EdgeNet: Semantic Scene Completion from RGB-D images. arxiv.org/abs/1908.02893

3 open faculty positions in cognitive neuroscience at NYU
Dear colleagues,

The Department of Psychology in the Faculty of Arts and Science at New York University is searching to hire up to three new faculty members in Cognitive Neuroscience. One search will be open rank and up to two will be at the tenure-track assistant professor level.

We seek applicants with an outstanding record of research in human cognitive neuroscience, broadly construed. Preference will be given to applicants whose program of research includes advanced measurement techniques and computational approaches to understanding brain and behavior. Applicants that bridge between or complement the existing research strengths of the Psychology Department are encouraged.

The Faculty of Arts and Science at NYU is at the heart of a leading research university that spans the globe. We seek scholars of the highest caliber, who embody the diversity of the United States as well as the global society in which we live. Because broad diversity is essential for creating an inclusive climate, we are committed to the fair treatment of and equal access to opportunity and advancement for all, and will assess the many qualifications of all applicants. We strongly encourage applications from women, racial and ethnic minorities, and other individuals who are under-represented in the profession, across color, creed, race, ethnic and national origin, physical ability, gender and sexual identity, or any other legally protected basis. NYU affirms the value of differing perspectives on the world as we strive to build the strongest possible university with the widest reach. To learn more about the FAS commitment to diversity, equality and inclusion, please read here: https://as.nyu.edu/facultydiversity.html

Review of applications will begin October 1, 2019.

The electronic application should include a CV, statements of research (no more than three pages) and teaching interests (no more than two pages), copies of at least three representative publications, and at least three reference letters.

Diversity and inclusion are important parts of the NYU mission. Applicants should include a statement describing how your (1) scholarship, (2) teaching and mentoring, and/or (3) service and engagement demonstrate your commitment to diversity, equity, and inclusion. We are particularly interested in hearing about (1) concrete steps you have taken (or are planning to take) to foster an inclusive intellectual environment in your lab, in the classroom, in the department and on campus, and/or in your field more generally, and (2) how these steps connect with your broader views on the topics of diversity, equity, and inclusion.

Please address any questions to the Assistant to the Department Chair, Paulo Campos at [email protected].

"A machine learning algorithm claims to predict heart attacks and death from heart disease with a degree of accuracy beating human practitioners."

https://www.artificialintelligence-news.com/2019/05/14/ml-algorithm-predicts-heart-attacks/

What kind of medical innovations can we see in 2019? Also, is Google and Microsoft taking the lead?

https://t.ly/Zj3m9

Blog Post Version: https://bit.ly/31tFbRD

#Medical #ArtificialIntelligence #healthcare #HealthTech #opioids #opioidcrisis #Google #Microsoft #AI #money

Generation of dopaminergic or cortical neurons from neuronal progenitors

Chen et al.: https://zenodo.org/record/3361000#.XU9Lny0ZPUK

"Yes its tough to make neurons, but its doable (...)"
- Thomas Durcan, in: https://openlabnotebooks.org/years-and-years-of-dopaminergic-neurons/

#Neurons #CorticalNeurons #DopaminergicNeurons

Facebook launches online Global Pytorch Hackathon. $61,000 in prizes. Submissions due Sept 16th.


https://pytorch.devpost.com/

I had the pleasure of attending their in person hackathon at Menlo Park yesterday. If you want some inspiration for potential projects, checkout their submissions page here, they were really good.

https://pytorchmpk.devpost.com/submissions

Pytorch rolled a bunch of new features out a few days ago. They seem to be really stepping up in response to TF 2.0.

If you're looking for teammates, signup on the page, then you can look at other profiles of those looking for teammates

https://pytorch.devpost.com/participants

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Lee et al.: https://arxiv.org/abs/1902.06720
#DeepLearning #MachineLearning #NeuralNetworks

Geoffrey Hinton : Deep Learning—A Technology With the Potential to Transform Health Care by Geoffrey Hinton

https://jamanetwork.com/journals/jama/article-abstract/2701666

Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio
https://arxiv.org/abs/1908.02924

Artificial Intelligence — The Revolution Hasn’t Happened Yet
https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7

"Gauge Equivariant Convolutional Networks and the Icosahedral CNN"
Taco S. Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling : https://arxiv.org/abs/1902.04615
#ArtificialIntelligence #DeepLearning #MachineLearning

Training a Neural Network? Start here!

https://www.kaggle.com/lavanyashukla01/training-a-neural-network-start-here

Learning Vision-based Flight in Drone Swarms by Imitation. arxiv.org/abs/1908.02999

Local Differential Privacy for Deep Learning. arxiv.org/abs/1908.02997

Constrained domain adaptation for segmentation. arxiv.org/abs/1908.02996

Fast Point R-CNN. arxiv.org/abs/1908.02990

Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Base...) arxiv.org/abs/1908.02974

#SciPy2019 keynote: The New Era of NLP

link: https://www.youtube.com/watch?v=KChtdexd5Jo&list=PLtmWHNX-gukLQlMvtRJ19s7-8MrnRV6h6&index=4

Awesome Graph Classification -- A collection of graph classification methods, covering embedding, deep learning, graph kernel, and factorization papers with reference implementations
https://github.com/benedekrozemberczki/awesome-graph-classification

Machine Learning From Scratch

GitHub : https://github.com/eriklindernoren/ML-From-Scratch

#artificialintelligence #deeplearning #machinelearning