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

👌Finding label errors in datasets and learning with noisy labels.

https://github.com/cgnorthcutt/cleanlab/

New book 🔥DEEP LEARNING WITH PYTORCH 2019 #DL #Python
#Book #CNN #RNN

@Machine_learn

GNNExplainer: Generating Explanations for Graph Neural Networks

https://arxiv.org/abs/1903.03894

Github : https://github.com/RexYing/gnn-model-explainer/

Practical Machine Learning with Python
#ML #Python
@Machine_learn

Machine learning for ios
#apple #ios #book
@Machine_learn

Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs


https://arxiv.org/abs/1910.06922

SIte : https://ajolicoeur.wordpress.com/

Github : https://github.com/AlexiaJM/MaximumMarginGANs

A collection of anomaly detection methods #Code #Python #Anomaly_detection @Machine_learn

T5: Text-To-Text Transfer Transformer

Github: https://github.com/google-research/text-to-text-transfer-transformer

Paper: https://arxiv.org/abs/1910.10683

@Machine_learn