@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...
@Machine_leaen
ai ,machine learning
#code #datasets #paper
• 1146 leaderboards
• 1223 tasks
• 1105 datasets
• 14779 papers with code
https://paperswithcode.com/sota
ai ,machine learning
#code #datasets #paper
• 1146 leaderboards
• 1223 tasks
• 1105 datasets
• 14779 papers with code
https://paperswithcode.com/sota
Paperswithcode
Papers with Code - Browse the State-of-the-Art in Machine Learning
14420 leaderboards • 5896 tasks • 13612 datasets • 168964 papers with code.
@Machine_learn
Memory-Efficient Adaptive Optimization
Source: https://arxiv.org/abs/1901.11150
Code: https://github.com/google-research/google-research/tree/master/sm3
Memory-Efficient Adaptive Optimization
Source: https://arxiv.org/abs/1901.11150
Code: https://github.com/google-research/google-research/tree/master/sm3
arXiv.org
Memory-Efficient Adaptive Optimization
Adaptive gradient-based optimizers such as Adagrad and Adam are crucial for achieving state-of-the-art performance in machine translation and language modeling. However, these methods maintain...
@Machine_learn
🚀 Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0
Tensorflow blog: https://medium.com/tensorflow/introducing-tf-gan-a-lightweight-gan-library-for-tensorflow-2-0-36d767e1abae
Code: https://github.com/tensorflow/gan
Free course: https://developers.google.com/machine-learning/gan/
Paper: https://arxiv.org/abs/1805.08318
🚀 Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0
Tensorflow blog: https://medium.com/tensorflow/introducing-tf-gan-a-lightweight-gan-library-for-tensorflow-2-0-36d767e1abae
Code: https://github.com/tensorflow/gan
Free course: https://developers.google.com/machine-learning/gan/
Paper: https://arxiv.org/abs/1805.08318
Medium
Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0
Posted by Joel Shor, Yoel Drori, Google Research Tel Aviv, Aaron Sarna, David Westbrook, Paige Bailey