114 citations · 355 across the 20 of their papers we have counts for
5 papers · 1 filter
Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GAN
Masaki Saito, Shunta Saito, Masanori Koyama +1
Training of Generative Adversarial Network (GAN) on a video dataset is a challenge because of the sheer size of the dataset and the complexity of each observation. In general, the…
DQN-TAMER: Human-in-the-Loop Reinforcement Learning with Intractable Feedback
Riku Arakawa, Sosuke Kobayashi, Yuya Unno +2
Exploration has been one of the greatest challenges in reinforcement learning (RL), which is a large obstacle in the application of RL to robotics. Even with state-of-the-art RL al…
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
Sho Yokoi, Sosuke Kobayashi, Kenji Fukumizu +2
In this paper, we propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions (e.g., sentences) with a very short learning time, as an alt…
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
Sosuke Kobayashi
We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are…
Unsupervised Learning of Style-sensitive Word Vectors
Reina Akama, Kento Watanabe, Sho Yokoi +2
This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) model (…