Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands
Hans Pinckaers, Geert Litjens : https://arxiv.org/abs/1910.10470
GitHub : https://github.com/DIAGNijmegen/neural-odes-segmentation
#MedNeurIPS #NeurIPS #NeurIPS2019
Hans Pinckaers, Geert Litjens : https://arxiv.org/abs/1910.10470
GitHub : https://github.com/DIAGNijmegen/neural-odes-segmentation
#MedNeurIPS #NeurIPS #NeurIPS2019
arXiv.org
Neural Ordinary Differential Equations for Semantic Segmentation...
Automated medical image segmentation plays a key role in quantitative research and diagnostics. Convolutional neural networks based on the U-Net architecture are the state-of-the-art. A key...
"Differentiable Convex Optimization Layers"
CVXPY creates powerful new PyTorch and TensorFlow layers
Agrawal et al.: https://locuslab.github.io/2019-10-28-cvxpylayers/
#PyTorch #TensorFlow #NeurIPS2019
CVXPY creates powerful new PyTorch and TensorFlow layers
Agrawal et al.: https://locuslab.github.io/2019-10-28-cvxpylayers/
#PyTorch #TensorFlow #NeurIPS2019
locuslab.github.io
Differentiable Convex Optimization Layers
CVXPY creates powerful new PyTorch and TensorFlow layers
Introduction to Reinforcement Learning
By DeepMind : https://youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
#ArtificialIntelligence #DeepLearning #ReinforcementLearning
By DeepMind : https://youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ
#ArtificialIntelligence #DeepLearning #ReinforcementLearning
ArtificialIntelligenceArticles
Yoshua Bengio talks about what's next for deep learning @ArtificialIntelligenceArticles omorrow at NeurIPS, Yoshua Bengio will propose ways for deep learning to handle "reasoning, planning, capturing causality and obtaining systematic generalization." He spoke…
YouTube
Yoshua Bengio | From System 1 Deep Learning to System 2 Deep Learning | NeurIPS 2019
Slides: https://www.iro.umontreal.ca/~bengioy/NeurIPS-11dec2019.pdf
Summary:
Past progress in deep learning has concentrated mostly on learning from a static dataset, mostly for perception tasks and other System 1 tasks which are done intuitively and unconsciously…
Summary:
Past progress in deep learning has concentrated mostly on learning from a static dataset, mostly for perception tasks and other System 1 tasks which are done intuitively and unconsciously…
NeurIPS 2019 Paper Awards
Neural Information Processing Systems Conference : https://medium.com/@NeurIPSConf/neurips-2019-paper-awards-807e41d0c1e
@ArtificialIntelligenceArticles
#ArtificialIntelligence #NeurIPS #NeurIPS2019
https://t.iss.one/ArtificialIntelligenceArticles
Neural Information Processing Systems Conference : https://medium.com/@NeurIPSConf/neurips-2019-paper-awards-807e41d0c1e
@ArtificialIntelligenceArticles
#ArtificialIntelligence #NeurIPS #NeurIPS2019
https://t.iss.one/ArtificialIntelligenceArticles
Medium
NeurIPS 2019 Paper Awards
With this blog post, it is our pleasure to unveil the NeurIPS paper awards for 2019, and share more information on the selection process…
Best 2019 Paper Awards in #ComputerVision
https://www.datasciencecentral.com/profiles/blogs/best-paper-awards-in-machine-learning
https://www.datasciencecentral.com/profiles/blogs/best-paper-awards-in-machine-learning
Datasciencecentral
Best 2019 Paper Awards in Computer Vision
The IEEE International Conference on Computer Vision received 4,303 papers and accepted 1,075 for the 2019 summit. Bellow is the best paper award.
Source: see…
Source: see…
NeurIPS 2019: Best paper awards
OUTSTANDING PAPER AWARDS:
Uniform convergence may be unable to explain generalization in deep learning.
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses (Honorable mention).
Fast and Accurate Least-Mean-Squares Solvers (Honorable mention).
OUTSTANDING NEW DIRECTIONS PAPER AWARD
Putting An End to End-to-End: Gradient-Isolated Learning of Representations
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
TEST OF TIME AWARD
Dual Averaging Method for Regularized Stochastic Learning and Online Optimization
Link:
https://medium.com/@NeurIPSConf/neurips-2019-paper-awards-807e41d0c1e
OUTSTANDING PAPER AWARDS:
Uniform convergence may be unable to explain generalization in deep learning.
Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses (Honorable mention).
Fast and Accurate Least-Mean-Squares Solvers (Honorable mention).
OUTSTANDING NEW DIRECTIONS PAPER AWARD
Putting An End to End-to-End: Gradient-Isolated Learning of Representations
Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations
TEST OF TIME AWARD
Dual Averaging Method for Regularized Stochastic Learning and Online Optimization
Link:
https://medium.com/@NeurIPSConf/neurips-2019-paper-awards-807e41d0c1e
Medium
NeurIPS 2019 Paper Awards
With this blog post, it is our pleasure to unveil the NeurIPS paper awards for 2019, and share more information on the selection process…
ICCV 2019 Best Papers Announced
SinGAN: Learning a Generative Model from a Single Natural Image
an unconditional generative model that can be learned from a single natural image ... capture the internal distribution of patches within the image ...
SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image
PAPER
https://openaccess.thecvf.com/content_ICCV_2019/papers/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.pdf
CODE (IN PYTORCH)
https://github.com/tamarott/SinGAN
Honorable mention
Specifying Object Attributes and Relations in Interactive Scene Generation
The method separates between a layout embedding and an appearance embedding. The dual embedding leads to generated images that better match the scene graph, have higher visual quality, and support more complex scene graphs
PAPER
https://openaccess.thecvf.com/content_ICCV_2019/papers/Ashual_Specifying_Object_Attributes_and_Relations_in_Interactive_Scene_Generation_ICCV_2019_paper.pdf
CODE
https://www. github.com/ashual/scene_generation
SinGAN: Learning a Generative Model from a Single Natural Image
an unconditional generative model that can be learned from a single natural image ... capture the internal distribution of patches within the image ...
SinGAN contains a pyramid of fully convolutional GANs, each responsible for learning the patch distribution at a different scale of the image
PAPER
https://openaccess.thecvf.com/content_ICCV_2019/papers/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.pdf
CODE (IN PYTORCH)
https://github.com/tamarott/SinGAN
Honorable mention
Specifying Object Attributes and Relations in Interactive Scene Generation
The method separates between a layout embedding and an appearance embedding. The dual embedding leads to generated images that better match the scene graph, have higher visual quality, and support more complex scene graphs
PAPER
https://openaccess.thecvf.com/content_ICCV_2019/papers/Ashual_Specifying_Object_Attributes_and_Relations_in_Interactive_Scene_Generation_ICCV_2019_paper.pdf
CODE
https://www. github.com/ashual/scene_generation
In the first five years of a full-time research position? Apply to join our global network of fellows, pursuing answers to some of the most important questions facing science and humanity.
Tag an exceptional researcher who should apply.
https://ow.ly/rLZG50xt3Br
Tag an exceptional researcher who should apply.
https://ow.ly/rLZG50xt3Br
CIFAR
CIFAR Azrieli Global Scholars
The CIFAR Azrieli Global Scholars program provides funding and support to help scholars build their network and develop essential skills.
The AI Index 2019 has just been released, with new in-depth and evolved research. AI touches many aspects of society - this report takes an interdisciplinary approach by design, analyzing and distilling patterns about AI's broad global impact. https://stanford.io/2RLwgcA
Stanford Institute for Human-Centered Artificial Intelligence
Introducing the AI Index 2019 Report
The AI Index 2019 Report takes an interdisciplinary approach by design, analyzing and distilling patterns about AI’s broad global impact on everything from national economies to job growth, research and public perception.
Machine Learning on Graphs #NeurIPS2019
Michael Galkin : https://medium.com/@mgalkin/machine-learning-on-graphs-neurips-2019-875eecd41069
#GraphNeuralNetworks #NLP
Michael Galkin : https://medium.com/@mgalkin/machine-learning-on-graphs-neurips-2019-875eecd41069
#GraphNeuralNetworks #NLP
Medium
Machine Learning on Graphs @ NeurIPS 2019
If you still had any doubts — it’s time to admit. Machine Learning on Graphs becomes a first-class citizen at AI conferences while being…
Trust Your Model: Model-Based Policy Optimization
Codes: https://github.com/JannerM/mbpo
Paper: https://arxiv.org/pdf/1906.08253.pdf
Codes: https://github.com/JannerM/mbpo
Paper: https://arxiv.org/pdf/1906.08253.pdf
GitHub
GitHub - jannerm/mbpo: Code for the paper "When to Trust Your Model: Model-Based Policy Optimization"
Code for the paper "When to Trust Your Model: Model-Based Policy Optimization" - GitHub - jannerm/mbpo: Code for the paper "When to Trust Your Model: Model-Based Policy O...
ImJoy: a new computational platform for deep learning https://www.scilifelab.se/news/imjoy-a-new-computational-platform-for-deep-learning/
What a statement: $1,000,000 prize money at the Kaggle "Deepfake Detection Challenge" – Identifying videos with facial or voice manipulations.
@ArtificialIntelligenceArticles
"These content generation and modification technologies may affect the quality of public discourse and the safeguarding of human rights—especially given that deepfakes may be used maliciously as a source of misinformation, manipulation, harassment, and persuasion. Identifying manipulated media is a technically demanding and rapidly evolving challenge that requires collaborations across the entire tech industry and beyond.
AWS, Facebook, Microsoft, the Partnership on AI’s Media Integrity Steering Committee, and academics have come together to build the Deepfake Detection Challenge (DFDC)."
https://www.kaggle.com/c/deepfake-detection-challenge
#AI #deeplearning #deepfakes #kaggle https://t.iss.one/ArtificialIntelligenceArticles
@ArtificialIntelligenceArticles
"These content generation and modification technologies may affect the quality of public discourse and the safeguarding of human rights—especially given that deepfakes may be used maliciously as a source of misinformation, manipulation, harassment, and persuasion. Identifying manipulated media is a technically demanding and rapidly evolving challenge that requires collaborations across the entire tech industry and beyond.
AWS, Facebook, Microsoft, the Partnership on AI’s Media Integrity Steering Committee, and academics have come together to build the Deepfake Detection Challenge (DFDC)."
https://www.kaggle.com/c/deepfake-detection-challenge
#AI #deeplearning #deepfakes #kaggle https://t.iss.one/ArtificialIntelligenceArticles
SuperDyna
Toward a General AI-Agent Architecture
Rich Sutton (presentation starts at 15 min.) : https://slideslive.com/38921889/biological-and-artificial-reinforcement-learning-4
#ArtificialIntelligence #ReinforcementLearning #AGI
Toward a General AI-Agent Architecture
Rich Sutton (presentation starts at 15 min.) : https://slideslive.com/38921889/biological-and-artificial-reinforcement-learning-4
#ArtificialIntelligence #ReinforcementLearning #AGI
SlidesLive
Biological and Artificial Reinforcement Learning 4
Reinforcement learning (RL) algorithms learn through rewards and a process of trial-and-error. This approach was strongly inspired by the study of animal behaviour and has led to outstanding...
Quality-Diversity optimisation algorithms
List of papers related to QD algorithms, links to tutorials and workshops, and pointers to existing implementations of QD algorithms. Cully et al.: https://quality-diversity.github.io
#EvolutionaryAlgorithms #QualityDiversity #Illumination
List of papers related to QD algorithms, links to tutorials and workshops, and pointers to existing implementations of QD algorithms. Cully et al.: https://quality-diversity.github.io
#EvolutionaryAlgorithms #QualityDiversity #Illumination
Quality-Diversity optimisation algorithms
About
This webpage intends to list papers related to QD algorithms, links to tutorials and workshops, and pointers to existing implementations of QD algorithms.