L0LearnEfficient Algorithms for L0 Regularized Learning
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Statistical-Learning-using-RThis is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.
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manifold mixupTensorflow implementation of the Manifold Mixup machine learning research paper
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aheadUnivariate and multivariate time series forecasting
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BayesHMMFull Bayesian Inference for Hidden Markov Models
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pyowlOrdered Weighted L1 regularization for classification and regression in Python
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PenaltyFunctions.jlJulia package of regularization functions for machine learning
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SSE-PTCodes and Datasets for paper RecSys'20 "SSE-PT: Sequential Recommendation Via Personalized Transformer" and NurIPS'19 "Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers"
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Regularization-Pruning[ICLR'21] PyTorch code for our paper "Neural Pruning via Growing Regularization"
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ANTsRAdvanced Normalization Tools in R
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AI Learning HubAI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics
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ISLR-PythonNotes and implementations in Python for ISLR.
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GDLibraryMatlab library for gradient descent algorithms: Version 1.0.1
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hyperstarHyperstar: Negative Sampling Improves Hypernymy Extraction Based on Projection Learning.
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SLOPESorted L1 Penalized Estimation
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DistillerNeural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
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r4sl📈 Machine Learning from the perspective of a Statistician using R
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AMP-RegularizerCode for our paper "Regularizing Neural Networks via Adversarial Model Perturbation", CVPR2021
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LearnedSortLearned Sort: a model-enhanced sorting algorithm
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bruceR📦 BRoadly Useful Convenient and Efficient R functions that BRing Users Concise and Elegant R data analyses.
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traj-pred-irlOfficial implementation codes of "Regularizing neural networks for future trajectory prediction via IRL framework"
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JCLALJCLAL is a general purpose framework developed in Java for Active Learning.
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Islr PythonAn Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
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consistencyImplementation of models in our EMNLP 2019 paper: A Logic-Driven Framework for Consistency of Neural Models
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BoostSRLBoostSRL: "Boosting for Statistical Relational Learning." A gradient-boosting based approach for learning different types of SRL models.
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Deep-Learning-Specialization-CourseraDeep Learning Specialization Course by Coursera. Neural Networks, Deep Learning, Hyper Tuning, Regularization, Optimization, Data Processing, Convolutional NN, Sequence Models are including this Course.
Stars: ✭ 75 (+102.7%)
Esl CnThe Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答。
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data-scienceLecture Slides for Introduction to Data Science
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tulipScaleable input gradient regularization
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pyunfoldIterative unfolding for Python
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sparsebnSoftware for learning sparse Bayesian networks
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ISLR.jlJuliaLang version of "An Introduction to Statistical Learning: With Applications in R"
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M2cgenTransform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
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etascalibrate ETAS, simulate using ETAS, estimate completeness magnitude & magnitude frequency distribution
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sparserega collection of modern sparse (regularized) linear regression algorithms.
Stars: ✭ 55 (+48.65%)
DROPFixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
Stars: ✭ 87 (+135.14%)
linear-treeA python library to build Model Trees with Linear Models at the leaves.
Stars: ✭ 128 (+245.95%)
sldm4-h2oStatistical Learning & Data Mining IV - H2O Presenation & Tutorial
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gglmannotate📈Annotate a ggplot with a description of a linear model
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DeeplearningPython for《Deep Learning》,该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现
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tests-as-linearPython port of "Common statistical tests are linear models" by Jonas Kristoffer Lindeløv.
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An Introduction To Statistical LearningThis repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.
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appliedstats📊 Methods of Applied Statistics Course Textbook Repository
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FSCNMFAn implementation of "Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks".
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golinearliblinear bindings for Go
Stars: ✭ 45 (+21.62%)
mixupspeechpro.com/
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deep-learning-notes🧠👨💻Deep Learning Specialization • Lecture Notes • Lab Assignments
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Awesome Decision Tree PapersA collection of research papers on decision, classification and regression trees with implementations.
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