60,564 research outputs found
Primitive Vassiliev Invariants and Factorization in Chern-Simons Perturbation Theory
The general structure of the perturbative expansion of the vacuum expectation
value of a Wilson line operator in Chern-Simons gauge field theory is analyzed.
The expansion is organized according to the independent group structures that
appear at each order. It is shown that the analysis is greatly simplified if
the group factors are chosen in a certain way that we call canonical. This
enables us to show that the logarithm of a polinomial knot invariant can be
written in terms of primitive Vassiliev invariants only.Comment: 15 pages, latex, 2 figure
More on softly broken N=2 QCD
We extend previous work on the soft breaking of supersymmetric QCD. We
present the formalism for the breaking due to a dilaton spurion for a general
gauge group and obtain the exact effective potential. We obtain some general
features of the vacuum structure in the pure Yang-Mills theory and we
also derive a general mass formula for this class of theories, in particular we
present explicit results for the mass spectrum in the case. Finally we
analyze the vacuum structure of the theory with one massless
hypermultiplet. This theory presents dyon condensation and a first order phase
transition in the supersymmetry breaking parameter driven by non-mutually local
BPS states. This could be a hint of Argyres-Douglas-like phases in
non-supersymmetric gauge theories.Comment: 35 pages, 9 Postscript figure
Compression-aware Training of Deep Networks
In recent years, great progress has been made in a variety of application
domains thanks to the development of increasingly deeper neural networks.
Unfortunately, the huge number of units of these networks makes them expensive
both computationally and memory-wise. To overcome this, exploiting the fact
that deep networks are over-parametrized, several compression strategies have
been proposed. These methods, however, typically start from a network that has
been trained in a standard manner, without considering such a future
compression. In this paper, we propose to explicitly account for compression in
the training process. To this end, we introduce a regularizer that encourages
the parameter matrix of each layer to have low rank during training. We show
that accounting for compression during training allows us to learn much more
compact, yet at least as effective, models than state-of-the-art compression
techniques.Comment: Accepted at NIPS 201
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