@Machine_learn
#code #paper
Y-Autoencoders: disentangling latent representations via sequential-encoding
Article: https://arxiv.org/abs/1907.10949
GitHub: https://github.com/mpatacchiola/Y-AE
#code #paper
Y-Autoencoders: disentangling latent representations via sequential-encoding
Article: https://arxiv.org/abs/1907.10949
GitHub: https://github.com/mpatacchiola/Y-AE
arXiv.org
Y-Autoencoders: disentangling latent representations via...
In the last few years there have been important advancements in generative models with the two dominant approaches being Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)....
@Machine_learn
#CapsuleNet #code
Stacked Capsule Autoencoders
https://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.htm
#CapsuleNet #code
Stacked Capsule Autoencoders
https://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.htm
@Machine_learn
Wasserstein Robust Reinforcement Learning
article:https://arxiv.org/abs/1907.13196v1
pdf: https://arxiv.org/pdf/1907.13196v1.pdf
Wasserstein Robust Reinforcement Learning
article:https://arxiv.org/abs/1907.13196v1
pdf: https://arxiv.org/pdf/1907.13196v1.pdf
arXiv.org
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}^{2}\text{L}$ --...
Learning Scrapy Learn the art of efficient web scraping and crawling with Python
#book #python #Scrapy
@Machine_leaen
#book #python #Scrapy
@Machine_leaen
2_5361938490604913448.pdf
4.3 MB
Learning Scrapy Learn the art of efficient web scraping and crawling with Python
#book #python #Scrapy
@Machine_leaen
#book #python #Scrapy
@Machine_leaen
@Machine_learn
New paper on training with pseudo-labels for semantic segmentation
Semi-Supervised Segmentation of Salt Bodies in Seismic Images:
SOTA (1st place) at TGS Salt Identification Challenge.
Github: https://github.com/ybabakhin/kaggle_salt_bes_phalanx
ArXiV: https://arxiv.org/abs/1904.04445
#GCPR2019 #Segmentation #CV
New paper on training with pseudo-labels for semantic segmentation
Semi-Supervised Segmentation of Salt Bodies in Seismic Images:
SOTA (1st place) at TGS Salt Identification Challenge.
Github: https://github.com/ybabakhin/kaggle_salt_bes_phalanx
ArXiV: https://arxiv.org/abs/1904.04445
#GCPR2019 #Segmentation #CV
Machine Learning Refined
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
5_6188486461180870748.pdf
10.9 MB
Machine Learning Refined
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
Machine Learning for OpenCV
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
5_6154467025956634927.pdf
27.1 MB
Machine Learning for OpenCV
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
@Machine_learn
Interpreting Latent Space of GANs for Semantic Face Editing
https://shenyujun.github.io/InterFaceGAN/
code: https://github.com/ShenYujun/InterFaceGAN.git
Interpreting Latent Space of GANs for Semantic Face Editing
https://shenyujun.github.io/InterFaceGAN/
code: https://github.com/ShenYujun/InterFaceGAN.git
@Machine_learn
How to Implement Progressive Growing GAN Models in Keras
https://machinelearningmastery.com/how-to-implement-progressive-growing-gan-models-in-keras/
How to Implement Progressive Growing GAN Models in Keras
https://machinelearningmastery.com/how-to-implement-progressive-growing-gan-models-in-keras/
@Machine_learn
The HSIC Bottleneck: Deep Learning without Back-Propagation🥺
An alternative to conventional backpropagation, that has a number of distinct advantages.
Link: https://arxiv.org/abs/1908.01580
#backpropagation #DL
The HSIC Bottleneck: Deep Learning without Back-Propagation🥺
An alternative to conventional backpropagation, that has a number of distinct advantages.
Link: https://arxiv.org/abs/1908.01580
#backpropagation #DL
arXiv.org
The HSIC Bottleneck: Deep Learning without Back-Propagation
We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks. The HSIC bottleneck is an alternative to the conventional cross-entropy loss and...
@Machine_learn
Rank-consistent Ordinal Regression for Neural Networks
Article: https://arxiv.org/abs/1901.07884
PyTorch: https://github.com/Raschka-research-group/coral-cnn
Rank-consistent Ordinal Regression for Neural Networks
Article: https://arxiv.org/abs/1901.07884
PyTorch: https://github.com/Raschka-research-group/coral-cnn
arXiv.org
Rank consistent ordinal regression for neural networks with...
In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category...