Machinelearning

How to Develop a Conditional GAN (cGAN) From Scratch

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images.

5.88K views07:30

Machinelearning

Check the data science channel there you will find a lot of articles, links and advanced researches .
Join and learn hot topics of data science @opendatascience

5.75K views16:23

Machinelearning

Literature of Deep Learning for Graphs
This is a paper list about deep learning for graphs.

https://github.com/DeepGraphLearning/LiteratureDL4Graph

GitHub

GitHub - DeepGraphLearning/LiteratureDL4Graph: A comprehensive collection of recent papers on graph deep learning

A comprehensive collection of recent papers on graph deep learning - DeepGraphLearning/LiteratureDL4Graph

5.84K views06:02

Machinelearning

Forwarded from Artificial Intelligence

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...

180 views08:16

Machinelearning

How to Identify and Diagnose GAN Failure Modes

https://machinelearningmastery.com/practical-guide-to-gan-failure-modes/

MachineLearningMastery.com

How to Identify and Diagnose GAN Failure Modes - MachineLearningMastery.com

How to Identify Unstable Models When Training Generative Adversarial Networks. GANs are difficult to train. The reason they are difficult to train is that both the generator model and the discriminator model are trained simultaneously in a zero sum game.…

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5.93K views08:16

Machinelearning

Neural Architecture Search at CVPR 2019

https://drsleep.github.io/NAS-at-CVPR-2019/

5.28K views07:55

Machinelearning

Predicting the Generalization Gap in Deep Neural Networks

http://ai.googleblog.com/2019/07/predicting-generalization-gap-in-deep.html

Googleblog

Predicting the Generalization Gap in Deep Neural Networks

5.27K views17:43

Machinelearning

Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data

Article: https://arxiv.org/abs/1907.04240
PDF: https://arxiv.org/pdf/1907.04240.pdf

arXiv.org

Bayesian deep learning with hierarchical prior: Predictions from...

Datasets in engineering applications are often limited and contaminated,
mainly due to unavoidable measurement noise and signal distortion. Thus, using
conventional data-driven approaches to build...

5.48K views08:01

Machinelearning

A Tour of Generative Adversarial Network Models

https://machinelearningmastery.com/tour-of-generative-adversarial-network-models/

MachineLearningMastery.com

A Tour of Generative Adversarial Network Models - MachineLearningMastery.com

Generative Adversarial Networks, or GANs, are deep learning architecture generative models that have seen wide success.

There are thousands of papers on GANs and many hundreds of named-GANs, that is, models with a defined name that often includes

6.54K views08:01

Machinelearning

Advancing Semi-supervised Learning with Unsupervised Data Augmentation

http://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html

Googleblog

Advancing Semi-supervised Learning with Unsupervised Data Augmentation

6.03K views07:36

Machinelearning

TRFL a library of reinforcement learning building blocks By the Research Engineering team at DeepMind:

https://github.com/deepmind/trfl

GitHub

GitHub - google-deepmind/trfl: TensorFlow Reinforcement Learning

TensorFlow Reinforcement Learning. Contribute to google-deepmind/trfl development by creating an account on GitHub.

5.24K views09:09

Machinelearning

Multilingual Universal Sentence Encoder for Semantic Retrieval
http://ai.googleblog.com/2019/07/multilingual-universal-sentence-encoder.html

research.google

Multilingual Universal Sentence Encoder for Semantic Retrieval

Posted by Yinfei Yang and Amin Ahmad, Software Engineers, Google Research Since it was introduced last year, “Universal Sentence Encoder (USE) for...

5.27K views09:05

Machinelearning

How to Code the GAN Training Algorithm and Loss Functions

https://machinelearningmastery.com/how-to-code-the-generative-adversarial-network-training-algorithm-and-loss-functions/

MachineLearningMastery.com

How to Code the GAN Training Algorithm and Loss Functions - MachineLearningMastery.com

The Generative Adversarial Network, or GAN for short, is an architecture for training a generative model. The architecture is comprised of two models. The generator that we are interested in, and a discriminator model that is used to assist in the training…

5.36K views09:06

Machinelearning

An implementation of the BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer (Interspeech 2019)

Article: https://arxiv.org/pdf/1907.03040.pdf

Github: https://github.com/guanlinchao/bert-dst

GitHub

GitHub - guanlinchao/bert-dst: BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations…

BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer - guanlinchao/bert-dst

4.89K views08:16

Machinelearning

Learning to learn with quantum neural networks via classical neural networks

https://arxiv.org/abs/1907.05415

arXiv.org

Learning to learn with quantum neural networks via classical...

Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges. One such...

7.41K views08:16

Machinelearning