Utterance-level Aggregation for Speaker Recognition in the Wild
Project page http://www.robots.ox.ac.uk/~vgg/research/speakerID/
paper https://arxiv.org/pdf/1902.10107.pdf

Convex Optimization: Algorithms and Complexity
Sébastien Bubeck

Theory Group, Microsoft Research
sebubeck@microsoft.com
https://arxiv.org/pdf/1405.4980.pdf

A new paper on learned lossless compression: 30% smaller images than PNG, using a fully parallel probabilistic model that is orders of magnitude faster than PixelCNN
PyTorch Implementation of the CVPR'19 Paper "Practical Full Resolution Learned Lossless Image Compression"
https://github.com/fab-jul/L3C-PyTorch

Very interesting paper from Google Research. Generating video from first and end frames
https://arxiv.org/pdf/1905.10240.pdf

Classification Accuracy Score for Conditional Generative Models
Suman Ravuri and Oriol Vinyals: https://arxiv.org/abs/1905.10887
#ArtificialIntelligence #DeepLearning #MachineLearning

Came out a few days back: MNIST test set with extra 50,000 training samples!
If your models overfit the original set, you should try it on this one! ;)
https://www.profillic.com/paper/arxiv:1905.10498

https://www.infoq.com/news/2019/05/google-tensorflow-graphics/ Google AI

Chinese AI Talent in Six Charts
Blog by Matt Sheehan: https://macropolo.org/china-ai-research-talent-data/
#ArtificialIntelligence #China #DeepLearning

Advanced NLP with spaCy by Ines Montani, core developers of spaCy: https://course.spacy.io

In this course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Creating accurate #MachineLearning Models which are capable of identifying and localizing multiple objects in a single image remained a core challenge in computer vision. But, with recent advancements in #DeepLearning, #ObjectDetection applications are easier to develop than ever before. So, if you want to know how to perform Real-Time Object Detection using #Tensorflow, you can refer to the following article:
https://medium.com/edureka/tensorflow-object-detection-tutorial-8d6942e73adc

TensorWatch: New debugging and visualization tool designed for deep learning, from Microsoft Research!

Github: https://github.com/microsoft/tensorwatch

“Instead of labeling images, a researcher now simply plays video games all day long.” 🤔

Free supervision from video games
http://bit.do/eTw8d

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Mingxing Tan and Quoc V. Le: https://arxiv.org/abs/1905.11946
Github: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
Blog: https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html?m=1
#ArtificialIntelligence #MachineLearning #NeuralNetworks

Defending Against Neural Fake News
Zellers et al.: https://arxiv.org/abs/1905.12616
#ArtificialIntelligence #DeepLearning #Society

Very proud and enthusiastic to contribute to this project!


https://www.korbit.ai/machinelearning

Multi-Sample Dropout for Accelerated Training and Better Generalization
Hiroshi Inoue: https://arxiv.org/abs/1905.09788
#ArtificialIntelligence #NeuralComputing #MachineLearning

Deep Scale-spaces: Equivariance Over Scale
Daniel E. Worrall and Max Welling: https://arxiv.org/abs/1905.11697
#ArtificialIntelligence #DeepLearning #MachineLearning

SinGAN: Learning a Generative Model from a Single Natural Image
Shaham et al.: https://arxiv.org/abs/1905.01164v1
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks

VAE-SBD
PyTorch implementation of the Variational Autoencoder with Spatial Broadcast Decoder.
GitHub by Daniel Daza: https://github.com/dfdazac/vaesbd
#deeplearning #pytorch #technology #innovation