https://youtu.be/TMFcfT5ZWC8 Peter Norvig Google AI Microsoft Research

A birds-eye view of optimization algorithms

By Fabian Pedregosa: https://fa.bianp.net/teaching/2018/eecs227at/

#ArtificialIntelligence #NeuralNetworks

State of the art- Latest from Google AI: Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction.
paper: https://www.profillic.com/paper/arxiv:1904.11111

These guys show improvement over state-of-the-art monocular depth prediction methods!

AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
Qian et al.: https://arxiv.org/abs/1905.05879
Demo: https://auspicious3000.github.io/autovc-demo/
#ArtificialIntelligence #DeepLearning #MachineLearning

ArviZ: Exploratory analysis of Bayesian models
Includes functions for posterior analysis, sample diagnostics, model checking, and comparison: https://arviz-devs.github.io/arviz/
#ArtificialIntelligence #Bayesian #BayesianInference #MachineLearning #Python

Machine Learning Open Source of the Month (v.May 2019)
https://medium.mybridge.co/machine-learning-open-source-for-the-past-month-v-may-2019-bf4ff9b80b1b

Model optimization with new Tensorflow tool
https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-pruning-api-42cac9157a6a

Deep Flow-Guided Video Inpainting
Xu et al.: https://nbei.github.io/video-inpainting.html
#AritifcialIntelligence #DeepLearning #MachineLearning

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 : https://helper.ipam.ucla.edu/publications/glws4/glws4_15546.pdf
Recording: https://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