Best Paper Award at the AI for social good Workshop at #ICML2019 https://medium.com/@jasonphang/deep-neural-networks-improve-radiologists-performance-in-breast-cancer-screening-565eb2bd3c9f
[code] https://github.com/nyukat/breast_cancer_classifier
[preprint] https://arxiv.org/pdf/1903.08297.pdf
[data specs] https://cs.nyu.edu/~kgeras/reports/datav1.0.pdf
[ICML '19] https://aiforsocialgood.github.io/icml2019/acceptedpapers.htm
[code] https://github.com/nyukat/breast_cancer_classifier
[preprint] https://arxiv.org/pdf/1903.08297.pdf
[data specs] https://cs.nyu.edu/~kgeras/reports/datav1.0.pdf
[ICML '19] https://aiforsocialgood.github.io/icml2019/acceptedpapers.htm
New Paper:
Stand-Alone Self-Attention in Vision Models
https://arxiv.org/abs/1906.05909
Can attention work as a stand-alone primitive for vision models?
We develop a pure self-attention model by replacing the spatial convolutions in a ResNet by a simple, local self-attention layer.
Stand-Alone Self-Attention in Vision Models
https://arxiv.org/abs/1906.05909
Can attention work as a stand-alone primitive for vision models?
We develop a pure self-attention model by replacing the spatial convolutions in a ResNet by a simple, local self-attention layer.
MIT neuroscientists have performed the most rigorous testing yet of computational models that mimic the brain’s visual cortex.
Using their current best model of the brain’s visual neural network, the researchers designed a new way to precisely control individual neurons and populations of neurons in the middle of that network. In an animal study, the team then showed that the information gained from the computational model enabled them to create images that strongly activated specific brain neurons of their choosing.
The findings suggest that the current versions of these models are similar enough to the brain that they could be used to control brain states in animals. The study also helps to establish the usefulness of these vision models, which have generated vigorous debate over whether they accurately mimic how the visual cortex works, says James DiCarlo, the head of MIT’s Department of Brain and Cognitive Sciences, an investigator in the McGovern Institute for Brain Research and the Center for Brains, Minds, and Machines, and the senior author of the study.
Full article: https://news.mit.edu/2019/computer-model-brain-visual-cortex-0502
Science paper: https://science.sciencemag.org/content/364/6439/eaav9436
Biorxiv (open access): https://www.biorxiv.org/content/10.1101/461525v1
https://t.iss.one/ArtificialIntelligenceArticles
Using their current best model of the brain’s visual neural network, the researchers designed a new way to precisely control individual neurons and populations of neurons in the middle of that network. In an animal study, the team then showed that the information gained from the computational model enabled them to create images that strongly activated specific brain neurons of their choosing.
The findings suggest that the current versions of these models are similar enough to the brain that they could be used to control brain states in animals. The study also helps to establish the usefulness of these vision models, which have generated vigorous debate over whether they accurately mimic how the visual cortex works, says James DiCarlo, the head of MIT’s Department of Brain and Cognitive Sciences, an investigator in the McGovern Institute for Brain Research and the Center for Brains, Minds, and Machines, and the senior author of the study.
Full article: https://news.mit.edu/2019/computer-model-brain-visual-cortex-0502
Science paper: https://science.sciencemag.org/content/364/6439/eaav9436
Biorxiv (open access): https://www.biorxiv.org/content/10.1101/461525v1
https://t.iss.one/ArtificialIntelligenceArticles
MIT News
Putting vision models to the test
MIT neuroscientists have performed the most rigorous testing yet of computational models that mimic the brain’s visual cortex. The results suggest that the current versions of these models are similar enough to the brain to allow them to actually control…
Can deep neural networks help cognitive scientists understand the brain?
Radoslaw M. Cichy & Daniel Kaiser discuss the value of DNNs as scientific models in a new TICS Opinion piece.
https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(19)30034-8 https://t.iss.one/ArtificialIntelligenceArticles
Radoslaw M. Cichy & Daniel Kaiser discuss the value of DNNs as scientific models in a new TICS Opinion piece.
https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(19)30034-8 https://t.iss.one/ArtificialIntelligenceArticles
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ArtificialIntelligenceArticles
for who have a passion for -
1. #ArtificialIntelligence
2. Machine Learning
3. Deep Learning
4. #DataScience
5. #Neuroscience
6. #ResearchPapers
7. Related Courses and Ebooks
1. #ArtificialIntelligence
2. Machine Learning
3. Deep Learning
4. #DataScience
5. #Neuroscience
6. #ResearchPapers
7. Related Courses and Ebooks
The future depends on some graduate student who is deeply suspicious of everything I have said. - Geoffrey Hinton https://t.iss.one/ArtificialIntelligenceArticles
Long Short-Term Memory: From Zero to Hero with PyTorch
https://blog.floydhub.com/long-short-term-memory-from-zero-to-hero-with-pytorch/
https://blog.floydhub.com/long-short-term-memory-from-zero-to-hero-with-pytorch/
Efficient Exploration via State Marginal Matching
Lee et al.
Blog: https://sites.google.com/view/state-marginal-matching
Paper: https://arxiv.org/abs/1906.05274
Code: https://github.com/RLAgent/state-marginal-matching
#ArtificialIntelligence #MachineLearning #ReinforcementLearning
Lee et al.
Blog: https://sites.google.com/view/state-marginal-matching
Paper: https://arxiv.org/abs/1906.05274
Code: https://github.com/RLAgent/state-marginal-matching
#ArtificialIntelligence #MachineLearning #ReinforcementLearning
Google
State Marginal Matching
Learning the Depths of Moving People by Watching Frozen People
Li et al.: https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Learning_the_Depths_of_Moving_People_by_Watching_Frozen_People_CVPR_2019_paper.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
Li et al.: https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Learning_the_Depths_of_Moving_People_by_Watching_Frozen_People_CVPR_2019_paper.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
Theoretical Physics for Deep Learning workshop at #ICML2019
Slides and videos: https://sites.google.com/view/icml2019phys4dl/schedule?authuser=0
#Physics #DeepLearning
Slides and videos: https://sites.google.com/view/icml2019phys4dl/schedule?authuser=0
#Physics #DeepLearning
Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
arXiv.org
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is...
ProMP: Proximal Meta-Policy Search
Builds on MAML, E-MAML and DiCE.
Rothfuss et al.: https://arxiv.org/pdf/1810.06784.pdf
Code: https://github.com/jonasrothfuss/promp
#ArtificialIntelligence #DeepLearning #MachineLearning #MetaLearning #ReinforcementLearning
Builds on MAML, E-MAML and DiCE.
Rothfuss et al.: https://arxiv.org/pdf/1810.06784.pdf
Code: https://github.com/jonasrothfuss/promp
#ArtificialIntelligence #DeepLearning #MachineLearning #MetaLearning #ReinforcementLearning
GitHub
GitHub - jonasrothfuss/ProMP: Implementation of Proximal Meta-Policy Search (ProMP) as well as related Meta-RL algorithm. Includes…
Implementation of Proximal Meta-Policy Search (ProMP) as well as related Meta-RL algorithm. Includes a useful experiment framework for Meta-RL. - jonasrothfuss/ProMP
From CVPR 2019: Turning Doodles into Stunning, Photorealistic Landscapes.
NVIDIA research harnesses generative adversarial networks to create highly realistic scenes.
Artists can use paintbrush and paint bucket tools to design their own landscapes with labels like river, rock and cloud
https://www.profillic.com/paper/arxiv:1903.07291
NVIDIA research harnesses generative adversarial networks to create highly realistic scenes.
Artists can use paintbrush and paint bucket tools to design their own landscapes with labels like river, rock and cloud
https://www.profillic.com/paper/arxiv:1903.07291
Profillic
Profillic: AI research & source code to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. Browse papers, source code, models, and more by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language…
From CVPR 2019: A team of scientists at Microsoft and academic collaborators reconstruct color images of a scene from the point cloud.
https://www.profillic.com/paper/arxiv:1904.03303
https://www.profillic.com/paper/arxiv:1904.03303
Profillic
Profillic: AI research & source code to supercharge your projects
Explore state-of-the-art in machine learning, AI, and robotics research. Browse papers, source code, models, and more by topics and authors. Connect with researchers and engineers working on related problems in machine learning, deep learning, natural language…
Event2Mind: Commonsense Inference on Events, Intents, and Reactions ACL 2018
demo https://uwnlp.github.io/event2mind/
paper https://homes.cs.washington.edu/~msap/pdfs/rashkin2018event2mind.pdf
demo https://uwnlp.github.io/event2mind/
paper https://homes.cs.washington.edu/~msap/pdfs/rashkin2018event2mind.pdf
Comprehensive Introduction to Neural Network Architecture
https://towardsdatascience.com/comprehensive-introduction-to-neural-network-architecture-c08c6d8e5d98
https://towardsdatascience.com/comprehensive-introduction-to-neural-network-architecture-c08c6d8e5d98
Medium
Intermediate Topics in Neural Networks
A detailed overview of neural architecture, activation functions, loss functions, output units.
SLIDES
What can Statistical Machine Translation teach Neural Text Generation about Optimization
Graham Neubig
@ NAACL Workshop on Methods for Optimizing and Evaluating Neural Language Generation6/6/2019
https://www.phontron.com/slides/neubig19neuralgen.pdf
What can Statistical Machine Translation teach Neural Text Generation about Optimization
Graham Neubig
@ NAACL Workshop on Methods for Optimizing and Evaluating Neural Language Generation6/6/2019
https://www.phontron.com/slides/neubig19neuralgen.pdf
Course 3 of the deeplearning.ai TensorFlow Specialization is now available on Coursera! You’ll learn how to build natural language processing systems using TensorFlow. Enroll in the Specialization for $49/month or audit for free: https://www.coursera.org/specializations/tensorflow-in-practice