13 citations · 32 across the 11 of their papers we have counts for
21 papers
EASE: Entity-Aware Contrastive Learning of Sentence Embedding
Sosuke Nishikawa, Ryokan Ri, Ikuya Yamada +2
We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities. The advantage of using entity supervision is…
Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models
Ryokan Ri, Yoshimasa Tsuruoka
We investigate what kind of structural knowledge learned in neural network encoders is transferable to processing natural language. We design artificial languages with structural p…
Utilizing Skipped Frames in Action Repeats via Pseudo-Actions
Taisei Hashimoto, Yoshimasa Tsuruoka
In many deep reinforcement learning settings, when an agent takes an action, it repeats the same action a predefined number of times without observing the states until the next act…
Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces
Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
Meta-reinforcement learning (RL) addresses the problem of sample inefficiency in deep RL by using experience obtained in past tasks for a new task to be solved. However, most meta-…
Optimistic Proximal Policy Optimization
Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
Reinforcement Learning, a machine learning framework for training an autonomous agent based on rewards, has shown outstanding results in various domains. However, it is known that…
Building a Computer Mahjong Player via Deep Convolutional Neural Networks
Shiqi Gao, Fuminori Okuya, Yoshihiro Kawahara +1
The evaluation function for imperfect information games is always hard to define but owns a significant impact on the playing strength of a program. Deep learning has made great ac…