@conference {1651,
	title = {How Important Is Weight Symmetry in Backpropagation?},
	booktitle = {Thirtieth AAAI Conference on Artificial Intelligence  (AAAI-16)},
	year = {2016},
	month = {Accepted},
	publisher = {Association for the Advancement of Artificial Intelligence},
	organization = {Association for the Advancement of Artificial Intelligence},
	address = {Phoenix, AZ.},
	abstract = {<p>Gradient backpropagation (BP) requires symmetric feedforward and feedback connections -- the same weights must be used for forward and backward passes. This "weight transport problem" (Grossberg 1987) is thought to be one of the main reasons to doubt BP{\textquoteright}s biologically plausibility. Using 15 different classification datasets, we systematically investigate to what extent BP really depends on weight symmetry. In a study that turned out to be surprisingly similar in spirit to Lillicrap et al.{\textquoteright}s demonstration (Lillicrap et al. 2014) but orthogonal in its results, our experiments indicate that: (1) the magnitudes of feedback weights do not matter to performance (2) the signs of feedback weights do matter -- the more concordant signs between feedforward and their corresponding feedback connections, the better (3) with feedback weights having random magnitudes and 100\% concordant signs, we were able to achieve the same or even better performance than SGD. (4) some normalizations/stabilizations are indispensable for such asymmetric BP to work, namely Batch Normalization (BN) (Ioffe and Szegedy 2015) and/or a "Batch Manhattan" (BM) update rule.</p>
},
	author = {Qianli Liao and JZ. Leibo and Tomaso Poggio}
}
