haskell-vaeLearning about Haskell with Variational Autoencoders
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autoencoder for physical layerThis is my attempt to reproduce and extend the results in the paper "An Introduction to Deep Learning for the Physical Layer" by Tim O'Shea and Jakob Hoydis
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TensorFlow-AutoencodersImplementations of autoencoder, generative adversarial networks, variational autoencoder and adversarial variational autoencoder
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Deepsvg[NeurIPS 2020] Official code for the paper "DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation". Includes a PyTorch library for deep learning with SVG data.
Stars: ✭ 403 (+706%)
Ad examplesA collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.
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mauiMulti-omics Autoencoder Integration: Deep learning-based heterogenous data analysis toolkit
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T3[EMNLP 2020] "T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack" by Boxin Wang, Hengzhi Pei, Boyuan Pan, Qian Chen, Shuohang Wang, Bo Li
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AutoEncodersVariational autoencoder, denoising autoencoder and other variations of autoencoders implementation in keras
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Video-Compression-NetA new approach to video compression by refining the shortcomings of conventional approach and substituting each traditional component with their neural network counterpart. Our proposed work consists of motion estimation, compression and compensation and residue compression, learned end-to-end to minimize the rate-distortion trade off. The whole…
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AutoencodersTorch implementations of various types of autoencoders
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NeurecNext RecSys Library
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Encoder-ForesteForest: Reversible mapping between high-dimensional data and path rule identifiers using trees embedding
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autoencoders tensorflowAutomatic feature engineering using deep learning and Bayesian inference using TensorFlow.
Stars: ✭ 66 (+32%)
seq3Source code for the NAACL 2019 paper "SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression"
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DancenetDanceNet -💃💃Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras)
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video autoencoderVideo lstm auto encoder built with pytorch. https://arxiv.org/pdf/1502.04681.pdf
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sldm4-h2oStatistical Learning & Data Mining IV - H2O Presenation & Tutorial
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peaxPeax is a tool for interactive visual pattern search and exploration in epigenomic data based on unsupervised representation learning with autoencoders
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Tensorflow Mnist VaeTensorflow implementation of variational auto-encoder for MNIST
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abae-pytorchPyTorch implementation of 'An Unsupervised Neural Attention Model for Aspect Extraction' by He et al. ACL2017'
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time-series-autoencoder📈 PyTorch dual-attention LSTM-autoencoder for multivariate Time Series 📈
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Tensorflow TutorialTensorflow tutorial from basic to hard, 莫烦Python 中文AI教学
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vae-pytorchAE and VAE Playground in PyTorch
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ZhihuThis repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.
Stars: ✭ 3,307 (+6514%)
Keras Idiomatic ProgrammerBooks, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework
Stars: ✭ 720 (+1340%)
deep-stegGlobal NIPS Paper Implementation Challenge of "Hiding Images in Plain Sight: Deep Steganography"
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Alae[CVPR2020] Adversarial Latent Autoencoders
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RecoderLarge scale training of factorization models for Collaborative Filtering with PyTorch
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VisualMLInteractive Visual Machine Learning Demos.
Stars: ✭ 104 (+108%)
Noise2Noise-audio denoising without clean training dataSource code for the paper titled "Speech Denoising without Clean Training Data: a Noise2Noise Approach". Paper accepted at the INTERSPEECH 2021 conference. This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio denoising methods by showing that it is possible to train deep speech denoisi…
Stars: ✭ 49 (-2%)
catseyeNeural network library written in C and Javascript
Stars: ✭ 29 (-42%)
Tensorflow BookAccompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Stars: ✭ 4,448 (+8796%)
Cifar-AutoencoderA look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.
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handson-ml도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
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Concise Ipython Notebooks For Deep LearningIpython Notebooks for solving problems like classification, segmentation, generation using latest Deep learning algorithms on different publicly available text and image data-sets.
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SAE-NADThe implementation of "Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence"
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SESF-FuseSESF-Fuse: An Unsupervised Deep Model for Multi-Focus Image Fusion
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PyodA Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
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AE-CNNICVGIP' 18 Oral Paper - Classification of thoracic diseases on ChestX-Ray14 dataset
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Rnn VaeVariational Autoencoder with Recurrent Neural Network based on Google DeepMind's "DRAW: A Recurrent Neural Network For Image Generation"
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Generative ModelsAnnotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
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mirapyMiraPy: A Python package for Deep Learning in Astronomy
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