Understanding Neural Networks via Feature Visualization: A survey
Nguyen et al.: https://arxiv.org/pdf/1904.08939v1.pdf
#neuralnetworks #generatornetwork #generativemodels

Unsupervised Learning with Graph Neural Networks
By Thomas Kipf.
Slides : http://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
Recording: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
#deeplearning #neuralnetworks #unsupervisedlearning #technology

Distributed deep learning/machine learning tasks

https://github.com/cerndb/dist-keras
https://github.com/Azure/DistributedDeepLearning/
https://towardsdatascience.com/deep-learning-with-apache-spark-part-2-2a2938a36d35

https://aiwiz.com/introduction-to-a-version-control-system-git-and-github/

https://www.intel.ai/introducing-nauta/#gs.ecvu0o

AI was 94 percent accurate in screening for lung cancer on 6,716 CT scans, reports a new paper in Nature, and when pitted against six expert radiologists, when no prior scan was available, the deep learning model beat the doctors: It had fewer false positives and false negatives.
https://www.nytimes.com/2019/05/20/health/cancer-artificial-intelligence-ct-scans.html

Revisiting Graph Neural Networks: All We Have is Low-Pass Filters "Our results indicate that graph neural networks only perform low-pass filtering on feature vectors"


https://arxiv.org/abs/1905.09550

Intel hardware vs Deep learning models
https://www.forbes.com/sites/janakirammsv/2019/05/26/running-deep-learning-models-on-intel-hardware-its-time-to-consider-a-different-os/amp/

Understanding Hinton’s Capsule Networks. Part I: Intuition.
Blog by Max Pechyonkin: https://medium.com/ai³-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
#MachineLearning #DeepLearning #GeoffreyHinton #ArtificialIntelligence #Theory

Augmented Neural ODEs
Dupont et al.
Github: https://github.com/EmilienDupont/augmented-neural-odes
Paper: https://arxiv.org/abs/1904.01681
#ArtificialIntelligence #MachineLearning #Pytorch

FastSpeech: Fast, Robust and Controllable Text to Speech

speeds up the mel-spectrogram generation by 270x and the end-to-end speech synthesis by 38x

ArXiv
https://arxiv.org/abs/1905.09263
Samples
https://speechresearch.github.io/fastspeech/

Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection #CVPR2019


Key component to close the gap between image & LiDAR based 3D object detection may be simply the representation of 3D information

SOTA on KITTI

https://arxiv.org/abs/1812.07179v4

DeepRED: Deep Image Prior Powered by RED


Unsupervised restoration algorithm combines Deep Image Prior with the Regularization by Denoising (RED) while avoiding the need to differentiate the chosen denoiser

https://arxiv.org/abs/1903.10176

Collections of Papers & Code on Domain Adaptation

https://github.com/zhaoxin94/awsome-domain-adaptation

TensorFlow Dev Summit 2019

https://www.youtube.com/playlist?list=PLQY2H8rRoyvzoUYI26kHmKSJBedn3SQuB

"Introduction to Deep Learning" Course

Slides, course materials, demos, and implementations

https://chokkan.github.io/deeplearning/

Myia is a new differentiable programming language. It aims to support large scale high performance computations (e.g. linear algebra) and their gradients. The main application Myia aims to support is research in artificial intelligence, in particular deep learning algorithms.


https://github.com/mila-iqia/myia

LSTM Autoencoder for Extreme Rare Event Classification in Keras
Ranjan et al.: https://towardsdatascience.com/lstm-autoencoder-for-extreme-rare-event-classification-in-keras-ce209a224cfb
#DeepLearning #DataScience #ArtificialIntelligence #DataScience

"Wasserstein GAN"

Written by James Allingham: http://www.depthfirstlearning.com/2019/WassersteinGAN

#DeepLearning #GenerativeModels #GenerativeAdversarialNetworks

Microsoft launches a drag-and-drop machine learning tool

Article by Frederic Lardinois: https://techcrunch.com/2019/05/02/microsoft-launches-a-drag-and-drop-machine-learning-tool-and-hosted-jupyter-notebooks/

#ArtificialIntelligence #DeepLearning #MachineLearning

Datasheets for Datasets
Gebru et al.: https://arxiv.org/abs/1803.09010
#Databases #ArtificialIntelligence #AIEthics #Ethics #MachineLearning

Minicourse in Deep Learning with PyTorch
By Alfredo Canziani: https://github.com/Atcold/pytorch-Deep-Learning-Minicourse
#DeepLearning #MachineLearning #PyTorch