Unfolding the Structure of a Document using Deep Learning.
#DL #paper
@Machine_learn
https://arxiv.org/abs/1910.03678

Exploring Massively Multilingual, Massive Neural Machine Translation


http://ai.googleblog.com/2019/10/exploring-massively-multilingual.html
#DL #paper
@Machine_learn

article: https://arxiv.org/pdf/1907.05019.pdf

Master open Cv second edition
#book #OpenCV
@Machine_learn

Chollet deeplearning
#DL #book #Python
@Machine_learn

OpenCV By Example
#DL #book #openCV
@Machine_learn

Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

video: https://www.youtube.com/watch?v=-5XxXRABXJs&feature=youtu.be

code: https://github.com/MIT-SPARK/Kimera

article: https://arxiv.org/abs/1910.02490

CrypTen: A new research tool for secure machine learning with PyTorch

https://ai.facebook.com/blog/crypten-a-new-research-tool-for-secure-machine-learning-with-pytorch


code: https://github.com/facebookresearch/CrypTen

ICCV 2019 papers open access

http://openaccess.thecvf.com/ICCV2019.py

Workshops:


http://openaccess.thecvf.com/ICCV2019_workshops/menu.py

Neural networks in NLP are vulnerable to adversarially crafted inputs.

We show that they can be trained to become certifiably robust against input perturbations such as typos and synonym substitution in text classification:

https://arxiv.org/abs/1909.01492

2019 #Deep_Learning Based Recommender System A #Survey and New Perspectives
@Machine_learn

Deep learning for biometrics
#book #Dl
@Machine_learn

Deep feature flow for video recognition
#Dl #Book
@Machine_learn

discriminative :
1:#Regression
2:#Logistic regression
3:#decision tree(Hunt)
4:#neural network(traditional network, deep network)
5:#Support Vector Machine(SVM)
Generative:
1:#Hidden Markov model
2:#Naive bayes
3:#K-nearest neighbor(KNN)
4:#Generative adversarial networks(GANs)
Deep learning:
1:CNN
R_CNN
Fast-RCNN
Mask-RCNN
2:RNN
3:LSTM
4:CapsuleNet
5:Siamese:
siamese cnn
siamese lstm
siamese bi-lstm
siamese CapsuleNet
6:time series data
SVR
DT(cart)
Random Forest linear
Bagging
Boosting

جهت درخواست و راهنمایی در رابطه با پیاده سازی مقالات و پایان نامه ها در رابطه با مباحث deep learning و machine learning با ایدی زیر در ارتباط باشید
@Raminmousa

Hamiltonian Neural Networks

https://eng.uber.com/research/hamiltonian-neural-networks/

paper: https://arxiv.org/pdf/1906.01563.pdf

code: https://github.com/greydanus/hamiltonian-nn

🔥OpenAI realesed the 1.5billion parameter GPT-2 model

Post: https://openai.com/blog/gpt-2-1-5b-release/
GPT-2 output detection model: https://github.com/openai/gpt-2-output-dataset/tree/master/detector
Research from partners on potential malicious uses: https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf

#NLU #GPT2 #OpenAI #NLP

HoloGAN (A new generative model) learns 3D representation from natural images

Article: https://arxiv.org/pdf/1904.01326.pdf

Code: https://github.com/thunguyenphuoc/HoloGAN

Dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

Applied Deep Learning — Umberto Michelucci (en) 2018

@Machine_learn

Stacked Capsule Autoencoders

https://github.com/google-research/google-research/tree/master/stacked_capsule_autoencoders

paper : https://arxiv.org/abs/1906.06818

http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.html

This AI Learned To Animate Humanoids 🚶

https://www.youtube.com/watch?v=cTqVhcrilrE

code: https://github.com/sebastianstarke/AI4Animation

Check out Lambda here and sign up for their GPU Cloud : https://lambdalabs.com/papers