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​Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning">
Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

🔗 Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning
Posted by Eleni Triantafillou, Student Researcher, and Vincent Dumoulin, Research Scientist, Google Research Recently, deep learning has...
​Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning">
Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

🔗 Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning
Posted by Eleni Triantafillou, Student Researcher, and Vincent Dumoulin, Research Scientist, Google Research Recently, deep learning has...
​Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning">
Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning

🔗 Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning
Posted by Eleni Triantafillou, Student Researcher, and Vincent Dumoulin, Research Scientist, Google Research Recently, deep learning has...
​Linear Discriminant Analysis for Dimensionality Reduction in Python - Machine Learning Mastery

🔗 Linear Discriminant Analysis for Dimensionality Reduction in Python - Machine Learning Mastery
Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Linear Discriminant Analysis, or LDA for short, is a predictive modeling algorithm for multi-class classification. It can also be used as a dimensionality reduction technique,
​Linear Discriminant Analysis for Dimensionality Reduction in Python - Machine Learning Mastery

🔗 Linear Discriminant Analysis for Dimensionality Reduction in Python - Machine Learning Mastery
Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Linear Discriminant Analysis, or LDA for short, is a predictive modeling algorithm for multi-class classification. It can also be used as a dimensionality reduction technique,
​new paper, Truncated Quantile Critics, improves SOTA on MuJoCo by 20-30% ! With TF and PT code.


Video: https://youtu.be/idp4k1L9UhM
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Truncated Quantile Critics



🎥 Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
👁 1 раз 68 сек.
Video for "Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics" by A. Kuznetsov, P. Shvechikov, A. Grishin, D. Vetrov
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Code Tensorflow: https://github.com/bayesgroup/tqc
Code PyTorch: https://github.com/bayesgroup/tqc_pytorch
​new paper, Truncated Quantile Critics, improves SOTA on MuJoCo by 20-30% ! With TF and PT code.


Video: https://youtu.be/idp4k1L9UhM
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Truncated Quantile Critics



🎥 Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
👁 1 раз 68 сек.
Video for "Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics" by A. Kuznetsov, P. Shvechikov, A. Grishin, D. Vetrov
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Code Tensorflow: https://github.com/bayesgroup/tqc
Code PyTorch: https://github.com/bayesgroup/tqc_pytorch
​new paper, Truncated Quantile Critics, improves SOTA on MuJoCo by 20-30% ! With TF and PT code.


Video: https://youtu.be/idp4k1L9UhM
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Наш телеграм канал - tglink.me/ai_machinelearning_big_data

🔗 Truncated Quantile Critics



🎥 Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
👁 1 раз 68 сек.
Video for "Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics" by A. Kuznetsov, P. Shvechikov, A. Grishin, D. Vetrov
Project page: https://bayesgroup.github.io/tqc
Paper: https://arxiv.org/abs/2005.04269
Code Tensorflow: https://github.com/bayesgroup/tqc
Code PyTorch: https://github.com/bayesgroup/tqc_pytorch