sdimple-chf's repositories

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video_face_detect

视频流中人脸识别检测:从《都挺好》第一集视频中检测出现的人脸数并合并相同人的脸,统计露脸人数。

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FCHD-Fully-Convolutional-Head-Detector

Code for FCHD - A fast and accurate head detector

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rgcnn

Neural Recommender System from "Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks"

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DeepLearningTutorials-1

Deep Learning与PyTorch入门实战视频教程

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SPM_toolkit

Neural network toolkit for sentence pair modeling.

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Age-and-Gender

同时识别年龄与性别

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deep-learning-with-keras-notebooks

Jupyter notebooks for using & learning Keras

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CASE

code for Truth Discovery by Claim and Source Embedding

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atics_i

First module assignment for Advanced Topics In Computer Science course held at the "Roma Tre" University of Rome.

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Tweets-Clustering-using-k-means

By clustering similar tweets together, we can generate a more concise and organized representation of the raw tweets, which will be very useful for many Twitter-based applications (e.g., truth discovery, trend analysis, search ranking, etc.)

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Twitter-Truth-Discovery

Implemented a maximum likelihood algorithm in python which performs credibility analysis on clusters of similar tweets gathered from the Tweepy Twitter API

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self-training-scikit-learn-PySpark

Semi-supervised method is a class of supervised learning techniques applied on a data with small amount of labeled and large amount of unlabeled data. Here, self-training method is applied to efficiently label unlabeled data. Effect of different parameters (probability threshold, unlabeled data ratio) are investigated versus the cost of human labeling. The model is developed in both sequential (with python) and distributed (with PySpark) systems. At the end, accuracy of the developed model is compared with label propagation model from Scikit-learn package.

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graph-based-recommendation-system

building a recommendation system using graph search methodologies. We will be comparing these different approaches and closely observe the limitations of each.

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Tweets-Clustering-KMeans

Implemented K-MEANS algorithm in Python using Jaccard distance as distance metric and analyzed various twitter based applications that involve truth discovery, trend analysis, search ranking.

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KDEm

This repository includes data and code for the algorithm of Kernel Density Estimation from Multiple Sources (KDEm) proposed in a KDD'16 paper

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Twitter-Truth-Discovery-1

Accurately ascertain both the correctness of each tweet and the reliability of each Twitter user.

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truthfinder

TruthFinder.org website

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