58 citations · 336 across the 38 of their papers we have counts for
8 papers · 1 filter
Random versus maximum entropy models of neural population activity
Ulisse Ferrari, Tomoyuki Obuchi, Thierry Mora
The principle of maximum entropy provides a useful method for inferring statistical mechanics models from observations in correlated systems, and is widely used in a variety of fie…
Accelerating cross-validation with total variation and its application to super-resolution imaging
Tomoyuki Obuchi, Shiro Ikeda, Kazunori Akiyama +1
We develop an approximation formula for the cross-validation error (CVE) of a sparse linear regression penalized by -norm and total variation terms, which is based on a per…
Approximate cross-validation formula for Bayesian linear regression
Yoshiyuki Kabashima, Tomoyuki Obuchi, Makoto Uemura
Cross-validation (CV) is a technique for evaluating the ability of statistical models/learning systems based on a given data set. Despite its wide applicability, the rather heavy c…
Relative species abundance of replicator dynamics with sparse interactions
Tomoyuki Obuchi, Yoshiyuki Kabashima, Kei Tokita
A theory of relative species abundance on sparsely-connected networks is presented by investigating the replicator dynamics with symmetric interactions. Sparseness of a network inv…
Multiple peaks of species abundance distributions induced by sparse interactions
Tomoyuki Obuchi, Yoshiyuki Kabashima, Kei Tokita
We investigate the replicator dynamics with "sparse" symmetric interactions which represent specialist-specialist interactions in ecological communities. By considering a large sel…
Sampling approach to sparse approximation problem: determining degrees of freedom by simulated annealing
Tomoyuki Obuchi, Yoshiyuki Kabashima
The approximation of a high-dimensional vector by a small combination of column vectors selected from a fixed matrix has been actively debated in several different disciplines. In…