1 - 6 out of 6Page size: 10
  1. 2021

  2. Quantization noise in low bit quantization and iterative adaptation to quantization noise in quantizable neural networks

    Chudakov, D., Goncharenko, A., Alyamkin, S. & Densidov, A., 20 Dec 2021, In: Journal of Physics: Conference Series. 2134, 1, 012004.

    Research output: Contribution to journal › Conference article › peer-review

  3. Iterative Adaptation to Quantization Noise

    Chudakov, D., Alyamkin, S., Goncharenko, A. & Denisov, A., 2021, Advances in Computational Intelligence - 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Proceedings. Rojas, I., Joya, G. & Catala, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 303-310 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12861 LNCS).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  4. 2020

  5. Applicability of Minifloats for Efficient Calculations in Neural Networks

    Kondrat’ev, A. Y. & Goncharenko, A. I., 1 Jan 2020, In: Optoelectronics, Instrumentation and Data Processing. 56, 1, p. 76-80 5 p.

    Research output: Contribution to journal › Article › peer-review

  6. 2019

  7. Winning solution on LPIRC-LL competition

    Goncharenko, A., Alyamkin, S., Denisov, A. & Terentev, E., Jun 2019, Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019. IEEE Computer Society, p. 10-16 7 p. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; vol. 2019-June).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  8. On Practical Approach to Uniform Quantization of Non-redundant Neural Networks

    Goncharenko, A., Denisov, A., Alyamkin, S. & Terentev, E., 1 Jan 2019, Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Proceedings. Tetko, I. V., Karpov, P., Theis, F. & Kurková, V. (eds.). Springer-Verlag GmbH and Co. KG, p. 349-360 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11728 LNCS).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

  9. Trainable Thresholds for Neural Network Quantization

    Goncharenko, A., Denisov, A., Alyamkin, S. & Terentev, E., 1 Jan 2019, Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings. Rojas, I., Joya, G. & Catala, A. (eds.). Springer-Verlag GmbH and Co. KG, p. 302-312 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11507 LNCS).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

ID: 3437364