This is important.
https://www.technologynetworks.com/neuroscience/news/how-synaptic-learning-depends-on-deep-brain-feedback-313436
https://www.technologynetworks.com/neuroscience/news/how-synaptic-learning-depends-on-deep-brain-feedback-313436
Neuroscience from Technology Networks
How Synaptic Learning Depends on Deep Brain Feedback
UNIGE scientists uncover the role of synaptic feedback systems in shaping learning processes in the brain’s cortex – a discovery that may prove valuable for developing efficient artificial intelligence.
Supervised learning with quantum-enhanced feature spaces
- Vojtěch Havlíček et. al.
Nature Version:
https://www.nature.com/articles/s41586-019-0980-2
Free Access:
https://arxiv.org/pdf/1804.11326
- Vojtěch Havlíček et. al.
Nature Version:
https://www.nature.com/articles/s41586-019-0980-2
Free Access:
https://arxiv.org/pdf/1804.11326
Google’s dataset search: https://toolbox.google.com/datasetsearch
#dataset #artificialintelligence #datasets #deeplearning #machinelearning
#dataset #artificialintelligence #datasets #deeplearning #machinelearning
Deep Learning Drizzle
"Read enough so you start developing intuitions and then trust your intuitions and go for it!" - Geoffrey Hinton
https://deep-learning-drizzle.github.io/
#artificialintelligence #deeplearning #machinelearning
"Read enough so you start developing intuitions and then trust your intuitions and go for it!" - Geoffrey Hinton
https://deep-learning-drizzle.github.io/
#artificialintelligence #deeplearning #machinelearning
The Neural Aesthetic
Notes and around 30 hours of video lectures, by Gene Kogan: https://ml4a.github.io/classes/itp-F18/
#art #artificialintelligence #deeplearning #generativeadversarialnetworks
Notes and around 30 hours of video lectures, by Gene Kogan: https://ml4a.github.io/classes/itp-F18/
#art #artificialintelligence #deeplearning #generativeadversarialnetworks
Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly
By K. Kandasamy et al.: https://www.cs.cmu.edu/~kkandasa/docs/proposal.pdf
Python Library: https://github.com/dragonfly/dragonfly/
Docs: https://dragonfly-opt.readthedocs.io/en/master/
#ArtificialIntelligence #DeepLearning #MachineLearning
By K. Kandasamy et al.: https://www.cs.cmu.edu/~kkandasa/docs/proposal.pdf
Python Library: https://github.com/dragonfly/dragonfly/
Docs: https://dragonfly-opt.readthedocs.io/en/master/
#ArtificialIntelligence #DeepLearning #MachineLearning
Tracking Progress in Natural Language Processing
By Sebastian Ruder: https://github.com/sebastianruder/NLP-progress
#deeplearning #machinelearning #naturallanguageprocessing
By Sebastian Ruder: https://github.com/sebastianruder/NLP-progress
#deeplearning #machinelearning #naturallanguageprocessing
Semantic Image Synthesis with Spatially-Adaptive Normalization”
Park et al.: https://nvlabs.github.io/SPADE/
#artificialintelligence #deeplearning #generativedesign
Park et al.: https://nvlabs.github.io/SPADE/
#artificialintelligence #deeplearning #generativedesign
nvlabs.github.io
Semantic Image Synthesis with Spatially-Adaptive Normalization
Reinforcement Learning for Improving Agent Design"
By David Ha
Blog: https://designrl.github.io
Paper: https://arxiv.org/abs/1810.03779
Code: https://github.com/hardmaru/astool/
#ReinforcementLearning
#MachineLearning #Design
By David Ha
Blog: https://designrl.github.io
Paper: https://arxiv.org/abs/1810.03779
Code: https://github.com/hardmaru/astool/
#ReinforcementLearning
#MachineLearning #Design
RL for Improving Agent Design
What happens when we let an agent learn a better body design?
Seven Myths in Machine Learning Research
https://crazyoscarchang.github.io/
https://crazyoscarchang.github.io/
Neighbourhood Consensus Networks
Rocco et al.: https://arxiv.org/abs/1810.10510
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #nips2018
Rocco et al.: https://arxiv.org/abs/1810.10510
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #nips2018
Coconet 🥥
The ML model behind yesterday’s Bach Doodle. By Huang et al.:
https://magenta.tensorflow.org/coconet
#artificialintelligence #deeplearning #machinelearning #tensorflow #tensorflowjs
The ML model behind yesterday’s Bach Doodle. By Huang et al.:
https://magenta.tensorflow.org/coconet
#artificialintelligence #deeplearning #machinelearning #tensorflow #tensorflowjs
Magenta
Coconet: the ML model behind today’s Bach Doodle
Have you seen today’s Doodle? Join us to celebrate J.S. Bach’s 334th birthday with the first AI-powered Google Doodle. You can create your own melody, an...
This resume does not exist
The following resume is generated by neural network trained on public resources.
By Enhancv: https://thisresumedoesnotexist.com/
#artificialintelligence #deeplearning #generativeadversarialnetworks
The following resume is generated by neural network trained on public resources.
By Enhancv: https://thisresumedoesnotexist.com/
#artificialintelligence #deeplearning #generativeadversarialnetworks
This Waifu Does Not Exist
StyleGAN-generated anime face & GPT-2-small-generated anime plot: https://www.thiswaifudoesnotexist.net/
#ArtificialIntelligence #generativeadversarialnetworks #StyleGAN
StyleGAN-generated anime face & GPT-2-small-generated anime plot: https://www.thiswaifudoesnotexist.net/
#ArtificialIntelligence #generativeadversarialnetworks #StyleGAN
Best Practices of ML Engineering"
https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
https://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
Brain signals converted into speech for the first time in human history
https://www.youtube.com/watch?v=TRIlCedyxOw
https://www.youtube.com/watch?v=TRIlCedyxOw