Neural Information Processing Systems http://nips.cc/

Following the unsupervised literature [4, 3, 5], the siamese networkFθ is trained by imposing structural properties on the fmapC such as bijectivity and orthogonality on the shape pairs in the training set.

Thispaperproposes a new quasi-attention that is compositional in nature, i.e., learning whether to add, subtract or nullify a certain vector when learning representations. This is strongly contrasted with vanilla attention, which simply re-weights input tokens.

Neural Information Processing Systems http://nips.cc/

Specifically,inPAC-Bayesian approach, [45,26,4,22]provide the generalization bounds of order O( 1 n). In implicit embedding approach, [12, 13, 52, 11, 58, 7] provide the convergence rate of orderO( 1n1/4), and [53] of orderO( 1 n). In the factor graph decomposition approach, [18, 51] present the generalization upper bounds of orderO( 1 n).