If you guys wanna get started with YOLO, take a look! YOLO is an extremely powerful object detection system.
https://becominghuman.ai/deep-learning-simplest-way-to-implement-yolo-an-extremely-powerful-object-detection-algorithm-9eae9d6191b7?gi=cc26e1486c94

Datasets: 23,000 NHS Doctor Jobs Postings
Download: https://www.kaggle.com/homelesssandwich/nhs-jobs

Learning Neural Causal Models from Unknown Interventions
Ke et al.: https://arxiv.org/abs/1910.01075
#AIDebate #MachineLearning #ArtificialIntelligence

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning. https://arxiv.org/abs/1912.08881

https://www.kcl.ac.uk/news/novel-method-to-identify-tumours-with-ai-in-development-by-school-researchers

Artificial Intelligence algorithms to provide surgeons with greater accuracy to delineate tumours

What was your favorite paper of 2019?
Feel free to add your choice with the paper url link
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch (https://arxiv.org/pdf/1909.01500.pdf)
SoftTriple Loss: Deep Metric Learning Without Triplet Sampling (https://arxiv.org/pdf/1909.05235.pdf)
Distributed Machine Learning on Mobile Devices: A Survey (https://arxiv.org/pdf/1909.08329v1.pdf)
Espresso: A Fast End-to-end Neural Speech Recognition Toolkit (https://arxiv.org/pdf/1909.08723v2.pdf)
MUSICNN: Pre-trained Convolutional Neural Networks for Music Audio Tagging (https://arxiv.org/pdf/1909.06654v1.pdf)
DeepPrivacy: A Generative Adversarial Network for Face Anonymization (https://arxiv.org/pdf/1909.04538v1.pdf)
BA-Net: Dense Bundle Adjustment Networks (https://openreview.net/pdf?id=B1gabhRcYX)
Momentum Contrast for Unsupervised Visual Representation Learning (https://arxiv.org/abs/1911.05722)

‘Cortex’: An open-source platform for deploying machine learning models as production web services
Github: https://github.com/cortexlabs/cortex
Tutorial: https://www.cortex.dev/iris-classifier
Examples: https://github.com/cortexlabs/cortex/tree/0.11/examples
https://www.marktechpost.com/2019/12/23/cortex-an-open-source-platform-for-deploying-machine-learning-models-as-production-web-services/

State of the art in deblurring and generating realistic high-resolution facial images
https://www.profillic.com/paper/arxiv:1912.10427

(An adversarial network comprising a generator and two discriminators is proposed)

Two Postdoc positions (m/f/d) in ‘Computational proteomics/deep learning’ and ‘Digital pathology/proteomics’
https://www.nature.com/naturecareers/job/two-postdoc-positions-mfd-in-computational-proteomicsdeep-learning-and-digital-pathologyproteomics-max-planck-institute-of-biochemistry-715743

https://t.iss.one/ArtificialIntelligenceArticles

Macaw: An Extensible Conversational Information Seeking Platform. https://arxiv.org/abs/1912.08904

Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning. https://arxiv.org/abs/1912.08881

Yann lecun : energy based self supervised learning

https://www.youtube.com/watch?v=A7AnCvYDQrU


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Videos of talks from #BlackinAI and #NeurIPS 2019, in case you missed it!

https://slideslive.com/neurips/neurips-2019-east-meeting-room-1-3-live?fbclid=IwAR0tEjCRVwRP6lXTYJNkNMHyffmyxlhot8X9LVi8fE6apTnIEMb1Wk77vwM

Shtetl-Optimized (entertaining & informative blog by theoretical computer scientist Scott Aaronson). NIPS vs. NeurIPS: guest post by Steven Pinker https://www.scottaaronson.com/blog/?p=4476

Best Machine Learning Research of 2019 https://opendatascience.com/best-machine-learning-research-of-2019/

Andrew Ng Interview with Father Of Deep Learning , Geoffrey Hinton |Andrew Ng and Geoffrey Hinton https://www.youtube.com/watch?v=089U2maCg0Q&feature=youtu.be&app=desktop