# Difference between 1x1 Convolution and TimeDistributed(Dense())

Are these lines of code equivalent in Keras? From a few runs, they seem to be, and also intuitively since the channels dimension of my data is 1, my understanding is that a fully connected acts like a convolutional layer. Is one better computationally (they have the same number of parameters)?

Conv1D(filters=1, kernel_size=1, strides=1, padding='causal')
TimeDistributed(Dense(1))


For example, I have a regression problem, where I have (batches,64) points coming in and (batches,64) points coming out of the model.

My model is:

model = Sequential()