The Power of Asymmetry in Binary Hashing

When approximating binary similarity using the hamming distance between short binary hashes, we show that even if the similarity is symmetric, we can have shorter and more accurate hashes by using two distinct code maps. I.e. by approximating the similarity between $x$ and $x'$ as the hamming distance between $f(x)$ and $g(x')$, for two distinct binary codes $f,g$, rather than as the hamming distance between $f(x)$ and $f(x')$.Comment: Accepted to NIPS 2013, 9 pages, 5 figure

Asymmetric Empirical Similarity

The paper offers a formal model of analogical legal reasoning and takes the model to data. Under the model, the outcome of a new case is a weighted average of the outcomes of prior cases. The weights capture precedential influence and depend on fact similarity (distance in fact space) and precedential authority (position in the judicial hierarchy). The empirical analysis suggests that the model is a plausible model for the time series of U.S. maritime salvage cases. Moreover, the results evince that prior cases decided by inferior courts have less influence than prior cases decided by superior courts