syncedreview

DeepMind Explores the Connection Between Gradient-Based Meta-Learning and Convex Optimization

Second nature for humans, meta-learning is a powerful yet challenging technique for AI systems that enables a model to meta-learn the parameters of a parameterized algorithm by self-evaluating its ...
marktechpost

Latest Machine Learning Research From Deepmind Explores The Connection Between Gradient-Based Meta-Learning And Convex Optimization

The term “meta-learning” refers to the process by which a learner adjusts to a new challenge by modifying an algorithm with known parameters. The algorithm’s parameters are meta-learned by measuring ...
IEEE

Pengcheng Wan

Backpropagation Algorithm,Bias Of Layer,Binary Classification,Conjugate Gradient,Connection Weights,Convergence Rate,Descent Direction,F1 Score,Feed-forward Network ...
IEEE

Qiao Li

Backpropagation Algorithm,Bias Of Layer,Binary Classification,Complex Plane,Conjugate Gradient,Connection Weights,Convergence Rate,Covariance Matrix,Crisis Management ...