW, tfidf, Word2Vec). Build several ML models like KNN, Naive Bayes, Logistic Regression, SVM, Random Forest, GBDT, LSTM(RNNs) etc. Objective: Given a text review, determine the sentiment of the review whether its positive or negative. Data Source: https://www.kaggle.com/snap/amazon-fine-food-reviews About Dataset The Amazon Fine Food Reviews dataset consists of reviews of fine foods from Amazon. Number of reviews: 568,454 Number of users: 256,059 Number of products: 74,258 Timespan: Oct 1999 - Oct 2012 Number of Attributes/Columns in data: 10 Attribute Information: Id ProductId - unique identifier for the product UserId - unqiue identifier for the user ProfileName HelpfulnessNumerator - number of users who found the review helpful HelpfulnessDenominator - number of users who indicated whether they found the review helpful or not Score - rating between 1 and 5 Time - timestamp for the review Summary - brief summary of the review Text - text of the review 1 Amazon Food Reviews EDA, NLP, Text Preprocessing and Visualization using TSNE Defined Problem Statement Performed Exploratory Data Analysis(EDA) on Amazon Fine Food Reviews Dataset plotted Word Clouds, Distplots, Histograms, etc. Performed Data Cleaning & Data Preprocessing by removing unneccesary and duplicates rows and for text reviews removed html tags, punctuations, Stopwords and Stemmed the words using Porter Stemmer Documented the concepts clearly Plotted TSNE plots for Different Featurization of Data viz. BOW(uni-gram), tfidf, Avg-Word2Vec and tf-idf-Word2Vec 2 KNN Applied K-Nearest Neighbour on Different Featurization of Data viz. BOW(uni-gram), tfidf, Avg-Word2Vec and tf-idf-Word2Vec Used both brute & kd-tree implementation of KNN Evaluated the test data on various performance metrics like accuracy also plotted Confusion matrix using seaborne Conclusions: KNN is a very slow Algorithm takes very long time to train. Best Accuracy is achieved by Avg Word2Vec Featurization which is of 89.38%. Both kd-tree and brute algorithms of KNN gives comparatively similar results. Overall KNN was not that good for this dataset. 3 Naive Bayes Applied Naive Bayes using Bernoulli NB and Multinomial NB on Different Featurization of Data viz. BOW(uni-gram), tfidf. Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne Printed Top 25 Important Features for both Negative and Positive Reviews Conclusions: Naive Bayes is much faster algorithm than KNN The performance of bernoulli naive bayes is way much more better than multinomial naive bayes. Best F1 score is acheived by BOW featurization which is 0.9342 4 Logistic Regression Applied Logistic Regression on Different Featurization of Data viz. BOW(uni-gram), tfidf, Avg-Word2Vec and tf-idf-Word2Vec Used both Grid Search & Randomized Search Cross Validation Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne Showed How Sparsity increases as we increase lambda or decrease C when L1 Regularizer is used for each featurization Did pertubation test to check whether the features are multi-collinear or not Conclusions: Sparsity increases as we decrease C (increase lambda) when we use L1 Regularizer for regularization. TF_IDF Featurization performs best with F1_score of 0.967 and Accuracy of 91.39. Features are multi-collinear with different featurization. Logistic Regression is faster algorithm. 5 SVM Applied SVM with rbf(radial basis function) kernel on Different Featurization of Data viz. BOW(uni-gram), tfidf, Avg-Word2Vec and tf-idf-Word2Vec Used both Grid Search & Randomized Search Cross Validation Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne Evaluated SGDClassifier on the best resulting featurization Conclusions: BOW Featurization with linear kernel with grid search gave the best results with F1-score of 0.9201. Using SGDClasiifier takes very less time to train. 6 Decision Trees Applied Decision Trees on Different Featurization of Data viz. BOW(uni-gram), tfidf, Avg-Word2Vec and tf-idf-Word2Vec Used both Grid Search with random 30 points for getting the best max_depth Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne Plotted feature importance recieved from the decision tree classifier Conclusions: BOW Featurization(max_depth=8) gave the best results with accuracy of 85.8% and F1-score of 0.858. Decision Trees on BOW and tfidf would have taken forever if had taken all the dimensions as it had huge dimension and hence tried with max 8 as max_depth 6 Ensembles(RF&GBDT) Applied Random Forest on Different Featurization of Data viz. BOW(uni-gram), tfidf, Avg-Word2Vec and tf-idf-Word2Vec Used both Grid Search with random 30 points for getting the best max_depth, learning rate and n_estimators. Evaluated the test data on various performance metrics like accuracy, f1-score, precision, recall,etc. also plotted Confusion matrix using seaborne Plotted world cloud of feature importance recieved from the RF and GBDT classifier Conclusions: TFIDF Featurization in Random Forest (BASE-LEARNERS=10) with random search gave the best results with F1-score of 0.857. TFIDF Featurization in GBDT (BASE-LEARNERS=275, DEPTH=10) gave the best results with F1-score of 0.8708.
