Full Stack Deep Learning Bootcamp
(Most of) Lectures of Day 1: https://fullstackdeeplearning.com/march2019
Happy learning!
#ArtificialIntelligence #DeepLearning #MachineLearning

L2 Regularization and Batch Norm

Blog by David Wu: https://blog.janestreet.com/l2-regularization-and-batch-norm/

#artificialintelligence #datascience #machinelearning

A Repository of Conversational Datasets

https://arxiv.org/abs/1904.06472

Mini Course in Deep Learning with PyTorch for AIMS
https://github.com/Atcold/pytorch-Deep-Learning-Minicourse

Google releases massive visual databases for machine learning

https://www.datasciencecentral.com/profiles/blogs/google-releases-massive-visual-databases-for-machine-learning

Visual-Inertial Mapping with Non-Linear Factor Recovery. https://arxiv.org/abs/1904.06504

Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain https://arxiv.org/abs/1904.06490

YouTube UGC Dataset for Video Compression Research. https://arxiv.org/abs/1904.06457

Patch redundancy in images: a statistical testing framework and some applications. https://arxiv.org/abs/1904.06428

Towards Accurate One-Stage Object Detection with AP-Loss. https://arxiv.org/abs/1904.06373

The iWildCam 2018 Challenge Dataset. https://arxiv.org/abs/1904.05986

Best Paper Awards in Computer Science (since 1996)

A well maintained list: https://jeffhuang.com/best_paper_awards.html

#artificialintelligence #award #machinelearning #papers #research @ArtificialIntelligenceArticles

Disney trying to automate making an animation
https://arxiv.org/pdf/1904.05440.pdf

Visualizing Attention in Transformer-Based Language Representation Models
Jesse Vig: https://arxiv.org/abs/1904.02679
#ArtificialIntelligence #MachineLearning #NaturalLanguageProcessing

Natural Language Semantics With Pictures: Some Language & Vision Datasets and Potential U... https://arxiv.org/abs/1904.07318

A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection. https://arxiv.org/abs/1904.07312

Brain Tumor Segmentation on MRI with Missing Modalities. https://arxiv.org/abs/1904.07290

entureBeat article on the singing voice conversion system developed at FAIR-Tel Aviv.
This can transform someone's singing voice into someone else's voice

https://venturebeat.com/2019/04/16/facebooks-ai-can-convert-one-singers-voice-into-another/

Ian Goodfellow: Generative Adversarial Networks (GANs)
@ArtificialIntelligenceArticles
This conversation with Ian led me to rethink the way I see several basic ideas in deep learning, including generative models, adversarial learning, and reasoning. I definitely enjoyed it and hope you do as well.



https://www.youtube.com/watch?v=Z6rxFNMGdn0&fbclid=IwAR1P-iKyeed3-8GziQp59ZxPBkHZ9yX1fK1slAUK1MIbQJPK7cy0exDzTQ8

https://t.iss.one/ArtificialIntelligenceArticles

Standards for AI Governance: International Standards to Enable Global Coordination in AI Research & Development
By Peter Cihon,Research Affiliate, Center for the Governance of AI Future of Humanity Institute, University of Oxford: https://www.fhi.ox.ac.uk/wp-content/uploads/Standards_-FHI-Technical-Report.pdf
#ArtificialIntelligence

Datasheets for Datasets

https://arxiv.org/abs/1803.09010
#Databases #ArtificialIntelligence #AIEthics #Ethics #MachineLearning