8 citations · 13 across the 4 of their papers we have counts for
4 papers
Approximated Infomax Early Stopping: Revisiting Gaussian RBMs on Natural Images
Taichi Kiwaki, Takaki Makino, Kazuyuki Aihara
We pursue an early stopping technique that helps Gaussian Restricted Boltzmann Machines (GRBMs) to gain good natural image representations in terms of overcompleteness and data fit…
Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences
Sainbayar Sukhbaatar, Takaki Makino, Kazuyuki Aihara
Learning invariant representations from images is one of the hardest challenges facing computer vision. Spatial pooling is widely used to create invariance to spatial shifting, but…
Apprenticeship Learning for Model Parameters of Partially Observable Environments
Takaki Makino, Johane Takeuchi
We consider apprenticeship learning, i.e., having an agent learn a task by observing an expert demonstrating the task in a partially observable environment when the model of the en…
Restricted Collapsed Draw: Accurate Sampling for Hierarchical Chinese Restaurant Process Hidden Markov Models
Takaki Makino, Shunsuke Takei, Issei Sato +1
We propose a restricted collapsed draw (RCD) sampler, a general Markov chain Monte Carlo sampler of simultaneous draws from a hierarchical Chinese restaurant process (HCRP) with re…