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{1547}  
MetaLearning Update Rules for Unsupervised Representation Learning
This is a clearlywritten, easy to understand paper. The results are not highly compelling, but as a first set of experiments, it's successful enough. I wonder what more constraints (fewer parameters, per the genome), more options for architecture modifications (e.g. different feedback schemes, per neurobiology), and a blackbox optimization algorithm (evolution) would do?  
{1528}  
Discovering hidden factors of variation in deep networks
 
{1455}  
Conducting credit assignment by aligning local distributed representations
Lit review.
 
{1426}  
Training neural networks with local error signals
 
{1432}  
Direct Feedback alignment provides learning in deep neural nets
 
{1423}  
PMID27824044 Random synaptic feedback weights support error backpropagation for deep learning.
Our proof says that weights W0 and W evolve to equilibrium manifolds, but simulations (Fig. 4) and analytic results (Supple mentary Proof 2) hint at something more specific: that when the weights begin near 0, feedback alignment encourages W to act like a local pseudoinverse of B around the error manifold. This fact is important because if B were exactly W + (the Moore Penrose pseudoinverse of W ), then the network would be performing GaussNewton optimization (Supplementary Proof 3). We call this update rule for the hidden units pseudobackprop and denote it by ∆hPBP = W + e. Experiments with the linear net work show that the angle, ∆hFA ]∆hPBP quickly becomes smaller than ∆hFA ]∆hBP (Fig. 4b, c; see Methods). In other words feedback alignment, despite its simplicity, displays elements of secondorder learning. 