diff --git a/docs/src/api.md b/docs/src/api.md
index 6c021f25..efc886e9 100644
--- a/docs/src/api.md
+++ b/docs/src/api.md
@@ -17,6 +17,7 @@ Optimisers.AMSGrad
 Optimisers.NAdam
 Optimisers.AdamW
 Optimisers.AdaBelief
+Optimisers.PAdam
 ```
 
 In addition to the main course, you may wish to order some of these condiments:
diff --git a/src/Optimisers.jl b/src/Optimisers.jl
index 20fc8aad..da5df27d 100644
--- a/src/Optimisers.jl
+++ b/src/Optimisers.jl
@@ -13,7 +13,7 @@ export destructure
 
 include("rules.jl")
 export Descent, Adam, Momentum, Nesterov, Rprop, RMSProp,
-       AdaGrad, AdaMax, AdaDelta, AMSGrad, NAdam, AdamW, RAdam, OAdam, AdaBelief,
+       AdaGrad, AdaMax, AdaDelta, AMSGrad, NAdam, AdamW, RAdam, OAdam, AdaBelief, PAdam,
        WeightDecay, ClipGrad, ClipNorm, OptimiserChain, Lion,
        AccumGrad
 
diff --git a/src/rules.jl b/src/rules.jl
index e994b740..74036a1b 100644
--- a/src/rules.jl
+++ b/src/rules.jl
@@ -543,7 +543,41 @@ function apply!(o::AdaBelief, state, x, dx)
 
   return (mt, st, βt .* β), dx′
 end
+"""
+PAdam(η = 1f-2, β = (9f-1, 9.99f-1), ρ = 2.5f-1, eps(typeof(η)))
+
+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)
+"""
+struct PAdam{T} <: AbstractRule
+    eta::T
+    beta::Tuple{T, T}
+    rho::T
+    epsilon::T
+end
+PAdam(η = 1f-2, β = (9f-1, 9.99f-1), ρ = 2.5f-1, ϵ = eps(typeof(η))) = PAdam{typeof(η)}(η, β, ρ, ϵ)
+                                                
+init(o::PAdam, x::AbstractArray) = (onevalue(o.epsilon, x), onevalue(o.epsilon, x), onevalue(o.epsilon, x))
+
+function apply!(o::PAdam, state, x, dx)
+  η, β, ρ, ϵ = o.eta, o.beta, o.rho, o.epsilon
+  mt, vt, v̂t = state
+                                               
+  @.. mt = β[1] * mt + (1 - β[1]) * dx
+  @.. vt = β[2] * vt + (1 - β[2]) * abs2(dx)
+  @.. v̂t = max(v̂t, vt)
+  dx′ = @lazy η * mt / (v̂t ^ ρ + ϵ)
+  
+  return (mt, vt, v̂t), dx′                                                
+end
 """
     WeightDecay(γ = 5f-4)
 
diff --git a/test/rules.jl b/test/rules.jl
index a10e055f..a4d3b8f6 100644
--- a/test/rules.jl
+++ b/test/rules.jl
@@ -8,7 +8,7 @@ RULES = [
   # All the rules at default settings:
   Descent(), Adam(), Momentum(), Nesterov(), Rprop(), RMSProp(),
   AdaGrad(), AdaMax(), AdaDelta(), AMSGrad(), NAdam(),
-  AdamW(), RAdam(), OAdam(), AdaBelief(), Lion(),
+  AdamW(), RAdam(), OAdam(), AdaBelief(), PAdam(), Lion(),
   # A few chained combinations:
   OptimiserChain(WeightDecay(), Adam(0.001)),
   OptimiserChain(ClipNorm(), Adam(0.001)),
@@ -266,4 +266,4 @@ end
   
   tree, x4 = Optimisers.update(tree, x3, g4)
   @test x4 ≈ x3
-end
\ No newline at end of file
+end