195 skills found · Page 7 of 7
mohamedehab00 / A Hybrid Arabic Text Summarization Approach Based On TransformersIn this paper, we proposed a sequential hybrid model based on a transformer to summarize Arabic articles. We used two approaches of summarization to make our model. The First is the extractive approach which depends on the most important sentences from the articles to be the summary, so we used Deep Learning techniques specifically transformers such as AraBert to make our summary, The second is abstractive, and this approach is similar to human summarization, which means that it can use some words which have the same meaning but different from the original text. We apply this kind of summary using MT5 Arabic pre-trained transformer model. We sequentially applied these two summarization approaches to building our A3SUT hybrid model. The output of the extractive module is fed into the abstractive module. We enhanced the summary’s quality to be closer to the human summary by applying this approach. We tested our model on the ESAC dataset and evaluated the extractive summary using the Rouge score technique; we got a precision of 0.5348 and a recall of 0.5515, and an f1 score of 0.4932 and the evaluation of the abstractive model is evaluated by user satisfaction. We add some features to our summary to make it more understandable by applying the metadata generation task” data about data” and classification. By applying metadata generation, we add facilities to our summary, identification, and summary organization. Metadata provides essential contextual details, as not all summaries are self-describing. Also, classify the original text to determine the summary topic before reading. We acquire 97.5% accuracy by using Support Vector Machine (SVM) and trained it using NADA corpus.
Aidenzich / TVAThis project is built around PyTorch-Lightning, offering a structured and friendly environment for anyone exploring sequential recommendation systems. This platform is designed to simplify the machine learning workflow, letting you focus more on the strategic aspects of model development and less on setup complexities.
BAMresearch / SequentialLearningAppSequential Learning App for Materials Discovery (SLAMD)
non-name-2020 / AMANetAttention and Memory-Augmented Networks for Dual-View Sequential Learning
bilalmirza8519 / Multi Layer OSELMMulti-layer online sequential extreme learning machines for image classification
uberwach / Ml Streaming SparkAn introduction to machine learning techniques in the high velocity case (including sequential learning) with Apache Spark.
JinyuZ1996 / RL ISNImplementation of "Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation" (TKDE 2022)
ThunderbornSakana / PyTorch Implementation Of Models Based On Longitudinal EHR DataPyTorch implementation of state-of-the-art deep learning models for learning patient representations from sequential EHR data.
BeileiCui / MS TFAL[MICCAI 2023] Rectifying Noisy Labels with Sequential Prior: Multi-Scale Temporal Feature Affinity Learning for Robust Video Segmentation
mpiccardi / A Vademecum Of Machine Learning 2014A short course on machine learning, with emphasis on "shallow" sequential models such as hidden Markov models (HMMs), conditional random fields (CRFs), Kalman and particle filters. NB: it dates from 2014 and covers no deep learning. Good for foundations.
lakshanakolur / Accent Recognition MLSupervised Machine Learning Model for Accent Recognition in English Speech using Sequential MFCC Features
liyipeng00 / ConvergenceConvergence Analysis of Sequential Federated Learning on Heterogeneous Data
abedidev / FedSLImplementation of FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks
respailab / CLMULA comprehensive framework consisting of sequential continual learning and machine unlearning requests for improving classification tasks
fazildgr8 / AppliedDeepLearningThis repository consists a set of Jupyter Notebooks with a different Deep Learning methods applied. Each notebook gives walkthrough from scratch to the end results visualization hierarchically. The Deep Learning methods include Multiperceptron layers, CNN, GAN, Autoencoders, Sequential and Non-Sequential deep learning models. The fields applied includes Image Classification, Time Series Prediction, Recommendation Systems , Anomaly Detection and Data Analysis.
xiaolLIN / IDURLTowards Interest Drift-driven User Representation Learning in Sequential Recommendation
CU-DitecT / Causal GAILCode for Causal GAIL. "Learning human driving behaviors with sequential causal imitation learning", AAAI-22.
rezamovahed93 / A Major Depressive Disorder Classifcation Framework Based On EEG Signals Using Statistical SpectralThis paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework.
yannTrm / Resnet 1DThis GitHub repository contains a specialized implementation of 1D Residual Networks (ResNets) for sequence data classification tasks. With tailored architectures and various ResNet variants, it offers efficient learning from 1D sequential data, making it ideal for applications such as time series analysis and sensor data classification.
hanjialiang / DeepRecCode for the WWW 2021 paper - DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce
