ICCV 2019 and CoRL 2019 Announce Best Papers; DeepMind AlphaStar Reaches ‘Grandmaster Level’
https://www.google.com/amp/s/syncedreview.com/2019/11/03/iccv-2019-and-corl-2019-announce-best-papers-deepmind-alphastar-reaches-grandmaster-level/amp/
https://www.google.com/amp/s/syncedreview.com/2019/11/03/iccv-2019-and-corl-2019-announce-best-papers-deepmind-alphastar-reaches-grandmaster-level/amp/
Synced
ICCV 2019 and CoRL 2019 Announce Best Papers; DeepMind AlphaStar Reaches ‘Grandmaster Level’
Synced Global AI Weekly November 3rd
Mixed Pooling Multi-View Attention Autoencoder for Representation Learning in Healthcare. https://arxiv.org/abs/1910.06456
Supercomputer analyzes web traffic across entire internet
https://news.mit.edu/2019/supercomputer-analyzes-web-traffic-across-entire-internet-1028
https://news.mit.edu/2019/supercomputer-analyzes-web-traffic-across-entire-internet-1028
MIT News | Massachusetts Institute of Technology
Supercomputer analyzes web traffic across entire internet
Using the MIT SuperCloud and the MIT Lincoln Laboratory Supercomputing Center, researchers have developed a model that captures what web traffic looks like around the world on a given day, to be used as a measurement tool for internet and network research.
Generalization through Memorization: Nearest Neighbor Language Models
Khandelwal et al.: https://arxiv.org/abs/1911.00172
#ArtificialIntelligence #DeepLearning #NeuralNetworks
Khandelwal et al.: https://arxiv.org/abs/1911.00172
#ArtificialIntelligence #DeepLearning #NeuralNetworks
arXiv.org
Generalization through Memorization: Nearest Neighbor Language Models
We introduce $k$NN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a $k$-nearest neighbors ($k$NN) model. The nearest neighbors are computed according...
Learning Neural Networks with Adaptive Regularization
Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon : https://arxiv.org/abs/1907.06288
Code: https://github.com/yaohungt/Adaptive-Regularization-Neural-Network
#ArtificialIntelligence #NeuralNetworks #NeurIPS2019
Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Geoffrey J. Gordon : https://arxiv.org/abs/1907.06288
Code: https://github.com/yaohungt/Adaptive-Regularization-Neural-Network
#ArtificialIntelligence #NeuralNetworks #NeurIPS2019
arXiv.org
Learning Neural Networks with Adaptive Regularization
Feed-forward neural networks can be understood as a combination of an
intermediate representation and a linear hypothesis. While most previous works
aim to diversify the representations, we...
intermediate representation and a linear hypothesis. While most previous works
aim to diversify the representations, we...
Seeing What a GAN Cannot Generate
Bau et al.: https://arxiv.org/abs/1910.11626
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
Bau et al.: https://arxiv.org/abs/1910.11626
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks
Integer Discrete Flows and Lossless Compression
Hoogeboom et al.: https://arxiv.org/abs/1905.07376
#Artificialintelligence #DeepLearning #MachineLearning
Hoogeboom et al.: https://arxiv.org/abs/1905.07376
#Artificialintelligence #DeepLearning #MachineLearning
"Differentiable Convex Optimization Layers"
CVXPY creates powerful new PyTorch and TensorFlow layers
Agrawal et al.: https://locuslab.github.io/2019-10-28-cvxpylayers/
#PyTorch #TensorFlow #NeurIPS2019
CVXPY creates powerful new PyTorch and TensorFlow layers
Agrawal et al.: https://locuslab.github.io/2019-10-28-cvxpylayers/
#PyTorch #TensorFlow #NeurIPS2019
locuslab.github.io
Differentiable Convex Optimization Layers
CVXPY creates powerful new PyTorch and TensorFlow layers
Deep Fundamental Matrix Estimation
https://vladlen.info/publications/deep-fundamental-matrix-estimation/
Paper- Code
https://vladlen.info/publications/deep-fundamental-matrix-estimation/
Paper- Code
The future belongs to Artificial Intelligence. Read this empirical research from the MIT-IBM Watson AI Lab on how tasks are reorganizing between people and machines as a result of AI and new technologies.
The report analyzes 170 million online job postings 2010-2017.
Read https://mitibmwatsonailab.mit.edu/research/publications/paper/download/The-Future-of-Work-How-New-Technologies-Are-Transforming-Tasks.pdf
The report analyzes 170 million online job postings 2010-2017.
Read https://mitibmwatsonailab.mit.edu/research/publications/paper/download/The-Future-of-Work-How-New-Technologies-Are-Transforming-Tasks.pdf
Research Associates in Federated and Adversarial Machine Learning at Imperial College London
The Resilient Information Systems Security Group (RISS) in the Department of Computing at Imperial College London is seeking a Research Assistant/Associate to work on EU funded Musketeer project. Musketeer aims to create a federated and privacy preserving machine learning data platform, that is interoperable, efficient and robust against internal and external threats. Led by IBM the project involves 11 academic and industrial partners from 7 countries and will validate its findings in two industrial scenarios in smart manufacturing and health care. Further details about the project can be found at: www.musketeer.eu
The main contribution of the RISS group to Musketeer project focuses on the investigation and development of federated machine learning algorithms robust against attacks at training and test time, including the investigation of new poisoning attack and defence strategies, as well as novel mechanisms to generate adversarial examples and mitigate their effects. The work also includes the analysis of scenarios where multiple malicious users collude to manipulate or degrade the performance of federated machine learning systems.
There will be opportunities to collaborate with other researchers and PhD students in the RISS group working on adversarial machine learning and other machine learning applications in the security domain.
To apply for this position, you will need to have a strong machine learning background with proven knowledge and track record in one or more of the following research areas and techniques:
Adversarial machine learning.
Robust machine learning.
Federated or distributed machine learning.
Deep learning.
Bayesian inference.
Research Assistant applicants will have a Master’s degree (or equivalent) in an area pertinent to the subject area, i.e., Computing or Engineering. Research Associate applicants will have a PhD degree (or equivalent) in an area pertinent to the subject area, i.e., Computing or Engineering.
You must have excellent verbal and written communication skills, enjoy working in collaboratively and be able to organise your own work with minimal supervision and prioritise work to meet deadlines. Preference will be given to applicants with a proven research record and publications in the relevant areas, including in prestigious machine learning and security journals and conferences.
The post is based in the Department of Computing at Imperial College London on the South Kensington Campus. The post holder will be required to travel occasionally to attend project meetings and to work collaboratively with the project partners.
*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £35,477 - £38,566 per annum.
How to apply:
Please complete our online application by visiting https://www.imperial.ac.uk/jobs/ and search using vacancy reference number ENG00916.
Applications must include the following:
A full CV and list of publications
A 1 page statement outlining why you think you would be ideal for this post.
Research Assistant salary in the range: £35,477 to £38,566 per annum*
Research Associate salary in the range: £40,215 to £47,579 per annum
Full Time, Fixed Term appointment for to start ASAP until the 31/11/2021
Should you have any queries regarding the application process please contact Jamie Perrins via [email protected]
Informal Enquiries can be addressed to Professor Emil Lupu ([email protected])
Closing Date 1st December 2019
The Resilient Information Systems Security Group (RISS) in the Department of Computing at Imperial College London is seeking a Research Assistant/Associate to work on EU funded Musketeer project. Musketeer aims to create a federated and privacy preserving machine learning data platform, that is interoperable, efficient and robust against internal and external threats. Led by IBM the project involves 11 academic and industrial partners from 7 countries and will validate its findings in two industrial scenarios in smart manufacturing and health care. Further details about the project can be found at: www.musketeer.eu
The main contribution of the RISS group to Musketeer project focuses on the investigation and development of federated machine learning algorithms robust against attacks at training and test time, including the investigation of new poisoning attack and defence strategies, as well as novel mechanisms to generate adversarial examples and mitigate their effects. The work also includes the analysis of scenarios where multiple malicious users collude to manipulate or degrade the performance of federated machine learning systems.
There will be opportunities to collaborate with other researchers and PhD students in the RISS group working on adversarial machine learning and other machine learning applications in the security domain.
To apply for this position, you will need to have a strong machine learning background with proven knowledge and track record in one or more of the following research areas and techniques:
Adversarial machine learning.
Robust machine learning.
Federated or distributed machine learning.
Deep learning.
Bayesian inference.
Research Assistant applicants will have a Master’s degree (or equivalent) in an area pertinent to the subject area, i.e., Computing or Engineering. Research Associate applicants will have a PhD degree (or equivalent) in an area pertinent to the subject area, i.e., Computing or Engineering.
You must have excellent verbal and written communication skills, enjoy working in collaboratively and be able to organise your own work with minimal supervision and prioritise work to meet deadlines. Preference will be given to applicants with a proven research record and publications in the relevant areas, including in prestigious machine learning and security journals and conferences.
The post is based in the Department of Computing at Imperial College London on the South Kensington Campus. The post holder will be required to travel occasionally to attend project meetings and to work collaboratively with the project partners.
*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £35,477 - £38,566 per annum.
How to apply:
Please complete our online application by visiting https://www.imperial.ac.uk/jobs/ and search using vacancy reference number ENG00916.
Applications must include the following:
A full CV and list of publications
A 1 page statement outlining why you think you would be ideal for this post.
Research Assistant salary in the range: £35,477 to £38,566 per annum*
Research Associate salary in the range: £40,215 to £47,579 per annum
Full Time, Fixed Term appointment for to start ASAP until the 31/11/2021
Should you have any queries regarding the application process please contact Jamie Perrins via [email protected]
Informal Enquiries can be addressed to Professor Emil Lupu ([email protected])
Closing Date 1st December 2019
10 Machine Learning Methods that Every Data Scientist Should Know
https://towardsdatascience.com/10-machine-learning-methods-that-every-data-scientist-should-know-3cc96e0eeee9
https://towardsdatascience.com/10-machine-learning-methods-that-every-data-scientist-should-know-3cc96e0eeee9
Medium
10 Machine Learning Methods that Every Data Scientist Should Know
Jump-start your data science skills
There is updated version of CS224n which uses pyTorch instead of tf and other updated resources. CS224N: Natural Language Processing with Deep Learning | Winter 2019:
https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z
https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z
YouTube
Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai