A reinforcement learning update that adjusts each neuron's input-output sensitivity using TD error can replace external exploration noise and backpropagation through time in small actor-critic tasks.
Any Target Function Exists in a Neighborhood of Any Sufficiently Wide Random Network: A Geometrical Perspective
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
It is known that any target function is realized in a sufficiently small neighborhood of any randomly connected deep network, provided the width (the number of neurons in a layer) is sufficiently large. There are sophisticated theories and discussions concerning this striking fact, but rigorous theories are very complicated. We give an elementary geometrical proof by using a simple model for the purpose of elucidating its structure. We show that high-dimensional geometry plays a magical role: When we project a high-dimensional sphere of radius 1 to a low-dimensional subspace, the uniform distribution over the sphere reduces to a Gaussian distribution of negligibly small covariances.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Dynamic Reinforcement Learning for Actors
A reinforcement learning update that adjusts each neuron's input-output sensitivity using TD error can replace external exploration noise and backpropagation through time in small actor-critic tasks.