Working memory revived in older adults by synchronizing rhythmic brain circuits
https://www.nature.com/articles/s41593-019-0371-x.epdf
https://www.nature.com/articles/s41593-019-0371-x.epdf
Nature Neuroscience
Working memory revived in older adults by synchronizing rhythmic brain circuits
The authors develop a noninvasive stimulation protocol to restore neural synchronization patterns and improve working memory in older humans, contributing to groundwork for future drug-free therapeutics targeting age-related cognitive decline.
An Overview of Deep Learning Applications in Manufacturing | Exxact
https://towardsdatascience.com/an-overview-of-deep-learning-applications-in-manufacturing-exxact-64018629ca
https://towardsdatascience.com/an-overview-of-deep-learning-applications-in-manufacturing-exxact-64018629ca
Medium
An Overview of Deep Learning Applications in Manufacturing | Exxact
Introduction to Deep Learning for Manufacturing
Fermionic neural-network states for ab-initio electronic structure
Choo et al.: https://arxiv.org/abs/1909.12852
#Physics #MachineLearning #NeuralNetworks
Choo et al.: https://arxiv.org/abs/1909.12852
#Physics #MachineLearning #NeuralNetworks
Machine learning for neural decoding
Glaser et al.: https://arxiv.org/abs/1708.00909
#Cognition #MachineLearning #NeuralNetworks
Glaser et al.: https://arxiv.org/abs/1708.00909
#Cognition #MachineLearning #NeuralNetworks
arXiv.org
Machine learning for neural decoding
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have...
Great applications for the fashion industry-
Poly-GAN: Garments are automatically placed on images of human models at an arbitrary pose
https://www.profillic.com/paper/arxiv:1909.02165
Poly-GAN: Garments are automatically placed on images of human models at an arbitrary pose
https://www.profillic.com/paper/arxiv:1909.02165
Profillic
Profillic: AI models, code & research to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. 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…
Built by Stanford researchers: TunaGAN: Modify high-resolution face images with good qualitative and quantitative performance.
https://www.profillic.com/paper/arxiv:1908.06163
https://www.profillic.com/paper/arxiv:1908.06163
Profillic
Profillic: AI models, code & research to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. 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…
‘Backronym’ May Help To Generate Ideas in Machine Learning by Visualizing Hundreds of Research Papers Together
Website: https://backronym.xyz/graph.html
Paper: https://arxiv.org/pdf/1908.01874v2.pdf
https://www.marktechpost.com/2019/09/29/backronym-may-help-to-generate-ideas-in-machine-learning-by-visualizing-hundreds-of-research-papers-together/
Website: https://backronym.xyz/graph.html
Paper: https://arxiv.org/pdf/1908.01874v2.pdf
https://www.marktechpost.com/2019/09/29/backronym-may-help-to-generate-ideas-in-machine-learning-by-visualizing-hundreds-of-research-papers-together/
MarkTechPost
‘Backronym’ May Help To Generate Ideas in Machine Learning by Visualizing Hundreds of Research Papers Together
‘Backronym’ May Help To Generate Ideas in Machine Learning by Visualizing Hundreds of Research Papers Together.
Why is Andrew Ng reading a 30-year old software engineering paper?
https://worrydream.com/refs/Brooks-NoSilverBullet.pdf
https://t.iss.one/ArtificialIntelligenceArticles
https://worrydream.com/refs/Brooks-NoSilverBullet.pdf
https://t.iss.one/ArtificialIntelligenceArticles
DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare
Xu et al.: https://arxiv.org/abs/1910.00116
#ArtificialIntelligence #DeepLearning #MachineLearning
Xu et al.: https://arxiv.org/abs/1910.00116
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare
We present DenseRaC, a novel end-to-end framework for jointly estimating 3D human pose and body shape from a monocular RGB image. Our two-step framework takes the body pixel-to-surface...
Compressive Transformers for Long-Range Sequence Modelling
Anonymous : https://openreview.net/forum?id=SylKikSYDH
#ArtificialIntelligence #MachineLearning #Transformer
Anonymous : https://openreview.net/forum?id=SylKikSYDH
#ArtificialIntelligence #MachineLearning #Transformer
OpenReview
Compressive Transformers for Long-Range Sequence Modelling
Long-range transformer using a compressive memory, achieves sota in wikitext-103 and enwik8 LM benchmarks, release a new book-level LM benchmark PG-19.
Regression Planning Networks
Xu et al.: https://arxiv.org/abs/1909.13072
#ArtificialIntelligence #DeepLearning #MachineLearning
Xu et al.: https://arxiv.org/abs/1909.13072
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
Regression Planning Networks
Recent learning-to-plan methods have shown promising results on planning
directly from observation space. Yet, their ability to plan for long-horizon
tasks is limited by the accuracy of the...
directly from observation space. Yet, their ability to plan for long-horizon
tasks is limited by the accuracy of the...
Python code for Artificial Intelligence - David Poole & Alan Mackworth
Download: https://artint.info/AIPython/aipython.pdf
Download: https://artint.info/AIPython/aipython.pdf
Efficient Graph Generation with Graph Recurrent Attention Networks
Liao et al.: https://arxiv.org/abs/1910.00760
Code: https://github.com/lrjconan/GRAN
#Graph #MachineLearning #NeuralNetworks
Liao et al.: https://arxiv.org/abs/1910.00760
Code: https://github.com/lrjconan/GRAN
#Graph #MachineLearning #NeuralNetworks
arXiv.org
Efficient Graph Generation with Graph Recurrent Attention Networks
We propose a new family of efficient and expressive deep generative models of graphs, called Graph Recurrent Attention Networks (GRANs). Our model generates graphs one block of nodes and...
Machine learning predicts behavior of biological circuits
https://www.sciencedaily.com/releases/2019/10/191002165235.htm
https://www.sciencedaily.com/releases/2019/10/191002165235.htm
ScienceDaily
Machine learning predicts behavior of biological circuits
Biomedical engineers have devised a machine learning approach to modeling the interactions between complex variables in engineered bacteria that would otherwise be too cumbersome to predict. Their algorithms are generalizable to many kinds of biological systems.
Unsupervised Doodling and Painting with Improved SPIRAL
Mellor et al. : https://arxiv.org/pdf/1910.01007.pdf
Blog : https://learning-to-paint.github.io
#ReinforcementLearning #GenerativeModels #DeepLearning
Mellor et al. : https://arxiv.org/pdf/1910.01007.pdf
Blog : https://learning-to-paint.github.io
#ReinforcementLearning #GenerativeModels #DeepLearning
Identifying Weights and Architectures of Unknown ReLU Networks
David Rolnick and Konrad P. Kording : https://arxiv.org/abs/1910.00744
#DeepLearning #MachineLearning #NeuralNetworks
David Rolnick and Konrad P. Kording : https://arxiv.org/abs/1910.00744
#DeepLearning #MachineLearning #NeuralNetworks
Evolution of learning is key to better artificial intelligence
https://phys.org/news/2019-09-evolution-key-artificial-intelligence.html
https://phys.org/news/2019-09-evolution-key-artificial-intelligence.html
phys.org
Evolution of learning is key to better artificial intelligence
Since "2001: A Space Odyssey," people have wondered: could machines like HAL 9000 eventually exist that can process information with human-like intelligence?
Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs. https://arxiv.org/abs/1909.12902
arXiv.org
Interpreting Distortions in Dimensionality Reduction by...
To perform visual data exploration, many dimensionality reduction methods
have been developed. These tools allow data analysts to represent
multidimensional data in a 2D or 3D space, while...
have been developed. These tools allow data analysts to represent
multidimensional data in a 2D or 3D space, while...
Learning Neural Causal Models from Unknown Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio : https://arxiv.org/abs/1910.01075
#MetaLearning #ArtificialIntelligence #CausalModels
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio : https://arxiv.org/abs/1910.01075
#MetaLearning #ArtificialIntelligence #CausalModels
arXiv.org
Learning Neural Causal Models from Unknown Interventions
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theoretical...