[P] Machine Learning for Classification (production grade)

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Hi. I want to share my experience of building an effective automotive classification process (in Dataiku) using machine learning process. ​ Git: https://github.com/elegantwist/catalog_classifier ​ One has to build a classifier of elements of a dictionary (company) based on a text description of the element company scope of interest. The classification […]

[R] A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

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​ https://i.redd.it/vf0219ecqf821.jpg Python: https://github.com/benedekrozemberczki/GAM Paper: https://github.com/benedekrozemberczki/GAM/blob/master/paper.pdf Abstract: Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs of different classes. When calculating such features, most existing approaches process the entire […]

[P] Recognizing hand gestures and direction (for controlling smart homes by pointing)

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In case someone else wants to control their Hue-lights or any other items by pointing and gestures. https://github.com/holli/hands_ai There is a simple model/framework to recognize classes and direction in one go. Using a modified yolo/single-shot-detector but with directions instead of bounding boxes. Also an example of using the results for […]

[R] Stochastic Graphlet Embedding

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Joint work of Dr. Anjan Dutta (CVC, UAB, Barcelona) and Dr. Hichem Sahbi (LIP6, UPMC, Paris) on "Stochastic Graphlet Embedding" is published in IEEE TNNLS. An efficient and robust graph embedding technique that encode the distribution of increasing sized graphlets with stochastic graph parsing and hashing: https://ieeexplore.ieee.org/document/8587135. ​ Overview of […]

How to Reduce Variance in the Final Deep Learning Model With a Horizontal Voting Ensemble

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Predictive modeling problems where the training dataset is small relative to the number of unlabeled examples are challenging. Neural networks can perform well on these types of problems, although they can suffer from high variance in model performance as measured on a training or hold-out validation datasets. This makes choosing […]

[Project] [P] Analyzing r/MachineLearning 2018 posts with Graphext unsupervised NLP algorithms

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​ 2509 posts from r/MachineLearning in 2018 clustered with Graphext We at Graphext ( @graphext ) use word2vec + dimensionality reduction + network algorithms to cluster all type of data, from text to images to numerical and categorical vectors to spot unsupervised patterns, among many other things in data science. […]