diff --git a/src/optimise/optimisers.jl b/src/optimise/optimisers.jl
index 0149490059..53cef6be9e 100644
--- a/src/optimise/optimisers.jl
+++ b/src/optimise/optimisers.jl
@@ -555,6 +555,47 @@ function apply!(o::AdaBelief, x, Δ)
   return Δ
 end
 
+"""
+    PAdam(η = 0.01, β::Tuple = (0.9, 0.999),  ρ = 0.25, ϵ = $EPS)
+
+The partially adaptive momentum estimation method (PADAM) [https://arxiv.org/pdf/1806.06763v1.pdf]
+
+# Parameters
+- Learning rate (`η`): Amount by which gradients are discounted before updating
+                       the weights.
+- Decay of momentums (`β::Tuple`): Exponential decay for the first (β1) and the
+                                   second (β2) momentum estimate.
+- Partially adaptive parameter (`p`): Varies between 0 and 0.5.
+- Machine epsilon (`ϵ`): Constant to prevent division by zero
+                         (no need to change default)
+# Examples
+```julia
+opt = PAdam()
+```
+"""
+mutable struct PAdam <: AbstractOptimiser
+    eta::Float64
+    beta::Tuple{Float64, Float64}
+    rho::Float64
+    epsilon::Float64
+    state::IdDict{Any, Any}
+end
+
+PAdam(η::Real = 0.01, β = (0.9, 0.999), ρ::Real = 0.25, ϵ::Real = EPS) = PAdam(η, β, ρ, ϵ, IdDict())
+PAdam(η::Real, β::Tuple, ρ::Real, state::IdDict) = PAdam(η, β, ρ, EPS, state)
+  
+function apply!(o::PAdam, x, Δ)
+  η, β, ρ = o.eta, o.beta, o.rho
+          
+  mt, vt, v̂t = get!(o.state, x) do
+      (fill!(similar(x), o.epsilon), fill!(similar(x), o.epsilon), fill!(similar(x), o.epsilon))
+    end :: NTuple{3,typeof(x)}
+        
+  @. mt = β[1] * mt + (1 - β[1]) * Δ
+  @. vt = β[2] * vt + (1 - β[2]) * Δ ^ 2
+  @. v̂t = max(v̂t, vt)
+  @. Δ = η * mt / (v̂t ^ ρ + o.epsilon)      
+end
 
 # Compose optimisers