1,693 research outputs found
The Power of Attention: Examining the Effects of Headquarters Attention to Reverse Knowledge Transfer
Sampling unknown large networks restricted by low sampling rates
Graph sampling plays an important role in data mining for large networks.
Specifically, larger networks often correspond to lower sampling rates. Under
the situation, traditional traversal-based samplings for large networks usually
have an excessive preference for densely-connected network core nodes. Aim at
this issue, this paper proposes a sampling method for unknown networks at low
sampling rates, called SLSR, which first adopts a random node sampling to
evaluate a degree threshold, utilized to distinguish the core from periphery,
and the average degree in unknown networks, and then runs a double-layer
sampling strategy on the core and periphery. SLSR is simple that results in a
high time efficiency, but experimental evaluation confirms that the proposed
method can accurately preserve many critical structures of unknown large
networks at sampling rates not exceeding 10%.Comment: 19 pages,14 figure
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Wisely utilizing the internal and external learning methods is a new
challenge in super-resolution problem. To address this issue, we analyze the
attributes of two methodologies and find two observations of their recovered
details: 1) they are complementary in both feature space and image plane, 2)
they distribute sparsely in the spatial space. These inspire us to propose a
low-rank solution which effectively integrates two learning methods and then
achieves a superior result. To fit this solution, the internal learning method
and the external learning method are tailored to produce multiple preliminary
results. Our theoretical analysis and experiment prove that the proposed
low-rank solution does not require massive inputs to guarantee the performance,
and thereby simplifying the design of two learning methods for the solution.
Intensive experiments show the proposed solution improves the single learning
method in both qualitative and quantitative assessments. Surprisingly, it shows
more superior capability on noisy images and outperforms state-of-the-art
methods
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