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ORCIDShingo Yashima
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2020 – today
- 2024
[c6]Yusuke Sekikawa, Shingo Yashima:
SAS: Structured Activation Sparsification. ICLR 2024- 2023
- [c5]Tomoya Takahashi, Shingo Yashima, Kohta Ishikawa, Ikuro Sato, Rio Yokota:
Pixel-level Contrastive Learning of Driving Videos with Optical Flow. CVPR Workshops 2023: 3180-3187 - [c4]Yusuke Sekikawa, Shingo Yashima:
Bit-Pruning: A Sparse Multiplication-Less Dot-Product. ICLR 2023 - 2022
- [c3]Hiroaki Igarashi, Kenichi Yoneji, Kohta Ishikawa, Rei Kawakami, Teppei Suzuki, Shingo Yashima, Ikuro Sato:
Multi-task Curriculum Learning based on Gradient Similarity. BMVC 2022: 705 - [c2]Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, Rei Kawakami:
Feature Space Particle Inference for Neural Network Ensembles. ICML 2022: 25452-25468 - [i3]Shingo Yashima, Teppei Suzuki, Kohta Ishikawa, Ikuro Sato, Rei Kawakami:
Feature Space Particle Inference for Neural Network Ensembles. CoRR abs/2206.00944 (2022) - [i2]Shingo Yashima:
Effectiveness of Function Matching in Driving Scene Recognition. CoRR abs/2208.09694 (2022) - 2021
- [c1]Shingo Yashima, Atsushi Nitanda, Taiji Suzuki:
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. AISTATS 2021: 1954-1962
2010 – 2019
- 2019
- [i1]Shingo Yashima, Atsushi Nitanda, Taiji Suzuki:
Exponential Convergence Rates of Classification Errors on Learning with SGD and Random Features. CoRR abs/1911.05350 (2019)
Coauthor Index
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