ArtificialIntelligenceArticles
Quantum Supremacy Using a Programmable Superconducting Processor Blog by John Martinis and Sergio Boixo : https://ai.googleblog.com/2019/10/quantum-supremacy-using-programmable.html #QuantumComputer #QuantumPhysics #QuantumSupremacy
Quantum supremacy using a programmable superconducting processor - J. Martinis - 11/1/2019
IQIM Seminar by John Martinis (Research Scientist and Professor of Physics Google and University of California, Santa Barbara), "Quantum supremacy using a programmable superconducting processor"
https://www.youtube.com/watch?v=FklMpRiTeTA
https://t.iss.one/ArtificialIntelligenceArticles
IQIM Seminar by John Martinis (Research Scientist and Professor of Physics Google and University of California, Santa Barbara), "Quantum supremacy using a programmable superconducting processor"
https://www.youtube.com/watch?v=FklMpRiTeTA
https://t.iss.one/ArtificialIntelligenceArticles
YouTube
Quantum supremacy using a programmable superconducting processor - J. Martinis - 11/1/2019
IQIM Seminar by John Martinis (Research Scientist and Professor of Physics Google and University of California, Santa Barbara), "Quantum supremacy using a programmable superconducting processor"
Presented in Caltech's Ramo Auditorium, November 1, 2019
…
Presented in Caltech's Ramo Auditorium, November 1, 2019
…
TensorFlow World 2019 Keynote
https://www.youtube.com/watch?v=MunFeX-0MD8&list=PLQY2H8rRoyvxcmHHRftsuiO1GyinVAwUg
https://www.youtube.com/watch?v=MunFeX-0MD8&list=PLQY2H8rRoyvxcmHHRftsuiO1GyinVAwUg
YouTube
TensorFlow World 2019 Keynote
O'Reilly and TensorFlow are teaming up to present the first TensorFlow World. It brings together the growing TensorFlow community to learn from each other an...
Making an Invisibility Cloak for evading Object Detectors!
https://www.profillic.com/paper/arxiv:1910.14667
(eg.the YOLOv2 detector is evaded using a pattern trained on the COCO dataset with a carefully constructed objective.)
Btw if you're interested in implementing this in your project/product, feel free to DM me
https://www.profillic.com/paper/arxiv:1910.14667
(eg.the YOLOv2 detector is evaded using a pattern trained on the COCO dataset with a carefully constructed objective.)
Btw if you're interested in implementing this in your project/product, feel free to DM me
Profillic
Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors - Profillic
Explore state-of-the-art in machine learning, AI, and robotics. 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, robotics…
Limitations of the Empirical Fisher Approximation
Kunstner et al.: https://arxiv.org/abs/1905.12558
#ArtificialIntelligence #MachineLearning
Kunstner et al.: https://arxiv.org/abs/1905.12558
#ArtificialIntelligence #MachineLearning
arXiv.org
Limitations of the Empirical Fisher Approximation for Natural...
Natural gradient descent, which preconditions a gradient descent update with the Fisher information matrix of the underlying statistical model, is a way to capture partial second-order...
Submitted to WACV 2020: Turning low-resolution pictures to super high resolution
https://www.profillic.com/paper/arxiv:1910.08761
a fully convolutional multi-stage neural network for 4× super-resolution for face images.
https://www.profillic.com/paper/arxiv:1910.08761
a fully convolutional multi-stage neural network for 4× super-resolution for face images.
Profillic
Component Attention Guided Face Super-Resolution Network: CAGFace: Model and Code
Click To Get Model/Code. To make the best use of the underlying structure of faces, the collective information through face datasets and the intermediate estimates during the upsampling process, here we introduce a fully convolutional multi-stage neural network…
On the Interaction Between Deep Detectors and Siamese Trackers in Video Surveillance
Kiran et al.: https://arxiv.org/abs/1910.14552
#ArtificialIntelligence #DeepLearning #MachineLearning
Kiran et al.: https://arxiv.org/abs/1910.14552
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
On the Interaction Between Deep Detectors and Siamese Trackers in...
Visual object tracking is an important function in many real-time video
surveillance applications, such as localization and spatio-temporal recognition
of persons. In real-world applications, an...
surveillance applications, such as localization and spatio-temporal recognition
of persons. In real-world applications, an...
Deep Learning course: lecture slides and lab notebooks
Built and maintained by Olivier Grisel and Charles Ollion: https://m2dsupsdlclass.github.io/lectures-labs/
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks #MachineLearning #NeuralNetworks
Built and maintained by Olivier Grisel and Charles Ollion: https://m2dsupsdlclass.github.io/lectures-labs/
#ArtificialIntelligence #DeepLearning #GenerativeAdversarialNetworks #MachineLearning #NeuralNetworks
lectures-labs
Deep Learning course: lecture slides and lab notebooks
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris
Keras / TPU integration in Tensorflow 2.1 (unreleased)
Google Cloud Platform : https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/README-TF2.1.md
#Keras #TPU #Tensorflow
Google Cloud Platform : https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/README-TF2.1.md
#Keras #TPU #Tensorflow
GitHub
GoogleCloudPlatform/training-data-analyst
Labs and demos for courses for GCP Training (https://cloud.google.com/training). - GoogleCloudPlatform/training-data-analyst
Game Theory is a branch of mathematics used to model the strategic interaction between different players in a context with predefined rules and outcomes.
Game Theory can be applied in different ambit of Artificial Intelligence:
Multi-agent AI systems.
Imitation and Reinforcement Learning.
Adversary training in Generative Adversarial Networks (GANs).
Game Theory can also be used to describe many situations in our daily life and Machine Learning models
Game Theory can be divided into 5 main types of games:
Cooperative vs Non-Cooperative Games: In cooperative games, participants can establish alliances in order to maximise their chances to win the game (eg. negotiations). In non-cooperative games, participants can’t instead form alliances (eg. wars).
Symmetric vs Asymmetric Games: In a symmetric game all the participants have the same goals and just their strategies implemented in order to achieve them will determine who wins the game (eg. chess). In asymmetric games instead, the participants have different or conflicting goals.
Perfect vs Imperfect Information Games: In Perfect Information games all the players can see the other players moves (eg. chess). Instead, in Imperfect Information games, the other players' moves are hidden (eg. card games).
Simultaneous vs Sequential Games: In Simultaneous games, the different players can take actions concurrently. Instead in Sequential games, each player is aware of the other players' previous actions (eg. board games).
Zero-Sum vs Non-Zero Sum Games: In Zero Sum games, if a player gains something that causes a loss to the other players. In Non-Zero Sum games, instead, multiple players can take benefit of the gains of another player.
Different aspects of Game Theory are commonly used in Artificial Intelligence, I will now introduce you to the Nash Equilibrium, Inverse Game Theory, designing AI Agents environments, and give you some practical examples.
Generative Adversarial Networks (GANs)
Multi-Agents Reinforcement Learning (MARL)
https://towardsdatascience.com/game-theory-in-artificial-intelligence-57a7937e1b88
Game Theory can be applied in different ambit of Artificial Intelligence:
Multi-agent AI systems.
Imitation and Reinforcement Learning.
Adversary training in Generative Adversarial Networks (GANs).
Game Theory can also be used to describe many situations in our daily life and Machine Learning models
Game Theory can be divided into 5 main types of games:
Cooperative vs Non-Cooperative Games: In cooperative games, participants can establish alliances in order to maximise their chances to win the game (eg. negotiations). In non-cooperative games, participants can’t instead form alliances (eg. wars).
Symmetric vs Asymmetric Games: In a symmetric game all the participants have the same goals and just their strategies implemented in order to achieve them will determine who wins the game (eg. chess). In asymmetric games instead, the participants have different or conflicting goals.
Perfect vs Imperfect Information Games: In Perfect Information games all the players can see the other players moves (eg. chess). Instead, in Imperfect Information games, the other players' moves are hidden (eg. card games).
Simultaneous vs Sequential Games: In Simultaneous games, the different players can take actions concurrently. Instead in Sequential games, each player is aware of the other players' previous actions (eg. board games).
Zero-Sum vs Non-Zero Sum Games: In Zero Sum games, if a player gains something that causes a loss to the other players. In Non-Zero Sum games, instead, multiple players can take benefit of the gains of another player.
Different aspects of Game Theory are commonly used in Artificial Intelligence, I will now introduce you to the Nash Equilibrium, Inverse Game Theory, designing AI Agents environments, and give you some practical examples.
Generative Adversarial Networks (GANs)
Multi-Agents Reinforcement Learning (MARL)
https://towardsdatascience.com/game-theory-in-artificial-intelligence-57a7937e1b88
Medium
Game Theory in Artificial Intelligence
An Introduction to Game Theory and how it can be applied to the different ambit of Artificial Intelligence.
Grammarly AI: The sweet spot of deep learning and natural language processing
https://bdtechtalks.com/2019/10/17/grammarly-ai-assistant-grammar-checker/
https://bdtechtalks.com/2019/10/17/grammarly-ai-assistant-grammar-checker/
TechTalks
Grammarly AI: The sweet spot of deep learning and natural language processing
Grammarly has found a niche suitable for the narrow capabilities of deep learning, and grown itself from a small app into the leading AI-based grammar checker.
16. Appendix: Mathematics for Deep Learning¶
https://d2l.ai/chapter_appendix_math/index.html
https://d2l.ai/chapter_appendix_math/index.html
Q8BERT: Quantized 8Bit BERT
Zafrir et al.: https://arxiv.org/abs/1910.06188
#NaturalLanguageProcessing #NLP #Transformer
Zafrir et al.: https://arxiv.org/abs/1910.06188
#NaturalLanguageProcessing #NLP #Transformer
arXiv.org
Q8BERT: Quantized 8Bit BERT
Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models contain a large...
How to Program UMAP from Scratch
And how to improve UMAP
By Nikolay Oskolkov : https://towardsdatascience.com/how-to-program-umap-from-scratch-e6eff67f55fe
#MachineLearning #DataScience #Bioinformatics
And how to improve UMAP
By Nikolay Oskolkov : https://towardsdatascience.com/how-to-program-umap-from-scratch-e6eff67f55fe
#MachineLearning #DataScience #Bioinformatics
Medium
How to Program UMAP from Scratch
And how to improve UMAP
Debate between Facebook's head of AI, Yann LeCun and Prof. Gary Marcus at New York University
What a great debate! https://youtu.be/aCCotxqxFsk
@ArtificialIntelligenceArticles
#ArtificialIntelligence #DeepLearning
What a great debate! https://youtu.be/aCCotxqxFsk
@ArtificialIntelligenceArticles
#ArtificialIntelligence #DeepLearning
YouTube
Artificial Intelligence Debate - Yann LeCun vs. Gary Marcus - Does AI Need More Innate Machinery?
Debate between Facebook's head of AI, Yann LeCun and Prof. Gary Marcus at New York University.The debate was moderated by Prof. David Chalmers. Recorded: Oct...
The schedule is almost complete for NeurIPS 2019
https://neurips.cc/Conferences/2019/ScheduleMultitrack
https://neurips.cc/Conferences/2019/ScheduleMultitrack
neurips.cc
NeurIPS 2019
NeurIPS Website
SocialIQA: Commonsense Reasoning about Social Interactions
Sap et al.: https://arxiv.org/abs/1904.09728
#Commonsense #MachineLearning #Reasoning
Sap et al.: https://arxiv.org/abs/1904.09728
#Commonsense #MachineLearning #Reasoning
arXiv.org
SocialIQA: Commonsense Reasoning about Social Interactions
We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social...
ICCV19 Best Paper Award
SinGAN: Learning a Generative Model from a Single Natural Image
"We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image"
Download Project Paper & Code Here
https://bit.ly/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
"We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as the image"
Download Project Paper & Code Here
https://bit.ly/SinGAN