An Explicitly Relational Neural Network Architecture
Shanahan et al.: https://arxiv.org/abs/1905.10307
#deeplearning #neuralnetworks #symbolicAI
Shanahan et al.: https://arxiv.org/abs/1905.10307
#deeplearning #neuralnetworks #symbolicAI
arXiv.org
An Explicitly Relational Neural Network Architecture
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an...
Learning to learn by Self-Critique
Antreas Antoniou and Amos Storkey: https://arxiv.org/abs/1905.10295
#ArtificialIntelligence #DeepLearning #MachineLearning
Antreas Antoniou and Amos Storkey: https://arxiv.org/abs/1905.10295
#ArtificialIntelligence #DeepLearning #MachineLearning
"A Latent Variational Framework for Stochastic Optimization"
By Philippe Casgrain: https://arxiv.org/abs/1905.01707
#ArtificialIntelligence #MachineLearning #Probability #Computation
By Philippe Casgrain: https://arxiv.org/abs/1905.01707
#ArtificialIntelligence #MachineLearning #Probability #Computation
arXiv.org
A Latent Variational Framework for Stochastic Optimization
This paper provides a unifying theoretical framework for stochastic
optimization algorithms by means of a latent stochastic variational problem.
Using techniques from stochastic control, the...
optimization algorithms by means of a latent stochastic variational problem.
Using techniques from stochastic control, the...
Last week, Yann LeCun, Stanley Osher, René Vidal, Rebecca Willett and I organized the workshop "Deep Geometric Learning of Big Data and Applications" at Institute for Pure and Applied Mathematics, UCLA.
All talks, from theoretical to practical deep learning, were pretty inspiring. All videos are available here:
https://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
Thanks to all speakers, poster presenters, participants and IPAM for a wonderful and insightful week!
All talks, from theoretical to practical deep learning, were pretty inspiring. All videos are available here:
https://www.ipam.ucla.edu/programs/workshops/workshop-iv-deep-geometric-learning-of-big-data-and-applications/?tab=schedule
Thanks to all speakers, poster presenters, participants and IPAM for a wonderful and insightful week!
IPAM
Workshop IV: Deep Geometric Learning of Big Data and Applications - IPAM
Aude Oliva (MIT): "there are about 200 papers using ConvNets to model the activity of the primate visual cortex."
She is running a challenge to explain fMRI and MEG data: http://algonauts.csail.mit.edu/challenge.html @ArtificialIntelligenceArticles
She is running a challenge to explain fMRI and MEG data: http://algonauts.csail.mit.edu/challenge.html @ArtificialIntelligenceArticles
👁Computer Vision: Image Classification
https://www.adhiraiyan.org/deeplearning/computer-vision-image-classification
https://www.adhiraiyan.org/deeplearning/computer-vision-image-classification
Yann Lecun : Ho parlato al quotidiano italiano La Stampa delle sfide che l'Intelligenza Artificiale sta affrontando oggi, dalle fake news ai contenuti inappropriati e di come insegnamo ai nostri sistemi a riconoscerli perchè possano intervenire e tutelarci.
Questa è senza dubbio una grande sfida ma ho fiducia nel futuro e negli sforzi che stiamo facendo per avvicinarci.
This is how I discussed with the italian daily La Stampa the key challenges AI is facing today, from fake news to inappropriate or extremist content, and how we teach our systems to understand those forms so that they can take an action to protect us.
It's a very challenging problem but I trust we'll get there, one day.
https://www.lastampa.it/2019/05/22/scienza/che-cosa-insegno-allia-yJ1jgYV5SH6nTbTOmfmmAN/
Questa è senza dubbio una grande sfida ma ho fiducia nel futuro e negli sforzi che stiamo facendo per avvicinarci.
This is how I discussed with the italian daily La Stampa the key challenges AI is facing today, from fake news to inappropriate or extremist content, and how we teach our systems to understand those forms so that they can take an action to protect us.
It's a very challenging problem but I trust we'll get there, one day.
https://www.lastampa.it/2019/05/22/scienza/che-cosa-insegno-allia-yJ1jgYV5SH6nTbTOmfmmAN/
lastampa.it/scienza
“Che cosa insegno all’IA”
Che cos’è l’Intelligenza Artificiale? Non è facile dirlo, è qualcosa che cambia sempre». Qualche settimana fa, a Yann LeCun è stato assegnato il Turing Award per i suoi lavori sulle reti neurali convoluzionali e con Yoshua Bengio e Geoffrey Hinton, tra la…
Good to watch
https://www.youtube.com/watch?v=Ioqrw4sCcwQ
https://www.youtube.com/watch?v=Ioqrw4sCcwQ
YouTube
CMU Neural Nets for NLP 2019 (8): Sentence and Contextualized Word Representations
This lecture (by Graham Neubig) for CMU CS 11-747, Neural Networks for NLP (Spring 2019) covers:
* Sentence Representations
* Contextual Word Representations
Site: http://phontron.com/class/nn4nlp2019/schedule/contextual-representation.html
* Sentence Representations
* Contextual Word Representations
Site: http://phontron.com/class/nn4nlp2019/schedule/contextual-representation.html
Epileptic Seizure Classification ML Algorithms
https://towardsdatascience.com/seizure-classification-d0bb92d19962
https://towardsdatascience.com/seizure-classification-d0bb92d19962
Medium
Epileptic Seizure Classification ML Algorithms
Binary Classification Machine Learning Algorithms in Python
Keras: Feature extraction on large datasets with Deep Learning
https://www.pyimagesearch.com/2019/05/27/keras-feature-extraction-on-large-datasets-with-deep-learning/
https://www.pyimagesearch.com/2019/05/27/keras-feature-extraction-on-large-datasets-with-deep-learning/
PyImageSearch
Keras: Feature extraction on large datasets with Deep Learning - PyImageSearch
In this tutorial you will learn how to use Keras feature extraction on large image datasets with Deep Learning. We'll also learn how to use incremental learning to train your image classifier on top of the extracted features.
A curated list of gradient boosting research papers with implementations.
https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers
https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers
GitHub
GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with implementations.
A curated list of gradient boosting research papers with implementations. - GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with ...
Week 2 CS294-158 Deep Unsupervised Learning (2/6/19)
#UCBerkeley #deeplearning #computervision
https://m.youtube.com/watch?v=mYCLVPRy2nc&feature=share
#UCBerkeley #deeplearning #computervision
https://m.youtube.com/watch?v=mYCLVPRy2nc&feature=share
YouTube
Week 2 CS294-158 Deep Unsupervised Learning (2/6/19)
UC Berkeley CS294-158 Deep Unsupervised Learning (Spring 2019)
Instructors: Pieter Abbeel, Xi (Peter) Chen, Jonathan Ho, Aravind Srinivas
https://sites.google.com/view/berkeley-cs294-158-sp19/home
Week 2 Lecture Contents:
- Likelihood Models Part I: Autoregressive…
Instructors: Pieter Abbeel, Xi (Peter) Chen, Jonathan Ho, Aravind Srinivas
https://sites.google.com/view/berkeley-cs294-158-sp19/home
Week 2 Lecture Contents:
- Likelihood Models Part I: Autoregressive…
ML algorithms and their math -
1. Naive Bayes - https://goo.gl/m3gh1o
2. Decision Trees (ID3) - https://goo.gl/HFqAd4
3. Random Forest - https://goo.gl/y3Au8M
4. K-means - https://goo.gl/worGWg
5. Ridge Regression - https://goo.gl/YGdUFr
6. Logistic Regression - https://goo.gl/zDvRcF
https://www.thelearningmachine.ai/ml
1. Naive Bayes - https://goo.gl/m3gh1o
2. Decision Trees (ID3) - https://goo.gl/HFqAd4
3. Random Forest - https://goo.gl/y3Au8M
4. K-means - https://goo.gl/worGWg
5. Ridge Regression - https://goo.gl/YGdUFr
6. Logistic Regression - https://goo.gl/zDvRcF
https://www.thelearningmachine.ai/ml
AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
Jeff Clune: https://arxiv.org/abs/1905.10985
#ArtificialIntelligence #ArtificialGeneralIntelligence #AGI
Jeff Clune: https://arxiv.org/abs/1905.10985
#ArtificialIntelligence #ArtificialGeneralIntelligence #AGI
arXiv.org
AI-GAs: AI-generating algorithms, an alternate paradigm for...
Perhaps the most ambitious scientific quest in human history is the creation of general artificial intelligence, which roughly means AI that is as smart or smarter than humans. The dominant...
Understanding Hinton’s Capsule Networks. Part I: Intuition.
Blog by Max Pechyonkin: https://medium.com/ai³-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
#MachineLearning #DeepLearning #GeoffreyHinton #ArtificialIntelligence #Theory
Blog by Max Pechyonkin: https://medium.com/ai³-theory-practice-business/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b
#MachineLearning #DeepLearning #GeoffreyHinton #ArtificialIntelligence #Theory
Medium
Understanding Hinton’s Capsule Networks. Part I: Intuition.
Part of Understanding Hinton’s Capsule Networks Series:
"End-to-End Deep Reinforcement Learning without Reward Engineering"
They developed an end-to-end method that allows robots to learn from a modest number of images that depict successful completion of a task.
https://bair.berkeley.edu/blog/2019/05/28/end-to-end/
They developed an end-to-end method that allows robots to learn from a modest number of images that depict successful completion of a task.
https://bair.berkeley.edu/blog/2019/05/28/end-to-end/
The Berkeley Artificial Intelligence Research Blog
End-to-End Deep Reinforcement Learning
without Reward Engineering
without Reward Engineering
The BAIR Blog
"Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor"
Do word embeddings really say that man is to doctor as woman is to nurse? Apparently not!
Nissim et al.: https://arxiv.org/abs/1905.09866
#ArtificialIntelligence #MachineLearning #NLProc #bias
Do word embeddings really say that man is to doctor as woman is to nurse? Apparently not!
Nissim et al.: https://arxiv.org/abs/1905.09866
#ArtificialIntelligence #MachineLearning #NLProc #bias
arXiv.org
Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor
Analogies such as "man is to king as woman is to X" are often used to illustrate the amazing power of word embeddings. Concurrently, they have also been used to expose how strongly human biases...
How to deliver on Machine Learning projects
A guide to the ML Engineering Loop.
By Emmanuel Ameisen and Adam Coates: https://blog.insightdatascience.com/how-to-deliver-on-machine-learning-projects-c8d82ce642b0
#ArtificialIntelligence #BigData #DataScience #DeepLearning #MachineLearning
A guide to the ML Engineering Loop.
By Emmanuel Ameisen and Adam Coates: https://blog.insightdatascience.com/how-to-deliver-on-machine-learning-projects-c8d82ce642b0
#ArtificialIntelligence #BigData #DataScience #DeepLearning #MachineLearning
Medium
How to deliver on Machine Learning projects
A guide to the ML Engineering Loop
Open-ended learning in symmetric zero-sum games
Balduzzi et al.: http://proceedings.mlr.press/v97/balduzzi19a.html
#ArtificialIntelligence #MachineLearning #Nash #GameTheory
Balduzzi et al.: http://proceedings.mlr.press/v97/balduzzi19a.html
#ArtificialIntelligence #MachineLearning #Nash #GameTheory
PMLR
Open-ended learning in symmetric zero-sum games
Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them ‘winner’ and ‘loser’. If the game ...