Visualizing piecewise linear neural networks
Neural networks are often thought of as opaque, black-box function approximators, but theoretical tools let us describe and visualize their behavior. In particular, let’s study piecewise-linearity, a…
Read the full articleYou might also wanna read
Demystifying the Training of Two-Layer Neural Networks
Exploring the mysteries of two-layer neural networks, a new method reveals how back-propagation untangles the 'black box.' But is it truly g
Decoding Infinite-Width Neural Networks: Breaking Down the Myths
Infinite-width neural networks offer insights but don't always match finite networks' biases. A new theorem challenges our assumptions.
Understanding the Computational Complexity of Forward Propagation in Neural Networks
Why are neural networks so slow?
Linear Paths to Compositional Brilliance
Exploring how modern models achieve compositional generalization, diving into the geometric constraints that guide AI's future. What do line
An Animated Neural Network Visualization That Shows Training in Real Time
Most NN explanations show the math before you ever see what happens. I built the opposite: an animated network that trains in front of you.
dev.to·20d agoUnraveling the Deep ReLU Network Puzzle
Deep feedforward ReLU networks remain a cornerstone of AI, yet their workings are often opaque. Recent insights into path relationships and

Comments
Sign in to join the conversation.
No comments yet. Be the first.