diff --git a/src_denoising/models/non_local.py b/src_denoising/models/non_local.py
index 7b5adcc..36474ae 100644
--- a/src_denoising/models/non_local.py
+++ b/src_denoising/models/non_local.py
@@ -100,11 +100,11 @@ def log1mexp(x, expm1_guard = 1e-7):
     # for x close to 0 we need expm1 for numerically stable computation
     # we furtmermore modify the backward pass to avoid instable gradients,
     # ie situations where the incoming output gradient is close to 0 and the gradient of expm1 is very large
-    expxm1 = torch.expm1(x[1 - t])
+    expxm1 = torch.expm1(x[~t])
     log1mexp_fw = (-expxm1).log()
     log1mexp_bw = (-expxm1+expm1_guard).log() # limits magnitude of gradient
 
-    y[1 - t] = log1mexp_fw.detach() + (log1mexp_bw - log1mexp_bw.detach())
+    y[~t] = log1mexp_fw.detach() + (log1mexp_bw - log1mexp_bw.detach())
     return y
 
 
diff --git a/src_denoising/ops.py b/src_denoising/ops.py
index ae40b88..d109d5b 100644
--- a/src_denoising/ops.py
+++ b/src_denoising/ops.py
@@ -63,7 +63,7 @@ def forward(ctx, x, y, I, chunk_size=64):
 
             If = I_chunk.view(b,1,this_chunk_size,o).expand(b,k,this_chunk_size,o)
             y_full = torch.cuda.FloatTensor(b,k,this_chunk_size,n).fill_(0)
-            y_full = y_full.scatter_add(source=y_chunk.permute(0,3,1,2), index=If, dim=3)
+            y_full.scatter_add_(3, If.long(), y_chunk.permute(0,3,1,2))
             z_interm = torch.cat([torch.matmul(y_full[:,i_k:i_k+1,:,:], x_interm) for i_k in range(k)], 1)
             z_chunk = z_interm.permute(0,2,3,1)
             z_chunks.append(z_chunk)
@@ -90,7 +90,7 @@ def backward(ctx, grad):
             If = I_chunk.view(b,1,this_chunk_size,o).expand(b,k,this_chunk_size,o)
             del I_chunk
             y_full = torch.cuda.FloatTensor(b,k,this_chunk_size,n).fill_(0)
-            y_full = y_full.scatter_add(source=y_chunk.permute(0,3,1,2), index=If, dim=3)
+            y_full.scatter_add_(3, If.long(), y_chunk.permute(0,3,1,2))
             del y_chunk
 
             for i_k in range(k):
@@ -206,4 +206,4 @@ def _finfo(tensor):
 
 def clamp_probs(probs):
     eps = _finfo(probs).eps
-    return probs.clamp(min=eps, max=1 - eps)
\ No newline at end of file
+    return probs.clamp(min=eps, max=1 - eps)