2020 - Meeting Schedule
Date (MM/DD)	Time	Location	Presenter/Title
11/10	14:20-16:00	online (see announcement mail)	Yusuke Sekikawa [@Denso IT] Event-based Vision: Emerging Sensors and Processors Ikuro Sato [@Denso IT, Tokyo Tech] Integer-Decomposition Method for Network Compression
10/13	14:20-16:00	online (see announcement mail)	Koki Madono [@AIST, Waseda Univ] Block-wise Scrambled Image Recognition Using Adaptation Network Toshiki Masuyama [@AIST, Waseda Univ] Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling
08/11	15:25-17:05	online (see announcement mail)	Atsushi Nitanda [@U-Tokyo] Optimization of neural networks in function spaces Kenta Oono [@Susuki Lab/PFN] Theoretical analysis of graph neural networks---expressive power, optimization and generalization
07/14	15:25-17:05	online (see announcement mail)	Hanna Hoshino [@Yokota Lab] Towards reproducible and reliable experiments: Weights and Biases Shun Iwase [@Yokota Lab] RePOSE: Render-and-Compare Iterative Pose Refinement Network for 6DoF Pose Estimation
06/09	15:25-17:05	online (see announcement mail)	Mohamed Wahib [@AIST/RWBC-OIL/RIKEN] Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA Yosuke Oyama [@Matsuoka Lab] The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
05/12	15:25-17:05	online (see announcement mail)	Ruidong Jin [M2@Murata Lab] An Application of Bipartite Graph Convolutional Networks on Metropolitan Emergency Medical Service Demand Analysis Ryuta Matsuno [M2@Murata Lab] Optimizing convolutional kernels of graphs for better performance
04/14	15:25-17:05	online (see announcement mail)	Asako Kanezaki [@TokyoTech] RotationNet for Joint Object Categorization and Unsupervised Pose Estimation from Multi-view Images Rei Kawakami [@TokyoTech/Denso IT Laboratory] Improving Robustness in Recognition: Motion, open-set, and multi-task learning
03/10	15:05-16:35	online (see announcement mail)	Ikuro Sato [@Denso IT Lab] Classifier Anonymization Technique for Co-Adaptation Breaking Hiroki Naganuma [@Yokota Lab] Trends in Deep Learning Theory at NeurIPS 2019
02/12	15:05-16:35	GSIC２階会議室	Shuhei Watanabe [@AIST] Speeding up of the Nelder-Mead Method by Data-driven Speculative Execution Yoshihiko Ozaki [@AIST] Practical Deep Neural Network Performance Prediction for Hyperparameter Optimization Yuki Tanigaki [@AIST] Paper presentation of "NAS-Bench-101: Towards Reproducible Neural Architecture Search"
01/07	15:05-16:35	W8E 10F large meeting room	Masato Motomura [@Motomura Lab] AI Computing Architectures: Recent Works and Future Efforts