ParaQG: A System for Generating Questions and Answers from Paragraphs
Kumar et al.: https://arxiv.org/abs/1909.01642
#ArtificialIntelligence #Language #MachineLearning

Mathematics for Machine Learning with 75+ video tutorials, 8+ hours of FREE content.

[https://www.youtube.com/playlist?list=PLcQCwsZDEzFmlSc6levE3UV9rZ8yY-D_7](https://www.youtube.com/playlist?list=PLcQCwsZDEzFmlSc6levE3UV9rZ8yY-D_7)

Write With Transformer
Hugging Face released a new version of their Write With Transformer app, using a language model trained directly on Arxiv to generate Deep Learning and NLP completions!
In addition, they add state-of-the-art NLP models such as GPT, GPT-2 and XLNet completions:

https://transformer.huggingface.co/

H / T : Lysandre Debut
#Transformer #Pytorch #NLP

@ArtificialIntelligenceArticles

Deep Equilibrium Models
Bai et al.: https://arxiv.org/abs/1909.01377
Code: https://github.com/locuslab/deq
Video: https://youtube.com/watch?v=ZA5FpkYhkAI
#MachineLearning #DeepLearning #ArtificialIntelligence

Google Dataset Search

https://toolbox.google.com/datasetsearch

#artificialintelligence #deeplearning #machinelearning

Researchers have created the most detailed map of the mouse brain to date, capturing the projections and connections of over 1,000 neurons (and counting).
https://gfycat.com/limitedorganicleopardseal-rsciences

Press release describing the research available here.

Research article: https://www.cell.com/cell/fulltext/S0092-8674(19)30842-6

Cool tool, where you can browse the connectome: https://ml-neuronbrowser.janelia.org/


https://t.iss.one/ArtificialIntelligenceArticles

SqueezeNAS: Fast neural architecture search for faster semantic segmentation
https://export.arxiv.org/abs/1908.01748

PyTorch for Deep Learning with Python Bootcamp


https://couponportable.blogspot.com/2019/09/pytorch-for-deep-learning-with-python.html

Evolution Strategies
Blog by Lilian Weng : https://lilianweng.github.io/lil-log/2019/09/05/evolution-strategies.html
#Evolution #MachineLearning #ReinforcementLearning

How is AI re-architecting different industries? Watch Andrew's fireside chat with TechCrunch's Lucas Matney: https://hubs.ly/H0kF0hx0

Carnegie Mellon University, Accepted Papers at NeurIPS 2019

Learn more → https://mld.ai/neurips2019

#NeurIPS2019 #NeurIPS #MachineLearning #ML #ComputationalNeuroscience #Research #CarnegieMellon #CMUAI #mldcmu

ICYMI: Proponents of an AI technique called generative modeling see it as novel enough to be considered a “third way” of learning about the universe. By generating its own data, the algorithm identifies the most plausible explanation for observations about a physical system, without preprogrammed knowledge of the physical processes at work in that system. The technique is clearly powerful, but whether it truly represents a new approach to science is open to debate. https://www.quantamagazine.org/how-artificial-intelligence-is-changing-science-20190311/

AI For Filmmaking
Recognising Shot Types with ResNets
By Rahul Somani.
Project: https://rsomani95.github.io/ai-film-1.html
Code: https://github.com/rsomani95/shot-type-classifier
#ArtificialIntelligence #DeepLearning #MachineLearning

Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets
Geva et al.: https://arxiv.org/abs/1908.07898
#ArtificialIntelligence #MachineLearning #NLP

Distributionally Robust Language Modeling
Oren et al.: https://arxiv.org/abs/1909.02060
#ArtificialIntelligence #DeepLearning #MachineLearning

Postdoctoral Position (two years) in Optimization for Statistical Learning
The Department of Mathematics and Mathematical Statistics at Umeå University has an opening for a postdoctoral researcher in mathematical statistics with an emphasis on optimization for statistical learning. The appointment is for two years (subject to satisfactory performance), starting in Fall 2019. The successful candidate is expected to conduct excellent research, actively engage with collaborators, and to participate in the daily activities of the research environment. Last day to apply is September 30, 2019.

Background

The expansion of Artificial Intelligence (AI), in the broad sense, is one of the most exciting developments of the 21st century. This progress opens up many possibilities but also poses grand challenges. The centre Wallenberg AI, Autonomous Systems, and Software Program (WASP) is launching a program to develop the mathematical side of this area. The aim is to strengthen the competence of Sweden as a nation within the area of AI and we are taking part of this program through this specific project. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. For more information about the research and other activities conducted within WASP, please visit https://wasp-sweden.org/ .

Project description and tasks

Industrial robots, autonomous cars, stocks trading algorithms, and deep network assisted evaluation of medical images all crucially involve real-time, intelligent and automated decision making from complex and heterogeneous data, at ever growing scale and pace. This presents unprecedented theoretical and algorithmic challenges and opportunities in collecting and transforming data into information, predictions and intelligent decisions.

Optimization theory is vital to modern statistical learning and at the forefront of these advances, and the main objective of this postdoctoral position is to develop the next generation of optimization tools to address the above challenges in the context of modern statistical learning, and potentially explore their applications in AI, including medical imaging, automated quality control, and self-driving cars, evaluated on both simulated and real data.

Within this broad framework, the successful candidate is encouraged to develop their own research agenda, in close collaboration with mentors and colleagues. Potential areas of interest include, but not limited to

Training generative adversarial networks,

Nonconvex algorithms for linear inverse problems (such as compressive sensing),

Robust optimization and defense against adversarial examples in deep neural nets,

Role of over-parametrization in training and generalization of deep neural nets,

Global geometry of nonconvex problems,

Efficient and scalable algorithms for constrained nonconvex optimization,

Application of Langevin dynamics and other Monte-Carlo techniques in optimization

Online and storage-optimal algorithms for large scale convex optimization.

For more details and information on how to apply, please visit: https://umu.mynetworkglobal.com/en/what:job/jobID:284855/

Two-year statistics/machine learning postdoctoral position in Sheffield, UK
Dear all,

We are looking to recruit a postdoc for a two-year position in the School of Maths and Statistics and the Department of Computer Science at the University of Sheffield to work on an EPSRC funded project on 'Physically-informed probabilistic modelling of air pollution in Kampala using a low-cost sensor network'.

Closing date: 19 September 2019
Start date: As soon as possible.

https://jobs.shef.ac.uk/sap/bc/webdynpro/sap/hrrcf_a_posting_apply?PARAM=cG9zdF9pbnN0X2d1aWQ9Mzc1MDlCNkFBMDJFMUVEOUIwRTc4MEUxMDcxN0Q5RTkmY2FuZF90eXBlPUVYVA%3d%3d&sap-client=400&sap-language=EN&sap-accessibility=X&sap-ep-themeroot=%2fSAP%2fPUBLIC%2fBC%2fUR%2fuos#

This is a collaborative project between the University of Sheffield and the University of Makerere, and will develop and deploy machine learning methodology to analyse air pollution data from Kampala, in order to determine the source of the pollution and to aid the design of mitigating interventions.

The project team will consist of three Research Associates: two in Kampala and one in Sheffield and will provide methodological support to a larger project funded by a Google Impact Award.

The Sheffield research associate will be supervised by Professor Richard Wilkinson in the School of Mathematics and Statistics and Dr Mauricio Álvarez and Dr Michael Smith in the Department of Computer Science, and will focus on the development of the mathematical tools needed to incorporate physics into machine learning models, and on the development of inferential approaches for these models that are able to deal with large amounts of noisy data. In particular, we aim to develop Gaussian process models that incorporate domain-knowledge about diffusion and advection of air pollution.

We are looking for someone with a PhD in statistics or theoretical/computational physical sciences, ideally with some experience of machine learning.

This is an international multi-party collaboration and will involve travel to Kampala (approximately one trip per year). The project is funded by the EPSRC Global Challenges Research Fund (GCRF), which supports cutting-edge research to address the challenges faced by developing countries.

Best wishes,

Richard and Mauricio

Bayesian Inference of Networks Across Multiple Sample Groups and Data Types. https://arxiv.org/abs/1909.02058

Minimizing the Societal Cost of Credit Card Fraud with Limited and Imbalanced Data. https://arxiv.org/abs/1909.01486

AI Institute "Geometry of Deep Learning" 2019 [Workshop] Day 1 | Session 1

https://www.youtube.com/watch?list=PLD7HFcN7LXRe30qq36It2XCljxc340O_d&v=d9YJJH86ERw&fbclid=IwAR3YzYqnCHznayfiDE1kTH8QFwbljYh7b2v6FAP1_h75rvyEzJSnt_aUdcA&app=desktop

rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch
Adam Stooke and Pieter Abbeel : https://arxiv.org/abs/1909.01500
#DeepLearning #PyTorch #ReinforcementLearning