diff --git a/onn/optimizer.py b/onn/optimizer.py
index 3a42dac..c31843a 100644
--- a/onn/optimizer.py
+++ b/onn/optimizer.py
@@ -387,8 +387,8 @@ class Adamlike(Optimizer):
     # refer to the subclasses for details.
 
     # these defaults match Adam's.
-    def __init__(self, lr=0.001, b1=0.9, b2=0.999,
-                 power=1/2, debias=True, runmax=False, eps=1e-8):
+    def __init__(self, lr=0.001, b1=0.9, b2=0.999, power=1/2,
+                 debias=True, runmax=False, yogi=False, eps=1e-8):
         self.b1 = _f(b1)  # decay term
         self.b2 = _f(b2)  # decay term
         self.b1_t_default = _f(b1)  # decay term power t
@@ -396,6 +396,7 @@ class Adamlike(Optimizer):
         self.power = _f(power)
         self.debias = bool(debias)
         self.runmax = bool(runmax)
+        self.yogi = bool(yogi)
         self.eps = _f(eps)
 
         super().__init__(lr)
@@ -420,7 +421,12 @@ class Adamlike(Optimizer):
         # keep local references of mt and vt to simplify
         # implementing all the variations of Adam later.
         mt = filter_gradients(self.mt, dW, self.b1)
-        vt = filter_gradients(self.vt, np.square(dW), self.b2)
+        if self.yogi:
+            g2 = np.square(dW)
+            vt = self.vt
+            vt -= (1 - self.b2) * np.sign(vt - g2) * g2
+        else:
+            vt = filter_gradients(self.vt, np.square(dW), self.b2)
 
         if self.runmax:
             self.vtmax[:] = np.maximum(vt, self.vtmax)
@@ -455,8 +461,8 @@ class Adagrad(Adamlike):
     # paper: https://web.stanford.edu/~jduchi/projects/DuchiHaSi11.pdf
 
     def __init__(self, lr=0.01, eps=1e-8):
-        super().__init__(lr=lr, b1=0.0, b2=-1.0,
-                         power=1/2, debias=False, runmax=False, eps=eps)
+        super().__init__(lr=lr, b1=0.0, b2=-1.0, power=1/2,
+                         debias=False, runmax=False, eps=eps)
 
     @property
     def g(self):
@@ -471,8 +477,8 @@ class RMSprop(Adamlike):
     # slides: http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
 
     def __init__(self, lr=0.001, mu=0.99, eps=1e-8):
-        super().__init__(lr=lr, b1=0.0, b2=mu,
-                         power=1/2, debias=False, runmax=False, eps=eps)
+        super().__init__(lr=lr, b1=0.0, b2=mu, power=1/2,
+                         debias=False, runmax=False, eps=eps)
 
     @property
     def mu(self):
@@ -499,8 +505,18 @@ class Adam(Adamlike):
 
     def __init__(self, lr=0.001, b1=0.9, b2=0.999,
                  debias=True, eps=1e-8):
-        super().__init__(lr=lr, b1=b1, b2=b2,
-                         power=1/2, debias=debias, runmax=False, eps=eps)
+        super().__init__(lr=lr, b1=b1, b2=b2, power=1/2,
+                         debias=debias, runmax=False, yogi=False, eps=eps)
+
+
+class Yogi(Adamlike):
+    # paper: https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization.pdf
+    # based on Adam. this changes the filtering for vt.
+
+    def __init__(self, lr=0.01, b1=0.9, b2=0.999,
+                 debias=True, eps=1e-3):
+        super().__init__(lr=lr, b1=b1, b2=b2, power=1/2,
+                         debias=debias, runmax=False, yogi=True, eps=eps)
 
 
 class AMSgrad(Adamlike):
@@ -509,8 +525,8 @@ class AMSgrad(Adamlike):
 
     def __init__(self, lr=0.001, b1=0.9, b2=0.999,
                  debias=True, eps=1e-8):
-        super().__init__(lr=lr, b1=b1, b2=b2,
-                         power=1/2, debias=debias, runmax=True, eps=eps)
+        super().__init__(lr=lr, b1=b1, b2=b2, power=1/2,
+                         debias=debias, runmax=True, yogi=False, eps=eps)
 
 
 class Padam(Adamlike):
@@ -520,5 +536,5 @@ class Padam(Adamlike):
 
     def __init__(self, lr=0.1, b1=0.9, b2=0.999,
                  power=1/8, debias=True, eps=1e-8):
-        super().__init__(lr=lr, b1=b1, b2=b2,
-                         power=power, debias=debias, runmax=True, eps=eps)
+        super().__init__(lr=lr, b1=b1, b2=b2, power=power,
+                         debias=debias, runmax=True, yogi=False, eps=eps)