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Found 4 papers, 2 papers with code
Bit-wise Training of Neural Network Weights
We introduce an algorithm where the individual bits representing the weights of a neural network are learned.
Training highly effective connectivities within neural networks with randomly initialized, fixed weights
We present some novel, straightforward methods for training the connection graph of a randomly initialized neural network without training the weights.
Convolutional Neural Networks on Randomized Data
Convolutional Neural Networks (CNNs) are build specifically for computer vision tasks for which it is known that the input data is a hierarchical structure based on locally correlated elements.
On modelling the emergence of logical thinking
In this paper, we argue than one of the most important aspects of the human mind is its capacity for logical thinking, which gives rise to many intellectual expressions that differentiate us from animal brains.
