18 citations · 37 across the 7 of their papers we have counts for
9 papers
Hierarchical Partitioning Forecaster
Christopher Mattern
In this work we consider a new family of algorithms for sequential prediction, Hierarchical Partitioning Forecasters (HPFs). Our goal is to provide appealing theoretical - regret g…
Gated Linear Networks
Joel Veness, Tor Lattimore, David Budden +8
This paper presents a new family of backpropagation-free neural architectures, Gated Linear Networks (GLNs). What distinguishes GLNs from contemporary neural networks is the distri…
Generalized Probability Smoothing
Christopher Mattern
In this work we consider a generalized version of Probability Smoothing, the core elementary model for sequential prediction in the state of the art PAQ family of data compression…
Online Learning with Gated Linear Networks
Joel Veness, Tor Lattimore, Avishkar Bhoopchand +3
This paper describes a family of probabilistic architectures designed for online learning under the logarithmic loss. Rather than relying on non-linear transfer functions, our meth…
On Probability Estimation by Exponential Smoothing
Christopher Mattern
Probability estimation is essential for every statistical data compression algorithm. In practice probability estimation should be adaptive, recent observations should receive a hi…
On Probability Estimation via Relative Frequencies and Discount
Christopher Mattern
Probability estimation is an elementary building block of every statistical data compression algorithm. In practice probability estimation is often based on relative letter frequen…