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
GPT-2: 1.5B Release
OpenAI : https://openai.com/blog/gpt-2-1-5b-release/
#ArtificialIntelligence #DeepLearning #MachineLearning
OpenAI : https://openai.com/blog/gpt-2-1-5b-release/
#ArtificialIntelligence #DeepLearning #MachineLearning
The human brain can rewire itself after a traumatic bodily injury, researchers report. Similar findings have been previously reported in animal studies, but this is one of the first studies where such a result has been documented in people.
https://news.missouri.edu/2019/talk-to-the-hand/
https://news.missouri.edu/2019/talk-to-the-hand/
news.missouri.edu
Talk to the hand
MU researchers find human brain can rewire itself after a traumatic bodily injury
Do Deep Neural Networks ‘See’ Faces Like Brains Do?
https://medium.com/syncedreview/do-deep-neural-networks-see-faces-like-brains-do-dc3eef334d79
https://medium.com/syncedreview/do-deep-neural-networks-see-faces-like-brains-do-dc3eef334d79
Medium
Do Deep Neural Networks ‘See’ Faces Like Brains Do?
Recognizing faces is as natural and habitual as can be for human beings. Even with their undeveloped vision, babies can recognize their…
A deeper dive into UMAP theory
Andy Coenen, Adam Pearce : https://pair-code.github.io/understanding-umap/supplement.html
#ArtificialIntelligence #DeepLearning #MachineLearning
Andy Coenen, Adam Pearce : https://pair-code.github.io/understanding-umap/supplement.html
#ArtificialIntelligence #DeepLearning #MachineLearning
pair-code.github.io
Understanding UMAP
UMAP is a new dimensionality reduction technique that offers increased speed and better preservation of global structure.
When Artificial Intelligence gets FUNNY with an ability to detect humour & predict LAUGHTER using multimodal language dataset, named UR-FUNNY.
It demonstrated the role of context & punchline in humour detection using TED Talk transcripts with laughter cues for humour analysis.
#EMNLP2019
Read: https://arxiv.org/pdf/1904.06618.pdf
GitHub: https://github.com/ROC-HCI/UR-FUNNY
It demonstrated the role of context & punchline in humour detection using TED Talk transcripts with laughter cues for humour analysis.
#EMNLP2019
Read: https://arxiv.org/pdf/1904.06618.pdf
GitHub: https://github.com/ROC-HCI/UR-FUNNY
GitHub
GitHub - ROC-HCI/UR-FUNNY: This repository presents UR-FUNNY dataset: first dataset for multimodal humor detection
This repository presents UR-FUNNY dataset: first dataset for multimodal humor detection - GitHub - ROC-HCI/UR-FUNNY: This repository presents UR-FUNNY dataset: first dataset for multimodal humor...
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Rather than hardcoding forward prediction, we try to get agents to *learn* that they need to predict the future.
Check out our #NeurIPS2019 paper!
https://learningtopredict.github.io
https://arxiv.org/abs/1910.13038
Rather than hardcoding forward prediction, we try to get agents to *learn* that they need to predict the future.
Check out our #NeurIPS2019 paper!
https://learningtopredict.github.io
https://arxiv.org/abs/1910.13038
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
Learning to Predict Without Looking Ahead
World Models Without Forward Prediction
NeurIPS 2019: Adversarial music that prevents Amazon Alexa from waking up
https://www.profillic.com/paper/arxiv:1911.00126
They target the attack on the wake-word detection system, jamming the model with some inconspicuous background music to deactivate the VAs while the audio adversary is present
https://www.profillic.com/paper/arxiv:1911.00126
They target the attack on the wake-word detection system, jamming the model with some inconspicuous background music to deactivate the VAs while the audio adversary is present
Profillic
Adversarial Music: Real World Audio Adversary Against Wake-word Detection System - Profillic
Explore state-of-the-art in machine learning, AI, and robotics. Browse models, source code, papers by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language processing, robotics…
TOP AI & MACHINE LEARNING RESEARCH PAPERS FROM 2019
https://www.topbots.com/top-ml-research-papers-2019/
https://www.topbots.com/top-ml-research-papers-2019/
TOPBOTS
Top AI & Machine Learning Research Papers From 2019
UPDATE: We’ve also summarized the top 2020 AI & machine learning research papers. With the AI industry moving so quickly, it’s difficult for ML practitioners to find the time to curate, analyze, and implement new research being published. To help you quickly…
Query2vec: Search query expansion with query embeddings
https://bytes.grubhub.com/search-query-embeddings-using-query2vec-f5931df27d79
https://bytes.grubhub.com/search-query-embeddings-using-query2vec-f5931df27d79
Medium
Query2vec: Search query expansion with query embeddings
Discovery and understanding of a product catalog is an important part of any e-commerce business. The traditional — and difficult — method…
The Measure of Intelligence
François Chollet : https://arxiv.org/abs/1911.01547
GitHub : https://github.com/fchollet/ARC
#ArtificialIntelligence #DeepLearning #MachineLearning
François Chollet : https://arxiv.org/abs/1911.01547
GitHub : https://github.com/fchollet/ARC
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
On the Measure of Intelligence
To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate...
Dynamics-Aware Unsupervised Discovery of Skills
Sharma et al.: https://arxiv.org/abs/1907.01657
#MachineLearning #Robotics #ReinforcementLearning
Sharma et al.: https://arxiv.org/abs/1907.01657
#MachineLearning #Robotics #ReinforcementLearning
arXiv.org
Dynamics-Aware Unsupervised Discovery of Skills
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a...