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NnpackAcceleration package for neural networks on multi-core CPUs
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FrvsrFrame-Recurrent Video Super-Resolution (official repository)
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AstronnDeep Learning for Astronomers with Tensorflow
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DfqPyTorch implementation of Data Free Quantization Through Weight Equalization and Bias Correction.
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Ensemble PytorchA unified ensemble framework for Pytorch to improve the performance and robustness of your deep learning model
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Cython Blis💥 Fast matrix-multiplication as a self-contained Python library – no system dependencies!
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IresnetImproved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)
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Kitnet PyKitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.
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OpenannAn open source library for artificial neural networks.
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Keras ContribKeras community contributions
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CryptonetsCryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
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Brain BitsA P300 online spelling mechanism for Emotiv headsets. It's completely written in Node.js, and the GUI is based on Electron and Vue.
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PygatPytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
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Cen[NeurIPS 2020] Code release for paper "Deep Multimodal Fusion by Channel Exchanging" (In PyTorch)
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XaynetXaynet represents an agnostic Federated Machine Learning framework to build privacy-preserving AI applications.
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PadasipPython Adaptive Signal Processing
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ElephasDistributed Deep learning with Keras & Spark
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PaddlexPaddlePaddle End-to-End Development Toolkit(『飞桨』深度学习全流程开发工具)
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Deep architectA general, modular, and programmable architecture search framework
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BnafPytorch implementation of Block Neural Autoregressive Flow
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Filter GraftingFilter Grafting for Deep Neural Networks(CVPR 2020)
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Adcme.jlAutomatic Differentiation Library for Computational and Mathematical Engineering
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WyrmAutodifferentiation package in Rust.
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Deep CfrScalable Implementation of Deep CFR and Single Deep CFR
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