I've been thinking a bit about the growing practice of fine-tuning generic pretrained models: first in computer vision, now NLP (highly recommend Sebastian Ruders great article on this https://ruder.io/nlp-imagenet/ )...Last time I mentioned this, people were skeptical that RL would be next.
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Death by algorithm: the age of killer robots is closer than you think
🔗 https://www.vox.com/2019/6/21/18691459/killer-robots-lethal-autonomous-weapons-ai-war
#machinelearning

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Machine Learning Zero to Hero
☞ https://bit.ly/2QCtsMj
#ai #tensorflow

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All materials of berkeley ai Deep Unsupervised Learning now up:
https://sites.google.com/view/berkeley-cs294-158-sp19/home

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Using #AI To Analyze Video As Imagery: The Impact Of Sampling Rate
https://buff.ly/2IBFRO8
#ArtificialIntelligence #MachineLearning #DeepLearning #robotics

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Bayesian Optimization with Binary Auxiliary Information
https://deepai.org/publication/bayesian-optimization-with-binary-auxiliary-information … by Yehong Zhang et al.
#ReinforcementLearning #Hyperparameter

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Best Paper Finalist (top 1% of accepted papers) Check it out!
https://openaccess.thecvf.com/content_CVPR_2019/html/Ribera_Locating_Objects_Without_Bounding_Boxes_CVPR_2019_paper.html

Comparison of different #MachineLearning approaches for neuroimaging data
Main take-aways - prediction accuracy increased once N ≥ 400
- Substantial effect of pipeline on accuracies: Is this the new p-hacking?
https://buff.ly/2NdaJcv

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Theory of the Frequency Principle for General Deep Neural Networks.
https://arxiv.org/abs/1906.09235
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Artificial Intelligence can write creative & convincingly human-like captions for any image. Great work by IBM Research at #cvpr2019 In order to ensure the generated captions did not sound too unnatural, the work employed conditional GAN training Read
https://arxiv.org/pdf/1805.00063.pdf

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#Machineearning for Everyone
https://bit.ly/2RvRRnj

#AI #ML #DataScience #Algorithms

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A Gentle Introduction to Upsampling and Transpose Convolution Layers for GANs

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images

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The #xtensor article series continues! Learn everything about xtensor constructors and initializer lists in

https://medium.com/@johan.mabille/how-we-wrote-xtensor-3-n-the-constructors-65a177260638


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This is incredible. This paper from MIT Computer Science & Artificial Intelligence Lab presented at #cvpr2019 shows how to reconstruct a face from speech patterns.
https://speech2face.github.io

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Using #DeepLearning to produce an #AutonomousSystem for detecting traffic signs on Google Street View images. The system could help to monitor street signs and identify those in need of replacement or repair.
Read: https://ow.ly/WW4630oZzJu

https://doi.org/10.1016/j.compenvurbsys.2019.101350

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All the datasets (there are a lot) released at #cvpr2019 are now indexed in
https://visualdata.io . Check them out!
#computervision #machinelearning #dataset

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summary of interesting optimization papers from ICLR 2019:
part I :
https://medium.com/@yaroslavvb/iclr-optimization-papers-i-fluctuation-dissipation-relations-for-sgd-a638ad9964cc
part II
https://medium.com/@yaroslavvb/iclr-optimization-papers-ii-44f03b98dc5f?postPublishedType=repub
part III
https://medium.com/@yaroslavvb/iclr-optimization-papers-iii-1e1edc050ba6

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decentralized decision making is out on arxiv: "Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives".
Link: https://arxiv.org/abs/1906.10667 .

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Look at this amazing collection of resources for teaching reproducible research to university students! 👨‍🎓.
https://guides.lib.uw.edu/research/reproducibility/teaching

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