AIM 2019 Challenge on Image Demoireing: Methods and Results. https://arxiv.org/abs/1911.03461
Towards Domain Adaptation from Limited Data for Question Answering Using Deep Neural Netw... https://arxiv.org/abs/1911.02655
Worst Cases Policy Gradients
Yichuan Charlie Tang, Jian Zhang, Ruslan Salakhutdinov : https://arxiv.org/abs/1911.03618
#MachineLearning #DeepLearning #ArtificialIntelligence
Yichuan Charlie Tang, Jian Zhang, Ruslan Salakhutdinov : https://arxiv.org/abs/1911.03618
#MachineLearning #DeepLearning #ArtificialIntelligence
Teaching a neural network to use a calculator
Blog by Reiichiro Nakano : https://reiinakano.com/2019/11/12/solving-probability.html
#ArtificialIntelligence #DeepLearning #MachineLearning
Blog by Reiichiro Nakano : https://reiinakano.com/2019/11/12/solving-probability.html
#ArtificialIntelligence #DeepLearning #MachineLearning
reiinakano’s blog
Teaching a neural network to use a calculator
This article explores a seq2seq architecture for solving simple probability problems in Deepmind’s Mathematics Dataset. A transformer is used to map questions to intermediate steps, while an external symbolic calculator evaluates intermediate expressions.…
How to Read Articles That Use Machine Learning
Users’ Guides to the Medical Literature
https://jamanetwork.com/journals/jama/article-abstract/2754798
Users’ Guides to the Medical Literature
https://jamanetwork.com/journals/jama/article-abstract/2754798
Jamanetwork
How to Read Articles That Use Machine Learning
This Users’ Guide to the Medical Literature discusses the use of machine learning models as a diagnostic tool, and it explains the important steps needed for making these models and the outcomes they derive clinically effective.
Geometry-Aware Neural Rendering
Josh Tobin, OpenAI Robotics, Pieter Abbeel : https://arxiv.org/abs/1911.04554
#DeepLearning #MachineLearning #NeurIPS2019
Josh Tobin, OpenAI Robotics, Pieter Abbeel : https://arxiv.org/abs/1911.04554
#DeepLearning #MachineLearning #NeurIPS2019
"SEMINAL DEBATE : Yoshua Bengio | Gary Marcus"
This Is The Debate The AI World Has Been Waiting For
LIVE STREAMING : https://bengio-marcus.eventbrite.ca
Date and Time : December 23, 2019 | 7:00 PM – 8:30 PM EST
#ArtificialIntelligence #Debate
This Is The Debate The AI World Has Been Waiting For
LIVE STREAMING : https://bengio-marcus.eventbrite.ca
Date and Time : December 23, 2019 | 7:00 PM – 8:30 PM EST
#ArtificialIntelligence #Debate
Scientists have found that a drug called rapamycin can help prevent dementia by restoring blood flow to the brain that is lost during aging.
https://www.labroots.com/trending/cell-and-molecular-biology/16097/rapamycin-prevent-age-related-memory-loss-cerebral-blood-flow
https://www.labroots.com/trending/cell-and-molecular-biology/16097/rapamycin-prevent-age-related-memory-loss-cerebral-blood-flow
LabRoots
Rapamycin Can Prevent Age-related Memory Problems and Loss of Cerebral Blood Flow | Cell And Molecular Biology
Scientists have found that a drug called rapamycin can help restore blood flow to the brain that is lost during aging. | Cell And Molecular Biology
"Gatsby Unit PhD: Training in theoretical and computational neuroscience and machine learning.
Deadline: 17th November 2019
The Gatsby Computational Neuroscience Unit is a leading research centre focused on theoretical neuroscience and machine learning. We study unsupervised, supervised and reinforcement learning in brains and machines; inference, coding and neural dynamics; Bayesian and kernel methods, and deep learning; with applications to the analysis of perceptual processing and cognition, neural data, signal and image processing, machine vision, network data and nonparametric hypothesis testing.
The Unit provides a unique opportunity for a critical mass of theoreticians to interact closely with each other, with the Sainsbury Wellcome Centre for Neural Circuits and Behaviour (SWC, with which we share a purpose-designed building), with the cross-faculty Centre for Computational Statistics and Machine Learning (CSML), and with other world-class research groups in related departments at UCL including: Computer Science; Functional Imaging; Neuroscience, Physiology and Pharmacology; Psychology; Neurology; Ophthalmology; The Ear Institute; Statistical Science; and the nearby Alan Turing and Francis Crick Institutes.
Students at the Gatsby Unit complete a four-year PhD in either machine learning or theoretical neuroscience, with minor emphasis in the complementary field. Courses in the first year, taught in conjunction with colleagues from the SWC and CSML, provide a comprehensive and intensive introduction to both fields. Students are encouraged to work and interact closely with peers and faculty in the SWC and/or CSML throughout their PhD, providing a uniquely multidisciplinary research environment.
Applicants should have a strong analytical and quantitative background, a keen interest in neuroscience, machine learning or both, and a relevant first degree, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics.
Full funding is available regardless of nationality and current residence. The Unit also welcomes applicants who have secured or are seeking funding from other sources.
Applications (including a CV, transcripts, statement of research interests, and letters from three referees) should be sent directly to [email protected]. Only applications complete by the deadline are guaranteed full consideration; late applications will be entertained only if places remain unfilled.
For further details of our programme and how to apply please see:
https://www.gatsby.ucl.ac.uk/teaching/phd
Further details of research interests are available from
https://www.gatsby.ucl.ac.uk/research.html
and the individual faculty webpages at
https://www.gatsby.ucl.ac.uk/Members.html."
Deadline: 17th November 2019
The Gatsby Computational Neuroscience Unit is a leading research centre focused on theoretical neuroscience and machine learning. We study unsupervised, supervised and reinforcement learning in brains and machines; inference, coding and neural dynamics; Bayesian and kernel methods, and deep learning; with applications to the analysis of perceptual processing and cognition, neural data, signal and image processing, machine vision, network data and nonparametric hypothesis testing.
The Unit provides a unique opportunity for a critical mass of theoreticians to interact closely with each other, with the Sainsbury Wellcome Centre for Neural Circuits and Behaviour (SWC, with which we share a purpose-designed building), with the cross-faculty Centre for Computational Statistics and Machine Learning (CSML), and with other world-class research groups in related departments at UCL including: Computer Science; Functional Imaging; Neuroscience, Physiology and Pharmacology; Psychology; Neurology; Ophthalmology; The Ear Institute; Statistical Science; and the nearby Alan Turing and Francis Crick Institutes.
Students at the Gatsby Unit complete a four-year PhD in either machine learning or theoretical neuroscience, with minor emphasis in the complementary field. Courses in the first year, taught in conjunction with colleagues from the SWC and CSML, provide a comprehensive and intensive introduction to both fields. Students are encouraged to work and interact closely with peers and faculty in the SWC and/or CSML throughout their PhD, providing a uniquely multidisciplinary research environment.
Applicants should have a strong analytical and quantitative background, a keen interest in neuroscience, machine learning or both, and a relevant first degree, for example in Computer Science, Engineering, Mathematics, Neuroscience, Physics, Psychology or Statistics.
Full funding is available regardless of nationality and current residence. The Unit also welcomes applicants who have secured or are seeking funding from other sources.
Applications (including a CV, transcripts, statement of research interests, and letters from three referees) should be sent directly to [email protected]. Only applications complete by the deadline are guaranteed full consideration; late applications will be entertained only if places remain unfilled.
For further details of our programme and how to apply please see:
https://www.gatsby.ucl.ac.uk/teaching/phd
Further details of research interests are available from
https://www.gatsby.ucl.ac.uk/research.html
and the individual faculty webpages at
https://www.gatsby.ucl.ac.uk/Members.html."
Self-training with Noisy Student improves ImageNet classification
https://arxiv.org/abs/1911.04252
https://arxiv.org/abs/1911.04252
arXiv.org
Self-training with Noisy Student improves ImageNet classification
We present Noisy Student Training, a semi-supervised learning approach that works well even when labeled data is abundant. Noisy Student Training achieves 88.4% top-1 accuracy on ImageNet, which...