vl_nnconv: allows to run with empty filters -- hence it supports susampling, padding, and adding a bias vector only
cleans convolution code as well
cnn_imagenet.m: removes dead code from the example
rename MEX config files for MATLAB prior to 2014a
vl_test_nnlayers.m: cosmetic
vl_nnconv.cu: fixes a bug affecting a special case
Xcode: sync
cnn_train.m: adds support for binary losses
restores linux compat
cnn_train.m: plots more diagnostic information
gpu fixes
add vl_simplenn_diagnose.m
fixes whitespaces
vl_simplenn_move.m: simpler implementation
cnn_train.m: cosmetic fixes
cnn_imagenet.m: caffe-like net achieves good performance (45.8 top5, 22.2 top1) on Imagenet 2012

Still a few percentage points to shave off with this architecture.
cnn_train.m: minor improvements
losses: allow to specify a different class per spatial location
vl_simplenn_display.m: better behaved with custom layers
vl_test_nnlayers.m: tests new loss variants
multiloss: average rather than sum