Function Approximation

A Network Computes Numbers. The Loss Decides What They Mean.

Function approximation, why one linear unit cannot learn XOR, and what a hidden layer actually buys. The opening of a from-scratch tour of inductive bias.

The Features You Choose Are the Assumptions You Make

Part 2 of What Your RL Algorithm Actually Assumes — how hand-crafted features compress the state space, and what you're betting on when you pick them.

Cipher Maps: A Unified Framework for Oblivious Function Approximation Through Algebraic Structures and Bernoulli Models