Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening

Deep learning #AI of > 1 M mammograms: "a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately."

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8861376

Securing machine learning models against adversarial attacks
https://www.elementai.com/news/2019/securing-machine-learning-models-against-adversarial-attacks

[Tutorial] Build a Gender Classifier for Live Webcam Stream using Tensorflow and OpenCV
https://hackernoon.com/gender-classifier-with-tensorflow-and-opencv-rg1ye3weq

Rachael Tatman - Put down the deep learning: When not to use neural networks and what to do instead
https://www.youtube.com/watch?time_continue=2&v=qw5dBdTXLEs

Activation maps for deep learning models in a few lines of code
https://www.kdnuggets.com/2019/10/activation-maps-deep-learning-models-lines-code.html

Working memory revived in older adults by synchronizing rhythmic brain circuits
https://www.nature.com/articles/s41593-019-0371-x.epdf

Cvpr 2019 paper

https://openaccess.thecvf.com/CVPR2019.py

The paper "Learning Predict-and-Simulate Policies From Unorganized Human Motion Data" is available here:
https://mrl.snu.ac.kr/publications/ProjectICC/ICC.html

Materials of the Summer school on Deep learning and Bayesian methods 2019
GitHub : https://github.com/bayesgroup/deepbayes-2019
#ArtificialIntelligence #DeepLearning #Bayesian

Machine Learning for Intelligent Systems: Cornell CS4780/CS5780

Lectures: https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS

Course Website: https://www.cs.cornell.edu/courses/cs4780/2018fa/syllabus/index.html

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Arora et al.: https://arxiv.org/abs/1910.01663
#RandomForests #MachineLearning #DeepLearning

The Illustrated GPT-2 (Visualizing Transformer Language Models)
Blog by Jay Alammar : https://jalammar.github.io/illustrated-gpt2/
#BERT #Transformer #ArtificialIntelligence

Critique of Paper by "Deep Learning Conspiracy" (Nature 521 p 436)

https://people.idsia.ch/~juergen/deep-learning-conspiracy.html

‎#deeplearning ‎#MachineLearning ‎#ArtificialIntelligence

Meta-Learning Deep Energy-Based Memory Models
Bartunov et al.: https://arxiv.org/abs/1910.02720
#MachineLearning #MetaLearning #EnergyBasedMemoryModels

Announcing NeurIPS Meetups!
By Neural Information Processing Systems Conference : https://medium.com/@NeurIPSConf/announcing-neurips-meetups-44b2385c67a2
#NeurIPS #Meetup #NeurIPS2019

GPyTorch
Gaussian processes for modern machine learning systems.
GitHub: https://github.com/cornellius-gp/gpytorch
Website: https://gpytorch.ai
#PyTorch #NeuralNetworks #MachineLearning

@ArtificialIntelligenceArticles

Deep learning method transforms shapes


https://www.sciencedaily.com/releases/2019/10/191018125516.htm

Information Closure Theory of Consciousness
https://arxiv.org/pdf/1909.13045.pdf

@ArtificialIntelligenceArticles

PhD positions in Deep Learning for Satellite Image Analysis at TU Berlin
The Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin, Germany is looking for highly motivated PhD candidates. The research of the PhD candidates will aim at developing innovative machine learning techniques (with a special focus on deep learning) for the analysis of big data from space.

The main topics include:
developing deep neural network models that can overcome the data imbalance problems for satellite image classification; and
developing active learning methods that are applicable to the designed deep neural networks.
The successful candidates will begin on January 2020 and will have a duration of 3 years. MSc degree is required in computer engineering or computer science with experience in computer vision, deep learning for image understanding. Very good command of German and English is required.

Interested candidates are requested to email their CVs to Prof. Begum Demir ([email protected]).

Multiple ML and AI Postdoc Opportunities at Northwestern University Feinberg School of Medicine
We are recruiting multiple postdoctoral fellows in the Division of Health and Biomedical Informatics at Northwestern University’s Feinberg School of Medicine. The postdoctoral fellow is expected to conduct research under guidance from Dr. Yuan Luo, Associate Professor and Chief AI Scientist, Northwestern University Clinical and Translational Sciences Institute. Our group website: https://labs.feinberg.northwestern.edu/lyg/. The fellow will also have the opportunities to work closely with top-notch clinicians from Northwestern Memorial Hospital, and strong supporting staff from Northwestern Medicine Enterprise Data Warehouse Team.

The successful candidate will have PhD in EECS, Biomedical Informatics, IEMS, Physics or related fields with solid programming skills. Experiences in some of the following areas are desirable: Machine Learning (ML) and/or Natural Language Processing (NLP) and/or time series analysis and/or -omic analysis. The candidate should demonstrate good communication skills and ability to work in a collaborative environment, to coordinate and supervise part of the research project.

We offer a competitive salary and an initial appointment of 12 months, starting 2019/2020. Extension of the postdoctoral position for up to 3 years is possible. Northwestern University is an exceptional research institution that has a world-class medical school and is an emerging hub in biomedical informatics; our department is located in downtown Chicago, one of the most vibrant cities in the US. Be part of a prestigious institution that offers great benefits, and enjoy our lakefront working environment.

Please send your application to Yuan Luo <[email protected]>, which should include:
- Curriculum vitae
- List of publications (attach a copy of one of your strongest papers)
- Contact details for 2 to 3 references

Northwestern University is an Equal Opportunity/Affirmative Action Employer.