How to train your MAML
By Anonymous: https://openreview.net/forum?id=HJGven05Y7
'TL;DR: MAML is great, but it has many problems, we solve many of those problems and as a result we learn most hyper parameters end to end, speed-up training and inference and set a new SOTA in few-shot learning'
#metalearning #deeplearning #fewshotlearning
By Anonymous: https://openreview.net/forum?id=HJGven05Y7
'TL;DR: MAML is great, but it has many problems, we solve many of those problems and as a result we learn most hyper parameters end to end, speed-up training and inference and set a new SOTA in few-shot learning'
#metalearning #deeplearning #fewshotlearning
OpenReview
How to train your MAML
MAML is great, but it has many problems, we solve many of those problems and as a result we learn most hyper parameters end to end, speed-up training and inference and set a new SOTA in few-shot...
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
Wortsman et al.: https://arxiv.org/abs/1812.00971
#ArtificialIntelligence #DeepLearning #MetaLearning
Wortsman et al.: https://arxiv.org/abs/1812.00971
#ArtificialIntelligence #DeepLearning #MetaLearning
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
Wortsman et al.: https://arxiv.org/abs/1812.00971
#ArtificialIntelligence #DeepLearning #MetaLearning
Wortsman et al.: https://arxiv.org/abs/1812.00971
#ArtificialIntelligence #DeepLearning #MetaLearning
ProMP: Proximal Meta-Policy Search
Builds on MAML, E-MAML and DiCE.
Rothfuss et al.: https://arxiv.org/pdf/1810.06784.pdf
Code: https://github.com/jonasrothfuss/promp
#ArtificialIntelligence #DeepLearning #MachineLearning #MetaLearning #ReinforcementLearning
Builds on MAML, E-MAML and DiCE.
Rothfuss et al.: https://arxiv.org/pdf/1810.06784.pdf
Code: https://github.com/jonasrothfuss/promp
#ArtificialIntelligence #DeepLearning #MachineLearning #MetaLearning #ReinforcementLearning
GitHub
GitHub - jonasrothfuss/ProMP: Implementation of Proximal Meta-Policy Search (ProMP) as well as related Meta-RL algorithm. Includes…
Implementation of Proximal Meta-Policy Search (ProMP) as well as related Meta-RL algorithm. Includes a useful experiment framework for Meta-RL. - jonasrothfuss/ProMP
AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence
"We should keep in mind the grandeur of the task we are discussing, which is nothing short than the creation of an artificial intelligence smarter than humans. If we succeed, we arguably have also created life itself..."
By Jeff Clune : https://arxiv.org/abs/1905.10985
#ArtificialIntelligence #ArtificialGeneralIntelligence #MetaLearning
"We should keep in mind the grandeur of the task we are discussing, which is nothing short than the creation of an artificial intelligence smarter than humans. If we succeed, we arguably have also created life itself..."
By Jeff Clune : https://arxiv.org/abs/1905.10985
#ArtificialIntelligence #ArtificialGeneralIntelligence #MetaLearning
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine : https://arxiv.org/abs/1909.04630
#MachineLearning #ArtificialIntelligence #Optimization #Control #MetaLearning
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine : https://arxiv.org/abs/1909.04630
#MachineLearning #ArtificialIntelligence #Optimization #Control #MetaLearning
arXiv.org
Meta-Learning with Implicit Gradients
A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an...
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine : https://arxiv.org/abs/1909.04630
#MachineLearning #ArtificialIntelligence #Optimization #Control #MetaLearning
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine : https://arxiv.org/abs/1909.04630
#MachineLearning #ArtificialIntelligence #Optimization #Control #MetaLearning
arXiv.org
Meta-Learning with Implicit Gradients
A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an...
Notes on iMAML: Meta-Learning with Implicit Gradients
By Ferenc Huszar : https://www.inference.vc/notes-on-imaml-meta-learning-without-differentiating-through/
#ArtificialIntelligence #MetaLearning #NeuralNetworks
By Ferenc Huszar : https://www.inference.vc/notes-on-imaml-meta-learning-without-differentiating-through/
#ArtificialIntelligence #MetaLearning #NeuralNetworks
inFERENCe
Notes on iMAML: Meta-Learning with Implicit Gradients
This week I read this cool new paper on meta-learning: it a slightly different
approach compared to its predecessors based on some observations about
differentiating the optima of regularized optimization.
* Aravind Rajeswaran, Chelsea Finn, Sham Kakade…
approach compared to its predecessors based on some observations about
differentiating the optima of regularized optimization.
* Aravind Rajeswaran, Chelsea Finn, Sham Kakade…
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Raghu et al.: https://arxiv.org/abs/1909.09157
#DeepLearning #MachineLearning #MetaLearning
Raghu et al.: https://arxiv.org/abs/1909.09157
#DeepLearning #MachineLearning #MetaLearning
arXiv.org
Rapid Learning or Feature Reuse? Towards Understanding the...
An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been Model Agnostic...
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Raghu et al.: https://arxiv.org/abs/1909.09157
#DeepLearning #MachineLearning #MetaLearning
Raghu et al.: https://arxiv.org/abs/1909.09157
#DeepLearning #MachineLearning #MetaLearning
arXiv.org
Rapid Learning or Feature Reuse? Towards Understanding the...
An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning. An especially successful algorithm has been Model Agnostic...