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What makes untrained deep neural networks (DNNs) different from the trainedperformant ones? By zooming into the weights in well-trained DNNs, we found itis the location of weights that hold most of the information encoded by thetraining. Motivated by this observation, we hypothesize that weights instochastic gradient-based method trained DNNs can be separated into twodimensions: the locations of weights and their exact values. To assess ourhypothesis, we propose a novel method named Lookahead Permutation (LaPerm) totrain DNNs by reconnecting the weights. We empirically demonstrate theversatility of LaPerm while producing extensive evidence to support ourhypothesis: when the initial weights are random and dense, our methoddemonstrates speed and performance similar to or better than that of regularoptimizers, e.g., Adam; when the initial weights are random and sparse (manyzeros), our method changes the way neurons connect and reach accuracycomparable to that of a well-trained fully initialized network; when theinitial weights share a single value, our method finds weight agnostic neuralnetwork with far better-than-chance accuracy.

Train-by-Reconnect: Decoupling Locations of Weights from their Values