Search Results for author: Yuu Jinnai

Found 14 papers, 4 papers with code

On Hash-Based Work Distribution Methods for Parallel Best-First Search

no code implementations10 Jun 2017 Yuu Jinnai, Alex Fukunaga

We show that Abstract Zobrist hashing outperforms previous methods on search domains using hand-coded, domain specific feature projection functions.

graph partitioning

A Survey of Parallel A*

1 code implementation16 Aug 2017 Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto

A* is a best-first search algorithm for finding optimal-cost paths in graphs.

Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search

2 code implementations18 May 2018 Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.

Image Captioning Neural Architecture Search +4

Policy and Value Transfer in Lifelong Reinforcement Learning

no code implementations ICML 2018 David Abel, Yuu Jinnai, Sophie Yue Guo, George Konidaris, Michael Littman

We consider the problem of how best to use prior experience to bootstrap lifelong learning, where an agent faces a series of task instances drawn from some task distribution.

reinforcement-learning Reinforcement Learning (RL)

Finding Options that Minimize Planning Time

no code implementations16 Oct 2018 Yuu Jinnai, David Abel, D. Ellis Hershkowitz, Michael Littman, George Konidaris

We formalize the problem of selecting the optimal set of options for planning as that of computing the smallest set of options so that planning converges in less than a given maximum of value-iteration passes.

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search

1 code implementation26 Mar 2019 Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

Neural Architecture Search (NAS) has shown great success in automating the design of neural networks, but the prohibitive amount of computations behind current NAS methods requires further investigations in improving the sample efficiency and the network evaluation cost to get better results in a shorter time.

Image Captioning Neural Architecture Search +4

Lipschitz Lifelong Reinforcement Learning

1 code implementation15 Jan 2020 Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman

We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks.

reinforcement-learning Reinforcement Learning (RL) +1

Blind Signal Separation for Fast Ultrasound Computed Tomography

no code implementations27 Apr 2023 Takumi Noda, Yuu Jinnai, Naoki Tomii, Takashi Azuma

The empirical result shows that FastUSCT significantly improves the quality of the image under the same imaging time to the conventional USCT method, especially when the imaging time is limited.

On the Depth between Beam Search and Exhaustive Search for Text Generation

no code implementations25 Aug 2023 Yuu Jinnai, Tetsuro Morimura, Ukyo Honda

To this end, we introduce Lookahead Beam Search (LBS), a multi-step lookahead search that optimizes the objective considering a fixed number of future steps.

Machine Translation Text Generation +2

Model-Based Minimum Bayes Risk Decoding

no code implementations9 Nov 2023 Yuu Jinnai, Tetsuro Morimura, Ukyo Honda, Kaito Ariu, Kenshi Abe

MBR decoding selects a hypothesis from a pool of hypotheses that has the least expected risk under a probability model according to a given utility function.

Text Generation

Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding

no code implementations5 Jan 2024 Yuu Jinnai, Kaito Ariu

Minimum Bayes-Risk (MBR) decoding is shown to be a powerful alternative to beam search decoding for a wide range of text generation tasks.

Image Captioning Machine Translation +3

Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding

no code implementations10 Jan 2024 Yuu Jinnai, Ukyo Honda, Tetsuro Morimura, Peinan Zhang

We propose two variants of MBR, Diverse MBR (DMBR) and $k$-medoids MBR (KMBR), methods to generate a set of sentences with high quality and diversity.

Language Modelling Large Language Model +1

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