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...
ArtificialIntelligenceArticles
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's talk at CVPR 2016 is a real treat to watch if you're a computer vision researcher! A nice overview of the CV challenges and a simplified, but clear description of the approach
https://www.youtube.com/watch?v=YgB6o_d4tL8 …
paper: https://arxiv.org/abs/1512.01413
#MachineLearning #DeepLearning
https://www.youtube.com/watch?v=YgB6o_d4tL8 …
paper: https://arxiv.org/abs/1512.01413
#MachineLearning #DeepLearning
YouTube
Computational Imaging for VLBI Image Reconstruction
This video is about Computational Imaging for VLBI Image Reconstruction
Comments on Michael Jordan’s Essay “Artificial Intelligence: The revolution hasn’t happened yet”
Emmanuel Candes, John Duchi, Chiara Sabatti. « We can summarize the points above with a slogan: cross-validation is not enough https://web.stanford.edu/~jduchi/projects/CandesDuSa19.pdf
Emmanuel Candes, John Duchi, Chiara Sabatti. « We can summarize the points above with a slogan: cross-validation is not enough https://web.stanford.edu/~jduchi/projects/CandesDuSa19.pdf
CariGANs: Unpaired Photo-to-Caricature Translation
Cao et al.: https://ai.stanford.edu/~kaidicao/carigan.pdf
Code: https://cari-gan.github.io
#ArtificialIntelligence #Caricature #GAN #GenerativeAdversarialNetworks
Cao et al.: https://ai.stanford.edu/~kaidicao/carigan.pdf
Code: https://cari-gan.github.io
#ArtificialIntelligence #Caricature #GAN #GenerativeAdversarialNetworks
A 2019 guide to Human Pose Estimation with Deep Learning
https://blog.nanonets.com/human-pose-estimation-2d-guide/
https://blog.nanonets.com/human-pose-estimation-2d-guide/
Artificial Intelligence Can Detect Alzheimer’s Disease in Brain Scans Six Years Before a Diagnosis
https://www.ucsf.edu/news/2019/01/412946/artificial-intelligence-can-detect-alzheimers-disease-brain-scans-six-years
paper:
A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using
F-FDG PET of the Brain
https://pubs.rsna.org/doi/10.1148/radiol.2018180958
https://www.ucsf.edu/news/2019/01/412946/artificial-intelligence-can-detect-alzheimers-disease-brain-scans-six-years
paper:
A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using
F-FDG PET of the Brain
https://pubs.rsna.org/doi/10.1148/radiol.2018180958
UCSF
Artificial Intelligence Can Detect Alzheimer’s Disease in Brain Scans Six Years Before a Diagnosis
Researchers programmed a machine-learning algorithm to diagnose early-stage Alzheimer’s disease from a very common type of brain scan.
TensorSpace: A Neural Network 3D Visualization Framework
Build interactive and intuitive model in browsers: https://tensorspace.org
#DeepLearning #MachineLearning #Keras #TensorFlow
@ArtificialIntelligenceArticles
Build interactive and intuitive model in browsers: https://tensorspace.org
#DeepLearning #MachineLearning #Keras #TensorFlow
@ArtificialIntelligenceArticles
tensorspace.org
TensorSpace.js
Home page of TensorSpace.js
Inferring the quantum density matrix with machine learning
Cranmer et al.: https://arxiv.org/abs/1904.05903
#QuantumPhysics #Physics #ArtificialIntelligence #MachineLearning
Cranmer et al.: https://arxiv.org/abs/1904.05903
#QuantumPhysics #Physics #ArtificialIntelligence #MachineLearning
Linguistic Knowledge and Transferability of Contextual Representations
Liu et al.: https://arxiv.org/abs/1903.08855
#ArtificialIntelligence #DeepLearning #MachineLearning
Liu et al.: https://arxiv.org/abs/1903.08855
#ArtificialIntelligence #DeepLearning #MachineLearning