Machine learning books and papers

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

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

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

🔥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

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

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

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

ICCV 2019 papers open access http://openaccess.thecvf.com/ICCV2019.py Workshops: http://openaccess.thecvf.com/ICCV2019_workshops/menu.py

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

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

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

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