1 citations · 2 across the 3 of their papers we have counts for
3 papers
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
Shrinivas Ramasubramanian, Harsh Rangwani, Sho Takemori +3
The rise in internet usage has led to the generation of massive amounts of data, resulting in the adoption of various supervised and semi-supervised machine learning algorithms, wh…
Towards Accurate Quantum Chemical Calculations on Noisy Quantum Computers
Naoki Iijima, Satoshi Imamura, Mikio Morita +4
Variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for noisy intermediate-scale quantum (NISQ) computers. It is promising for quantum chemical c…
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Harsh Rangwani, Shrinivas Ramasubramanian, Sho Takemori +3
Self-training based semi-supervised learning algorithms have enabled the learning of highly accurate deep neural networks, using only a fraction of labeled data. However, the major…