We propose an unsupervised “local learning” algorithm for learning a metric in the input space. Geometrically, for a given query point, the algorithm finds the minimum volume ...
In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. Duri...
We present a biology-inspired probabilistic graphical model, called the hypernetwork model, and its application to medical diagnosis of disease. The hypernetwork models are a way ...
JungWoo Ha, Jae-Hong Eom, Sung-Chun Kim, Byoung-Ta...
We present a general Bayesian framework for hyperparameter tuning in L2-regularized supervised learning models. Paradoxically, our algorithm works by first analytically integratin...
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and ...
Reinaldo A. C. Bianchi, Raquel Ros, Ramon Ló...