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📚TheoryIntermediate

Universal Approximation Theorems

The Universal Approximation Theorems say that a neural network with at least one hidden layer and a suitable activation can approximate any continuous function on a compact domain as closely as you like.

#universal approximation theorem#cybenko#hornik+12
📚TheoryAdvanced

Reinforcement Learning Theory

Reinforcement Learning (RL) studies how an agent learns to act in an environment to maximize long-term cumulative reward.

#reinforcement learning
Advanced
Filtering by:
#function approximation
#mdp
#bellman equation
+12
📚TheoryAdvanced

Neural Network Expressivity

Neural network expressivity studies what kinds of functions different network architectures can represent and how efficiently they can do so.

#neural network expressivity#depth separation#relu linear regions+12
📚TheoryIntermediate

Universal Approximation Theorem

The Universal Approximation Theorem (UAT) says a feedforward neural network with one hidden layer and a non-polynomial activation (like sigmoid or ReLU) can approximate any continuous function on a compact set as closely as we want.

#universal approximation theorem#cybenko#hornik+12