Katie Bouman TED Talk
Katie Bouman is the postdoctoral fellow who led the development of the algorithm used to image a black hole.
TED Talk:
https://www.ted.com/talks/katie_bouman_what_does_a_black_hole_look_like
Katie Bouman is the postdoctoral fellow who led the development of the algorithm used to image a black hole.
TED Talk:
https://www.ted.com/talks/katie_bouman_what_does_a_black_hole_look_like
Ted
How to take a picture of a black hole
At the heart of the Milky Way, there's a supermassive black hole that feeds off a spinning disk of hot gas, sucking up anything that ventures too close -- even light. We can't see it, but its event horizon casts a shadow, and an image of that shadow could…
Fast Interactive Object Annotation with Curve-GCN"
Ling et al.
Paper: https://arxiv.org/pdf/1903.06874.pdf
Video: https://www.youtube.com/watch?v=ycD2BtO-QzU
#PyTorch Code: https://github.com/fidler-lab/curve-gcn
#ArtificialIntelligence #DeepLearning #MachineLearning
Ling et al.
Paper: https://arxiv.org/pdf/1903.06874.pdf
Video: https://www.youtube.com/watch?v=ycD2BtO-QzU
#PyTorch Code: https://github.com/fidler-lab/curve-gcn
#ArtificialIntelligence #DeepLearning #MachineLearning
YouTube
Fast Interactive Object Annotation with Curve-GCN
Paper is accepted by Conference on Computer Vision and Pattern Recognition (CVPR), 2019
Paper link: https://arxiv.org/abs/1903.06874
Code is available at: https://github.com/fidler-lab/curve-gcn
Paper link: https://arxiv.org/abs/1903.06874
Code is available at: https://github.com/fidler-lab/curve-gcn
Best of arXiv.org for AI, Machine Learning, and Deep Learning – March 2019 #insidebigdata #BigDataAnalytics https://insidebigdata.com/2019/04/09/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-march-2019/
This one is a must read - the latest #ComputerVision #PatternRecognition https://deepai.org/publication/relational-action-forecasting
An overview of embedding models of entities and relationships for knowledge base completion
https://arxiv.org/abs/1703.08098
https://arxiv.org/abs/1703.08098
Liquid Splash Modeling with Neural Networks
https://ge.in.tum.de/download/2018-mlflip-um/2018-mlflip-um-talk.pdf
https://ge.in.tum.de/download/2018-mlflip-um/2018-mlflip-um-talk.pdf
The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos
https://dimadamen.github.io/TheProsandCons/index.html
https://arxiv.org/pdf/1812.05538.pdf
https://dimadamen.github.io/TheProsandCons/index.html
https://arxiv.org/pdf/1812.05538.pdf
Deep brain stimulation of the internal capsule enhances human cognitive control and prefrontal cortex function
https://www.nature.com/articles/s41467-019-09557-4
https://www.nature.com/articles/s41467-019-09557-4
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering. https://arxiv.org/abs/1904.04357
arXiv.org
Heterogeneous Memory Enhanced Multimodal Attention Model for Video...
In this paper, we propose a novel end-to-end trainable Video Question
Answering (VideoQA) framework with three major components: 1) a new
heterogeneous memory which can effectively learn global...
Answering (VideoQA) framework with three major components: 1) a new
heterogeneous memory which can effectively learn global...
Relational Reasoning Network (RRN) for Anatomical Landmarking. https://arxiv.org/abs/1904.04354
Neural Rerendering in the Wild. https://arxiv.org/abs/1904.04290
Masquer Hunter: Adversarial Occlusion-aware Face Detection
https://arxiv.org/abs/1709.05188v2
https://arxiv.org/abs/1709.05188v2
best paper awards at Conference NAACL 2019
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
https://arxiv.org/abs/1904.05233
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
https://arxiv.org/abs/1904.05233
Analysing Mathematical Reasoning Abilities of Neural Models
Saxton et al.: https://arxiv.org/abs/1904.01557
Code and data: https://github.com/deepmind/mathematics_dataset
#ArtificialIntelligence #MachineLearning #NeuralNetworks
Saxton et al.: https://arxiv.org/abs/1904.01557
Code and data: https://github.com/deepmind/mathematics_dataset
#ArtificialIntelligence #MachineLearning #NeuralNetworks
Robots that Learn to Use Improvised Tools
Blog by Annie Xie: https://bair.berkeley.edu/blog/2019/04/11/tools/
#ArtificialIntelligence #DeepLearning #ReinforcementLearning #Robotics
Blog by Annie Xie: https://bair.berkeley.edu/blog/2019/04/11/tools/
#ArtificialIntelligence #DeepLearning #ReinforcementLearning #Robotics
Workshop on Fairness Accountability Transparency and Ethics in Computer Vision at CVPR 2019
https://sites.google.com/view/fatecv/home
https://sites.google.com/view/fatecv/home
Google
FATE/CV
Complete ML Study Path On Github
It contains links and resources to learn Tensorflow and Scikit-Learn
https://github.com/clone95/Virgilio @ArtificialIntelligenceArticles
It contains links and resources to learn Tensorflow and Scikit-Learn
https://github.com/clone95/Virgilio @ArtificialIntelligenceArticles
A new Dataset from Stanford of 1000+ knee MRIs.
https://stanfordmlgroup.github.io/competitions/mrnet/
https://stanfordmlgroup.github.io/competitions/mrnet/
stanfordmlgroup.github.io
MRNet: A Dataset of KneeMRs and Competition for Automated Knee MR Interpretation.
The MRNet dataset consists of 1,370 knee MRI exams performed at Stanford University Medical Center. The dataset contains 1,104 (80.6%) abnormal exams, with 319 (23.3%) ACL tears and 508 (37.1%) meniscal tears; labels were obtained through manual extraction…
https://www.youtube.com/watch?v=guBrLgRKJuw&feature=share
Intro to TensorFlow for Deep Learning
https://eu.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
Intro to TensorFlow for Deep Learning
https://eu.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
YouTube
Discussing TensorFlow with the founder of Udacity (TensorFlow Meets)
TensorFlow 2.0 makes it easier than ever to get started with deep learning and machine learning. On this episode of TensorFlow Meets, Paige (@DynamicWebPaige...