no code implementations • 10 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.
1 code implementation • 16 Aug 2017 • Alex Fukunaga, Adi Botea, Yuu Jinnai, Akihiro Kishimoto
A* is a best-first search algorithm for finding optimal-cost paths in graphs.
2 code implementations • 18 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.
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.
no code implementations • 16 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.
no code implementations • 2 Mar 2019 • Yuu Jinnai, Jee Won Park, David Abel, George Konidaris
One of the main challenges in reinforcement learning is solving tasks with sparse reward.
1 code implementation • 26 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.
Ranked #7 on Neural Architecture Search on CIFAR-10 Image Classification (Params metric)
1 code implementation • 15 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.
no code implementations • ICLR 2020 • Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Konidaris
While many option discovery methods have been proposed to accelerate exploration in reinforcement learning, they are often heuristic.
no code implementations • 27 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.
no code implementations • 25 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.
no code implementations • 9 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.
no code implementations • 5 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.
no code implementations • 10 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.