An agent that selects proto-objects (segmented color regions) instead of fixed patches matches or beats a patch-based baseline on CarRacing and Doom Take Cover while using 62% fewer parameters in its smallest configuration.
PReLU: Yet Another Single-Layer Solution to the XOR Problem
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
This paper demonstrates that a single-layer neural network using Parametric Rectified Linear Unit (PReLU) activation can solve the XOR problem, a simple fact that has been overlooked so far. We compare this solution to the multi-layer perceptron (MLP) and the Growing Cosine Unit (GCU) activation function and explain why PReLU enables this capability. Our results show that the single-layer PReLU network can achieve 100\% success rate in a wider range of learning rates while using only three learnable parameters.
fields
cs.NE 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Neuroevolution of Self-Attention Over Proto-Objects
An agent that selects proto-objects (segmented color regions) instead of fixed patches matches or beats a patch-based baseline on CarRacing and Doom Take Cover while using 62% fewer parameters in its smallest configuration.