An Algorithmic Barrier to Neural Circuit Understanding
Venkatakrishnan Ramaswamy: https://www.biorxiv.org/content/10.1101/639724v1
#Algorithme #Neuroscience #innovation #technology

Five Functions of the Brain that are Inspiring #AI Research: https://towardsdatascience.com/five-functions-of-the-brain-that-are-inspiring-ai-research-2ba482ab8e2a
————
#BigData #MachineLearning #DataScience #Neuroscience #NeuralNetworks #Algorithms #DeepLearning

MRI Images Created by #AI Could Help Train #DeepLearning Models. https://healthitanalytics.com/news/mri-images-created-by-ai-could-help-train-deep-learning-models #BigData #Analytics #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #VueJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #NeuroScience

New #deeplearning paper at the intersection of #AI #mathematics #psychology and #neuroscience: A mathematical theory of semantic development in deep neural networks: arxiv.org/abs/1810.10531 https://t.me/ArtificialIntelligenceArticles

At the heart of most deep learning generalization bounds (VC, Rademacher, PAC-Bayes) is uniform convergence (u.c.). We argue why u. c. may be unable to provide a complete explanation of generalization, even if we take into account the implicit bias of SGD.

https://arxiv.org/pdf/1902.04742.pdf

https://t.me/ArtificialIntelligenceArticles

Looking to fall in love... with science? 😍 Help scientists train machines to study stroke lesions by swiping on our app:

https://braindrles.us/#/

#citizenscience #braindr #braindrles #neuroscience #machinelearning #swipesforscience #openscience #OHBM2019

Memorize-Generalize: An online algorithm for learning higher-order sequential structure with cloned Hidden Markov Models
Rikhye et al.: https://www.biorxiv.org/content/10.1101/764456v1
#Biology #Neuroscience #HiddenMarkovModels

Post-doc position in Deep learning and NLP at EMORY School of Medicine (Atlanta, USA)
Department of Biomedical Informatics at Emory School of Medicine is searching for a postdoctoral scholar. The Laboratory is led by Dr. Imon Banerjee (website), who is also affiliated with the Departments of Radiology and Biomedical Informatics at Emory University. The lab focuses on cutting‐edge research at the intersection of imaging science and biomedical informatics, developing and applying AI methods to large amounts of medical data for biomedical discovery, precision medicine, and precision health (early detection and prediction of future disease).



The postdoctoral scholar will be working on two core research topics: (1) develop foundational AI methods for analyzing and extracting information from clinical texts; (2) develop clinical prediction models using multi-modal and longitudinal electronic medical records (EMR) data. The scholar will deploy and evaluate these methods as clinical applications to transform medical care.



Requirements:



Post-graduate degree (PhD or MD, completed or near completion) in biomedical data science, informatics, computer science, engineering, statistics, computational biology, or a related field, with a background or interest in imaging



· Experience in machine learning and AI, particularly in computer vision and image analysis



· Strong record of distinguished scholarly achievement



· Outstanding communication and presentation skills with fluency in spoken and written English

https://t.me/ArtificialIntelligenceArticles

· Established record of distinguished scholarly achievement



Interested applicants should submit a Curriculum Vitae, a brief statement of research interests using this link: https://faculty-emory.icims.com/jobs/42390/job

Machine Learning Researcher / Computational Neuroscientist


https://jobs.apple.com/en-us/details/200104555/machine-learning-researcher-computational-neuroscientist

https://t.me/ArtificialIntelligenceArticles

AI meets physics - using artificial neural networks to approximate solutions of the three-body problem.


I'm increasingly intrigued by this paper (https://arxiv.org/pdf/1910.07291.pdf) showing the application of Artificial Neural networks to the infamously insoluble three-body problem in physics, where we try to work out the future position of three objects sometime in the future given Newton's equations of motion. I think it has important implications to how we think about approximation and how we achieve it in practice.

From the authors: "Our results provide evidence that, for computationally challenging regions of phase-space, a trained ANN can replace existing numerical solvers, enabling fast and scalable simulations of many-body systems to shed light on outstanding phenomena such as the formation of black-hole binary systems or the origin of the core collapse in dense star clusters."

https://t.me/ArtificialIntelligenceArticles