Preferred Networks

@PreferredNet

Official account for Preferred Networks, Inc. (PFN). Japanese Account:

Tokyo, Japan and Berkeley, CA
Vrijeme pridruživanja: prosinac 2014.

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  1. 30. sij

    [lecture] A special lecture on "AI Strategy" by Harvard Business School and INSEAD will be held in Roppongi on Jan.31, 2020. Prof. Kireyev of INSEAD will talk about the case study “Preferred Networks: A Deep Learning Startup Powers the Internet of Things.”

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  2. 30. sij

    [internship] Preferred Network's 2020 Summer-autumn Global Internship Program 2nd Batch is now on going. Application period of 2nd Batch: 0:00, Jan 20 2020 to 23:59, Feb 16 2020 (JST: UTC+9) For details, please refer to the following call for application:

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    29. sij

    [blog] In the development of Optuna, one of the difficult challenges is to evaluate the performance of new sampling and pruning algorithms correctly. This blog post introduces a benchmark tool named Kurobako, which is used to address the challenge.

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    23. sij

    Neptune integrates with Optuna from ! It is simple. 1. Add a callback: study.optimize(objective, n_trials=100, callbacks=[opt_utils.NeptuneMonitor()]) 2. Monitor your search results. 3. Done.

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  5. proslijedio/la je Tweet
    16. sij

    Released / v7.1.0! This release focuses on improving the stability of both CuPy and Chainer.

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    16. sij

    Released / v6.7.0! Mainly bug fixes. This is the final release of v6 series, which is the last version supporting Python 2.

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  7. 14. sij

    [News] Preferred Networks releases Optuna v1.0, the first major version of the open-source hyperparameter optimization framework for machine learning. Optimize your optimization.

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    28. pro 2019.

    We are demonstrating our recent progress on automated animation of anime characters at , together with Whomor Inc. and M2 Co., Ltd., until Dec 31. Stop by and interact with characters generated by at B hall No. 2212, Tokyo Big Sight Aomi Exhibition Hall!

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    28. pro 2019.

    Two animated characters generated by . Check out the other 30 characters at our booth at Comic Market 97!

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    19. pro 2019.

    [blog] Deep Reinforcement is challenging to apply to real world tasks where data collection comes at a cost. In this blog post, we look towards state representation learning to accelerate learning and list potential data sources to learn them.

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  11. 16. pro 2019.

    []Preferred Network's 2020 Summer-autumn global internship program is open for application now! Application period: 6:00pm, Dec 16 2019 to 12:00pm (noon), March 30 2020(JST: UTC+9) For details, please refer to the following call for application:

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    Team CDS wins the MineRL Challenge 2019! Congrats to Team mc_rl & Team I4DS for their ranking AI agents. Thank you to all the participants & sponsors who brought the MineRL Challenge to life. Dive into the dataset:

    A team photo of the MineRL Challenge 2019 at Neurips 2019 in Vancouver, BC.
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  13. proslijedio/la je Tweet
    14. pro 2019.

    [] Alexander I. Cowen-Rivers will present his internship work at PFN, "Emergent Communication with World Models" today at NeurIPS2019 Workshop on Emergent Communication Paper:

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  14. proslijedio/la je Tweet
    14. pro 2019.

    "Swarm-inspired Reinforcement Learning via Collaborative Inter-agent Knowledge Distillation" is work led by PFN intern Zhang-Wei Hong. The paper proposes CIKD, a method to improve ensemble reinforcement learning by periodically distilling knowledge between agents.

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  15. proslijedio/la je Tweet
    14. pro 2019.

    "Learning Latent State Spaces for Planning through Reward Prediction" is work led by PFN intern . The paper explores the merits of performing model-based RL by learning latent state spaces solely from reward signals.

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  16. proslijedio/la je Tweet
    14. pro 2019.

    "ChainerRL: A Deep Reinforcement Learning Library" introduces ChainerRL, PFN's open source Deep RL library. ChainerRL has faithful reproductions of 9 key Deep RL algorithms, and has easily composable abstractions for performing RL research.

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  17. proslijedio/la je Tweet
    12. pro 2019.

    [] This evening poster (ID:216) on research done by our tech advisor Prof. Kenji Fukumizu at Preferred Networks. “Semi-flat minima and saddle points by embedding neural networks to overparameterization” Paper: Slides:

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  18. proslijedio/la je Tweet
    12. pro 2019.

    [] Poster presentation (ID:139) this morning by K. Hayashi, T. Yamaguchi (intern), Y. Sugawara, and S. Maeda. “Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks” Paper: Slides:

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  19. proslijedio/la je Tweet
    12. pro 2019.

    [] Poster presentation (ID:153) this morning by M. Kusumoto et al on memory-efficient backpropagation. “A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation” Paper: Poster:

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  20. 12. pro 2019.

    Optuna chat rooms are now available on Gitter for communication with other developers and users. Join if you have any questions or simply want to talk about the framework, hyperparameter optimization or AutoML in general.

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