Papers/Publications

International Conferences

• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “A Stochastic Momentum Accelerated Quasi-Newton Method for Neural Networks (Student Abstract)”, Proceedings of the 36th AAAI Conference on Artificial Intelligence, Feb 2022
• S. Indrapriyadarsini, H. Ninomiya, T. Kamio, H. Asai, “On the Practical Robustness of the Nesterov’s Accelerated Quasi-Newton Method”, Proceedings of the 36th AAAI Conference on Artificial Intelligence, Feb 2022
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “A Nesterov’s Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning”, International Symposium on Nonlinear Theory and its Applications, NOLTA, IEICE, Nov 2020 (Student Paper Award)
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “A Neural Network Approach to Analog Circuit Design Optimization Using Nesterov's Accelerated Quasi-Newton Method”, IEEE International Symposium on Circuits and Systems (ISCAS), 2020
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, H. Asai, “An Adaptive Stochastic Nesterov Accelerated Quasi Newton Method for Training RNNs”, International Symposium on Nonlinear Theory and its Applications, NOLTA, IEICE, Dec 2019 (Best Student Paper Award)
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, H. Asai, “A Stochastic Quasi-Newton Method with Nesterov’s Accelerated Gradient”, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML-PKDD, LNCS vol.11906, pp. 743-760, Springer, Cham, Sept 2019
• S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “Momentum acceleration of quasi-Newton Training for Neural Networks”, 16th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2019, (pp. 268-281). Springer, Cham.
• S. Yasuda, S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “A Stochastic Variance Reduced Nesterov's Accelerated Quasi-Newton Method”, 18th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, 2019
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, H. Asai, “Implementation of a modified Nesterov’s Accelerated quasi-Newton method on Tensorflow”, 17th IEEE International Conference on Machine Learning and Applications (ICMLA) IEEE, 2018.
• R.V Sastry, S. Karthik, Adithya R, R. Aravind, S. Indrapriyadarsini, Gagandeep Panwar, A. G. Ramakrishnan, "Calibration-free SSVEP-based BCI Switch", 16th IEEE India Council International Conference (INDICON), Dec 2019
• R.V Sastry, R. Aravind, Gagandeep Panwar, S. Indrapriyadarsini, A. G. Ramakrishnan, "Automatic User Customized Brain Switch", 13th IEEE India Council International Conference (INDICON) , Dec 2016

Journals

• S. Indrapriyadarsini, H. Ninomiya, M. Nishimura, “On the Convergence of Stochastic Accelerated quasi-Newton Methods for Neural Networks”, (Under Review) 2022
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “Accelerating Symmetric Rank 1 Quasi-Newton Method with Nesterov’s Gradient”, Algorithms 2022, 15(1), 6;
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “A Nesterov’s Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning”, NOLTA Journal, Vol. 12(3), pp. 323-335, IEICE, Jul 2021 (Invited Paper)
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, H. Asai, “aSNAQ: An Adaptive Stochastic Nesterov Accelerated Quasi Newton Method for Training RNNs”, NOLTA Journal, IEICE, Vol.E11-N, No.4, pp. 409-421, Oct. 2020 (Invited Paper)
• S. Yasuda, S. Indrapriyadarsini, H. Ninomiya, T. Kamio, H. Asai, “addHessian: Combining quasi-Newton method with first-order method for neural network training”, NOLTA Journal, Vol 13(2), pp. 361-366, IEICE, April 2022
• S. Mahboubi, R. Yamatomi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “On the study of Memory-Less quasi-Newton Method with Momentum Term for Neural Network Training”, NOLTA Journal, Vol 13(2), pp. 271-276, IEICE, April 2022
• S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “Momentum Acceleration of quasi-Newton based Optimization Technique for Neural Network Training”, NOLTA Journal, Vol. 12(3), pp. 554-574, IEICE, Jul2021
• S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “A Robust quasi-Newton Training with Adaptive Momentum for Microwave Circuit Models in Neural Networks”, Journal of Signal Processing 24.1 (2020): 11-17.

International Workshops and Symposiums

• S. Indrapriyadarsini, H. Ninomiya, M. Nishimura, "Stochastic Accelerated Quasi-Newton for Training Neural Networks," Eastern European Machine Learning Summer School, EEML Jul 2022
• S. Indrapriyadarsini, H. Ninomiya, M. Nishimura, “Accelerated Stochastic Quasi-Newton Methods for Training Neural Networks,” 8th International Symposium towards Future Advanced Research (ISFAR), Shizuoka University, Mar 2022 (Best Presentation Award)
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “Accelerating Symmetric Rank 1 Quasi-Newton Method with Nesterov’s Gradient”, Affinity Workshop WiML NeurIPS, Dec 2021
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “A modified limited memory Nesterov’s accelerated quasi-Newton”, WiML Workshop, ICML Jul 2021
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “VLSI Physical Design Automation using Deep Reinforcement Learning”, WiML Workshop @ NeurIPS, Dec 2020.
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “Applications of Deep Learning in Electronic Design Automation”, Eastern European Machine Learning Summer School, EEML Jul 2020.
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, H. Asai, “A Modified Nesterov’s Accelerated quasi-Newton Method on Tensorflow,” 5th International Symposium towards Future Advanced Research (ISFAR), Shizuoka University, Mar 2019

Domestic Conferences and Technical Meets (Peer reviewed) ・国内研究会発表（査読有）

• S. Yasuda, S. Indrapriyadarsini, H. Ninomiya, T. Kamio, H. Asai, “addHessian: Combining quasi-Newton method with first-order method for neural network training”, Non-Linear Science Workshop, NOLTA IEICE, Dec 2021.
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “A modified limited memory Nesterov’s accelerated quasi-Newton”, NOLTAソサイエティ大会, IEICE, Jun 2021.
• S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, T. Kamio, H. Asai, “Neural Networkを用いたAnalog回路設計”, 総合大会, IEICE, March 2020
• S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “慣性付メモリーレス準ニュートン学習法に関する研究”, NOLTAソサイエティ大会, IEICE, Jun 2021.
• S. Yasuda, S. Indrapriyadarsini, H. Ninomiya, T. Kamio, H. Asai, “ニューラルネットワーク学習のための適応的な分散低減二次手法”, NOLTAソサイエティ大会, IEICE, Jun 2021.
• K. Fujise, S. Indrapriyadarsini, T. Kamio, H. Ninomiya, H. Asai, “マルチエージェント深層強化学習による自動配線”, NOLTAソサイエティ大会, IEICE, Jun 2021.
• Bhargava R V, S. Indrapriyadarsini, T. Kamio, H. Ninomiya, H. Asai, “A Reinforcement Learning based Image Preprocessing for Object Detection”, NOLTAソサイエティ大会, IEICE, Jun 2021.
• S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “適応的慣性項を用いた準ニュートン学習法に関する研究”, 総合大会, 広島, IEICE, Mar 2020
• S. Mahboubi, R. Yamatomi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “On the study of Memory-Less quasi-Newton Method with Momentum Term for Neural Network Training”</a>, Non-Linear Science Workshop, NOLTA IEICE, Dec 2021.
• S. Yasuda, S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “確率的分散低減を用いたネステロフの加速準ニュートン法”, 総合大会, 広島, IEICE, Mar 2020
• S. Kitagawa, S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, H. Asai, “Knowledge Based Neural Network の概念を用いた学習モデルの効率について”, 電気学会電子回路研究会, ECT-019-086, IEE Japan, Dec 2019
• K. Fujise, S. Indrapriyadarsini, S. Mahboubi, H. Ninomiya, H. Asai, “様々な最適化法におけるCascade-correlationの性能調査”, 電気学会電子回路研究会, ECT-019-093, IEE Japan, Dec 2019
• S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “準ニュートン法における慣性項の影響に関する研究”, 信学技報, vol. 118, no. 498, NLP2018-137, pp. 69-74, Mar 2019
• Abhishek D, Bhargava R V, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “Neural Network Assistance for Determining Circuit Design Parameters”, 電気学会電子回路研究会, ECT-019-088, IEE Japan, Dec 2019
• Y. Sato, S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “ネステロフの加速勾配を用いたBarzilai-Borwein法の高速化”, 総合大会, IEICE, Jan 2019
• S. Mahboubi, S. Indrapriyadarsini, H. Ninomiya, H. Asai, “On the Influence of Momentum term in quasi-Newton method”, 信学技報, vol. 118, no. 498, NLP2018-137, pp. 69-74, Mar 2018

Talks

• ICLR 2022 Socials : “Optimization in ML and DL - A discussion on theory and practice”
• WiML Workshop Socials @NeurIPS 2021 : “Optimization Techniques”