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...
Learning Neural Causal Models from Unknown Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio : https://arxiv.org/abs/1910.01075
#MetaLearning #ArtificialIntelligence #CausalModels
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio : https://arxiv.org/abs/1910.01075
#MetaLearning #ArtificialIntelligence #CausalModels
arXiv.org
Learning Neural Causal Models from Unknown Interventions
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theoretical...
Meta-Learning Deep Energy-Based Memory Models
Bartunov et al.: https://arxiv.org/abs/1910.02720
#MachineLearning #MetaLearning #EnergyBasedMemoryModels
Bartunov et al.: https://arxiv.org/abs/1910.02720
#MachineLearning #MetaLearning #EnergyBasedMemoryModels
Learning Neural Causal Models from Unknown Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio : https://arxiv.org/abs/1910.01075
#MetaLearning #MachineLearning #ArtificialIntelligence
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio : https://arxiv.org/abs/1910.01075
#MetaLearning #MachineLearning #ArtificialIntelligence
arXiv.org
Learning Neural Causal Models from Unknown Interventions
Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data. However, there are theoretical...
Useful Models for Robot Learning
Slides by Marc Deisenroth : https://deisenroth.cc/talks/2019-12-14-neurips-ws.pdf
#ReinforcementLearning #Robotics #MetaLearning
Slides by Marc Deisenroth : https://deisenroth.cc/talks/2019-12-14-neurips-ws.pdf
#ReinforcementLearning #Robotics #MetaLearning
How Meta-Learning Could Help Us Accomplish Our Grandest AI Ambitions, and Early, Exotic Steps in that Direction
Jeff Clune : https://www.cs.uwyo.edu/~jeffclune/share/2019_12_13_NeurIPS_Metalearning.pdf
#ArtificialGeneralIntelligence #AGI #MetaLearning
Jeff Clune : https://www.cs.uwyo.edu/~jeffclune/share/2019_12_13_NeurIPS_Metalearning.pdf
#ArtificialGeneralIntelligence #AGI #MetaLearning
"Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm"
Chelsea Finn and Sergey Levine : https://arxiv.org/abs/1710.11622
#MachineLearning #ArtificialIntelligence #MetaLearning #NeuralComputing
Chelsea Finn and Sergey Levine : https://arxiv.org/abs/1710.11622
#MachineLearning #ArtificialIntelligence #MetaLearning #NeuralComputing
Stanford CS330: Multi-Task and Meta-Learning, 2019
Lecture videos, Finn et al.: https://youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5
#ArtificialIntelligence #DeepLearning #MetaLearning
Lecture videos, Finn et al.: https://youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5
#ArtificialIntelligence #DeepLearning #MetaLearning
YouTube
Stanford CS330: Deep Multi-Task and Meta Learning
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai