Compressive Transformers for Long-Range Sequence Modelling
https://openreview.net/forum?id=SylKikSYDH
https://openreview.net/forum?id=SylKikSYDH
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.
Watch Lex Fridman discussing the future landscape of Autonomous Vehicles at our Deep Learning Summit in San Francisco last year. We'll be returning to San Francisco in January 2020
@ArtificialIntelligenceArticles
https://www.youtube.com/watch?v=YgRD30iE4o0
https://t.iss.one/ArtificialIntelligenceArticles
@ArtificialIntelligenceArticles
https://www.youtube.com/watch?v=YgRD30iE4o0
https://t.iss.one/ArtificialIntelligenceArticles
YouTube
Lex Fridman - The Future Landscape of Autonomous Vehicles
This presentation took place at the RE•WORK Machine Intelligence in Autonomous Vehicles Summit in San Francisco, 2017.
Was Lex right about the future of Autonomous Vehicles? Lex will be back at the RE•WORK Summit in San Francisco, January 2020 - See more…
Was Lex right about the future of Autonomous Vehicles? Lex will be back at the RE•WORK Summit in San Francisco, January 2020 - See more…
CalTech Machine Learning Full course by Prof. Yaser Abu Moustafa. I have learnt the most from this course.
https://www.youtube.com/watch?v=idu8kaPFf1A&list=PL41qI9AD63BMXtmes0upOcPA5psKqVkgS
https://www.youtube.com/watch?v=idu8kaPFf1A&list=PL41qI9AD63BMXtmes0upOcPA5psKqVkgS
YouTube
CalTech ML Course Lecture 01 - The Learning Problem
The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Components of the learning problem. Lecture 1 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials on the course…
Google Releases Two New NLP Dialog Datasets - Anthony Alford
https://www.infoq.com/news/2019/10/google-nlp-dataset/
https://www.infoq.com/news/2019/10/google-nlp-dataset/
InfoQ
Google Releases Two New NLP Dialog Datasets
Researchers from Google AI released two new dialog datasets for natural-language processing (NLP) development: Coached Conversational Preference Elicitation (CCPE) and Taskmaster-1. The datasets contain thousands of conversations as well as labels and annotations…
A differentiable bipartite matching algorithm with theoretical convergence
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
code https://github.com/ZENGXH/DMM_Net
paper https://arxiv.org/pdf/1909.12471.pdf
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
code https://github.com/ZENGXH/DMM_Net
paper https://arxiv.org/pdf/1909.12471.pdf
GitHub
ZENGXH/DMM_Net
Differentiable Mask-Matching Network for Video Object Segmentation (ICCV 2019) - ZENGXH/DMM_Net
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.