Tomorrow I will be interviewing Katy Cook on her fantastic new book The Psychology of #SiliconValley: Ethical Threats and Emotional Unintelligence in the #Tech Industry. Get your free open access book here https://snglrty.co/2OGGKH9

"Latent ODEs for Irregularly-Sampled Time Series"
Paper by Rubanova et al.: https://arxiv.org/abs/1907.03907
GitHub: https://github.com/YuliaRubanova/latent_ode
#MachineLearning #OrdinaryDifferentialEquations #TimeSeries

Language Models as Knowledge Bases?
Petroni et al.: https://arxiv.org/abs/1909.01066
#Transformers #NaturalLanguageProcessing #MachineLearning

Finally Mr.Yoshua Bengio talks about causal inference.
https://www.youtube.com/watch?v=0GsZ_LN9B24&feature=youtu.be&fbclid=IwAR09eC-Pg_uB6vNCHZcek5jJaRLIX09Yo5SxmKAba0xPi_bAndvcYdIOxvo

https://t.iss.one/ArtificialIntelligenceArticles

Write-A-Video: Computational Video Montage from Themed Text
webpage: https://faculty.idc.ac.il/arik/site/writeVideo.asp
video: https://vimeo.com/357657704

Object-Guided Instance Segmentation for Biological Images. https://arxiv.org/abs/1911.09199

Active Learning for Deep Detection Neural Networks. https://arxiv.org/abs/1911.09168

"Deep Learning with PyTorch" provides a detailed, hands-on introduction to building and training neural networks with PyTorch, a popular open-source machine learning framework.

This book includes:

Introduction to deep learning and the PyTorch library

Pre-trained networks

Tensors

The mechanics of learning

Using a neural network to fit data

Get a free copy for a limited time👇
https://lnkd.in/gGHeyst

He has been talking about this for a while :

https://www.youtube.com/watch?v=ph04b-poNr4&feature=youtu.be&fbclid=IwAR1g-SrVXbxzitP9DKIGCJO3tLDSngC9AqA8HY6AGAcpZUL3qANbJCzck9s

PyTorch 101
By Ayoosh Kathuria: https://blog.paperspace.com/pytorch-101-understanding-graphs-and-automatic-differentiation/
1. Understanding Graphs, Automatic Differentiation and Autograd
2. Building Your First Neural Network
3. Going Deep with PyTorch
4. Memory Management and Using Multiple GPUs
5. Understanding Hooks for debugging back pass

"Fast Task Inference with Variational Intrinsic Successor Features"
Hansen et al.: https://arxiv.org/abs/1906.05030
#DeepLearning #ReinforcementLearning #UnsupervisedLearning

Yoshua explains how Deep Learning has developed in 2019 (Video)
https://www.youtube.com/watch?v=eKMA1Tscdag&utm_content=buffer7ba5c&utm_medium=Social_Media+&utm_source=Linkedin&utm_campaign=Buffer_Linkedin_TRW

Fast Sparse ConvNets. https://arxiv.org/abs/1911.09723

RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. https://arxiv.org/abs/1911.09712

Object-Guided Instance Segmentation for Biological Images. https://arxiv.org/abs/1911.09199

Talking Head Anime from a Single Image

https://pkhungurn.github.io/talking-head-anime/

Richard Feynman, Winner of the 1965 Nobel Prize in Physics, gives us an insightful lecture about computer heuristics: how computers work, how they file information, how they handle data, how they use their information in allocated processing in a finite amount of time to solve problems and how they actually compute values of interest to human beings. These topics are essential in the study of what processes reduce the amount of work done in solving a particular problem in computers, giving them speeds of solving problems that can outmatch humans in certain fields but which have not yet reached the complexity of human driven intelligence. The question if human thought is a series of fixed processes that could be, in principle, imitated by a computer is a major theme of this lecture and, in Feynman's trademark style of teaching, gives us clear and yet very powerful answers for this field which has gone on to consume so much of our lives today. No doubt this lecture will be of crucial interest to anyone who has ever wondered about the process of human or machine thinking and if a synthesis between the two can be made without violating logic. ---

https://www.youtube.com/watch?v=ipRvjS7q1DI&fbclid=IwAR1ysEkCG2hcjuGw9TOZHMkOU35wSAOvXv6bEfEi4U8yPQiXKy0pUElLfnU