Anomaly Detection - Another Challenge for Artificial Intelligence


https://www.experfy.com/blog/anomaly-detection-another-challenge-for-artificial-intelligence

Newtonian Monte Carlo: a second-order gradient method for speeding up MCMC
https://openreview.net/forum?id=SklKcJ3EFH

"Top 100 most discussed academic papers" (across all fields) this year:


Explore the list of the most discussed and shared research of 2019. The world’s climate emergency, vaccinations, and developments in AI all feature heavily in this year’s list. https://www.altmetric.com/top100/ #AltmetricTop100 https://t.iss.one/ArtificialIntelligenceArticles

Neuroscience Methods Tutorial
https://www.youtube.com/watch?v=iHthMSN65bA&fbclid=IwAR0kutS1dI_s51Y8bPoCjGLhOnz-x74ELOcSVNWROvtyU6lRaEHsoHBElcQ

You've spent hours studying AI and building projects. What's the next step? This report from workera company, walks you through the different AI career tracks and the skills recruiters are looking for. Download the report:

https://workera.ai/candidates/report/

Oktoberfest Food Dataset
The data was aquired during Schanzer Almfest at Ingolstadt in 2018 by IlassAG.
Github: https://github.com/a1302z/OktoberfestFoodDataset
Paper: https://arxiv.org/pdf/1912.05007.pdf
Find The Most Updated and Free Datasets of Different Industries including Health, Insurance, Banking etc.https://www.marktechpost.com/data-sets/

An interesting new algorithm from DeepMind that aligns agent behaviour with a user's objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function and unknown unsafe states.

Paper: https://arxiv.org/abs/1912.05652
Code: https://github.com/rddy/ReQueST

https://medium.com/@xamat/the-year-in-ai-2019-ml-ai-advances-recap-c6cc1d902d5 https://t.iss.one/ArtificialIntelligenceArticles

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
Such et al.: https://arxiv.org/abs/1912.07768
#ArtificialIntelligence #MachineLearning #NeuralNetworks

Network of Evolvable Neural Units: Evolving to Learn at a Synaptic Level
Paul Bertens, Seong-Whan Lee : https://arxiv.org/abs/1912.07589
#NeuralComputing #MachineLearning #ArtificialIntelligence

PolSF: PolSAR image dataset on San Francisco
Github: https://github.com/liuxuvip/PolSF
Paper: https://arxiv.org/pdf/1912.07259v1.pdf

A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern
Czarnecki et al.: https://arxiv.org/abs/1912.07559
#ArtificialIntelligence #DeepLearning #MachineLearning

PolSF: PolSAR image dataset on San Francisco
Github: https://github.com/liuxuvip/PolSF
Paper: https://arxiv.org/pdf/1912.07259v1.pdf

Love science research? Here's an interesting new service from IBM that produces summaries of Science papers based on information expressed through natural language queries.

White paper: https://arxiv.org/abs/1908.11152
Product: https://dimsum.eu-gb.containers.appdomain.cloud/

Searching for resources to put your deep learning model into production? Check this up: https://github.com/ahkarami/Deep-Learning-in-Production

It includes a comprehensive list of tutorials on :
a) How to convert PyTorch Models in Production
b) How to convert PyTorch Models to C++
c) How to deploy TensorFlow Models in Production
d) How to convert Keras Models in Production
e) How to deploy MXNet Models in Production

With additional tutorial list on:
a) Model Conversion between Deep Learning Frameworks
b) Caffe2
c) Resources for Designing UI (Front-End Development)
d) Mobile app Development
e) Back-End Development Part
f) GPU Management Libraries
g) Speed-up & Scalabale Python Codes

Keep Learning!!

Best of arXiv.org for AI, Machine Learning, and Deep Learning – November 2019
https://insidebigdata.com/2019/12/18/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-november-2019/

The Neuroscience of Consciousness - Interview with Christof Koch, Chief Scientist of The Allen Institute for Brain Science
https://www.youtube.com/watch?v=NsvEKyzRXdY&feature=youtu.be

From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

https://papers.nips.cc/paper/9060-from-deep-learning-to-mechanistic-understanding-in-neuroscience-the-structure-of-retinal-prediction


#DeepLearning #Neuroscience #NeurIPS2019

Interrogating theoretical models of neural computation with deep inference
Bittner et al.: https://www.biorxiv.org/content/10.1101/837567v2
#Neuroscience

What is adversarial machine learning, and how is it used today?

-Generative modeling, security, model-based optimization, neuroscience, fairness, and more!

Here's a fantastic video overview by Ian Goodfellow.
https://videos.re-work.co/videos/1351-ian-goodfellow
#ML #adversarialML #AI #datascience

A great summary of all the invited talks, presentations, posters and workshops at this years Neural Information Processing Systems Conference (NeurIPS2019) happened in Vancouver last week. Thank you Chip Huyen https://huyenchip.com/2019/12/18/key-trends-neurips-2019.html
https://t.iss.one/ArtificialIntelligenceArticles